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      "entry_id": "concept/emergence",
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      "value": "\"We consider an ability to be emergent if it is not present in smaller models but is present in larger models.\"",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://arxiv.org/abs/2206.07682v2",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/emergence",
      "field": "metric_concentration",
      "value": "2 metrics account for more than 92% of claimed emergent abilities on BIG-Bench: Multiple Choice Grade and Exact String Match",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2304.15004",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/emergence",
      "field": "metrics_showing_emergence",
      "value": "emergent abilities appear under 4 of 39 hand-annotated BIG-Bench metrics",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2304.15004",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/emergence",
      "field": "induced_in_vision",
      "value": "emergence induced by metric choice in fully connected, convolutional and self-attentional networks — autoencoders on CIFAR100, a LeNet on MNIST, autoregressive transformers on Omniglot",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2304.15004",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/grokking",
      "field": "delay_magnitude",
      "value": "\"Training accuracy becomes close to perfect at <10^3 optimization steps, but it takes close to 10^6 steps for validation accuracy to reach that level\"",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://ar5iv.labs.arxiv.org/html/2201.02177",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/grokking",
      "field": "weight_decay_effect",
      "value": "weight decay \"has a very large effect on data efficiency, more than halving the amount of samples needed compared to most other interventions\"",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://ar5iv.labs.arxiv.org/html/2201.02177",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/grokking",
      "field": "learned_algorithm",
      "value": "the modular-addition network was reverse-engineered as discrete Fourier transforms and trigonometric identities converting addition to rotation about a circle",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://arxiv.org/abs/2301.05217",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/grokking",
      "field": "three_phases",
      "value": "memorization, circuit formation, cleanup — \"grokking, rather than being a sudden shift, arises from the gradual amplification of structured mechanisms encoded in the weights\"",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://arxiv.org/abs/2301.05217v3",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/hallucination",
      "field": "singleton_lower_bound",
      "value": "\"hallucination rate, after pretraining, should be at least the fraction of training facts that appear once\"",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://arxiv.org/abs/2509.04664",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/hallucination",
      "field": "origin_as_binary_error",
      "value": "\"Hallucinations need not be mysterious — they originate simply as errors in binary classification.\"",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://arxiv.org/abs/2509.04664v1",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/hallucination",
      "field": "benchmark_abstention_credit",
      "value": "GPQA, MMLU-Pro and SWE-bench are all scored binary, with no credit for an \"I don't know\" response",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2509.04664",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/hallucination",
      "field": "letter_counting_trials",
      "value": "DeepSeek-V3 returned \"2\" or \"3\" in ten independent trials when asked how many Ds are in DEEPSEEK",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2509.04664",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/in-context-learning",
      "field": "random_label_finding",
      "value": "randomly replacing the labels in demonstrations barely hurt performance, consistently over 12 models including GPT-3",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2202.12837",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/in-context-learning",
      "field": "what_demonstrations_supply",
      "value": "the label space, the distribution of the input text, and the overall format of the sequence",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://arxiv.org/abs/2202.12837",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/in-context-learning",
      "field": "flipped_label_scale_dependence",
      "value": "\"overriding semantic priors is an emergent ability of model scale\" — small models ignore flipped in-context labels, large models can follow them",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2303.03846v2",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/in-context-learning",
      "field": "induction_head_definition",
      "value": "a head showing prefix matching (attending back to tokens previously followed by the current token) and copying (raising the logit of the attended-to token)",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/kv-cache",
      "field": "memory_waste_before_paging",
      "value": "60-80% of KV cache memory lost to fragmentation and over-reservation; under 4% with paged allocation",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://vllm.ai/blog/2023-06-20-vllm",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/kv-cache",
      "field": "paged_attention_throughput_gain",
      "value": "2-4x throughput over FasterTransformer and Orca at comparable latency",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2309.06180",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/kv-cache",
      "field": "gqa_uptraining_cost",
      "value": "5% of original pre-training compute to convert a multi-head checkpoint to grouped-query attention",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2305.13245",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/kv-cache",
      "field": "anthropic_cache_default_lifetime",
      "value": "5 minutes",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://platform.claude.com/docs/en/build-with-claude/prompt-caching",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/kv-cache",
      "field": "anthropic_cache_lookback_window",
      "value": "20 blocks",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://platform.claude.com/docs/en/build-with-claude/prompt-caching",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/kv-cache",
      "field": "anthropic_cache_breakpoints_max",
      "value": "4 per request",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://platform.claude.com/docs/en/build-with-claude/prompt-caching",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/kv-cache",
      "field": "openai_cache_retention",
      "value": "at least 30 minutes after the latest write or reuse",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://developers.openai.com/api/docs/guides/prompt-caching",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/model-collapse",
      "field": "definition",
      "value": "\"use of model-generated content in training causes irreversible defects in the resulting models, where tails of the original content distribution disappear\"",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://arxiv.org/abs/2305.17493v3",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/model-collapse",
      "field": "journal_publication",
      "value": "Nature, volume 631, issue 8022, pages 755-759, published 24 July 2024",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://api.crossref.org/works/10.1038/s41586-024-07566-y",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/model-collapse",
      "field": "experiment_setup",
      "value": "OPT-125m finetuned on wikitext2; each generation trained on 5-way beam-search continuations from the previous generation, in one arm with no original data retained and in another with 10% retained",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://arxiv.org/abs/2305.17493",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/model-collapse",
      "field": "accumulation_result",
      "value": "\"accumulating the successive generations of synthetic data alongside the original real data avoids model collapse\"; test error acquires a finite upper bound independent of the number of iterations",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://arxiv.org/abs/2404.01413v2",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/model-context-protocol",
      "field": "current_revision",
      "value": "2026-07-28",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://modelcontextprotocol.io/specification/versioning",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/model-context-protocol",
      "field": "base_protocol",
      "value": "JSON-RPC, version 2.0",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://modelcontextprotocol.io/specification/2026-07-28",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/model-context-protocol",
      "field": "version_string_meaning",
      "value": "the date of the last backwards-incompatible change, not a release date",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://modelcontextprotocol.io/specification/versioning",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/model-context-protocol",
      "field": "announced",
      "value": "2024-11-25, open-sourced by Anthropic",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://www.anthropic.com/news/model-context-protocol",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/model-context-protocol",
      "field": "deprecation_window",
      "value": "at least twelve months before a deprecated feature may be removed, or ninety days under the expedited exception",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://modelcontextprotocol.io/specification/versioning",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/openai-daybreak",
      "field": "blue_definition",
      "value": "Daybreak Blue supports common defensive work with OpenAI's mainline models",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://openai.com/index/daybreak-for-frontline-defenders/",
      "accessed": "2026-09-04",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/openai-daybreak",
      "field": "red_definition",
      "value": "Daybreak Red gives approved organizations access to specialized cyber models for more sensitive and technically demanding work",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://openai.com/index/daybreak-for-frontline-defenders/",
      "accessed": "2026-09-04",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/openai-daybreak",
      "field": "blue_base",
      "value": "GPT-5.6 Sol; API stable alias gpt-daybreak-blue-latest",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://help.openai.com/en/articles/20001258-openai-daybreak-trusted-access-for-cyber-overview",
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      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/openai-daybreak",
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      "value": "GPT-5.6 Cyber; API stable alias gpt-daybreak-red-latest",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://help.openai.com/en/articles/20001258-openai-daybreak-trusted-access-for-cyber-overview",
      "accessed": "2026-09-04",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/openai-daybreak",
      "field": "governance",
      "value": "Daybreak Access is OpenAI's Trusted Access for Cyber program; it does not remove all safeguards or all refusals",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://help.openai.com/en/articles/20001258-openai-daybreak-trusted-access-for-cyber-overview",
      "accessed": "2026-09-04",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/openai-daybreak",
      "field": "approved_organizations",
      "value": "2,000 approved organizations and workspaces",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://openai.com/index/daybreak-for-frontline-defenders/",
      "accessed": "2026-09-04",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/openai-daybreak",
      "field": "frontline_defenders_commitment",
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      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://openai.com/index/daybreak-for-frontline-defenders/",
      "accessed": "2026-09-04",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/openai-daybreak",
      "field": "ms_isac_pilot",
      "value": "a pilot with the Multi-State Information Sharing and Analysis Center (MS-ISAC) pairing Daybreak access with guided training for an initial group of public sector and water system defenders",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://openai.com/index/daybreak-for-frontline-defenders/",
      "accessed": "2026-09-04",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/openai-daybreak",
      "field": "defense_network_scale",
      "value": "more than 35 products and partner-operated services across the Daybreak Defense Network",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://openai.com/index/daybreak-for-frontline-defenders/",
      "accessed": "2026-09-04",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/openai-daybreak",
      "field": "water_sector_support",
      "value": "up to US$1 million in no-cost API credits, Daybreak access and technical assistance, offered to affected states and utilities after attacks on U.S. water systems",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://openai.com/index/daybreak-for-frontline-defenders/",
      "accessed": "2026-09-04",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/reversal-curse",
      "field": "celebrity_asymmetry",
      "value": "GPT-4 answered \"Who is Tom Cruise's mother?\" correctly 79% of the time and the reverse, \"Who is Mary Lee Pfeiffer's son?\", 33% of the time",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2309.12288v4",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/reversal-curse",
      "field": "likelihood_claim",
      "value": "after finetuning on \"A is B\", the likelihood of the correct answer to the reversed question is not higher than for a random name",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://arxiv.org/abs/2309.12288v4",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/reversal-curse",
      "field": "robustness",
      "value": "\"robust across model sizes and model families and is not alleviated by data augmentation\"",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://arxiv.org/abs/2309.12288v4",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/reversal-curse",
      "field": "in_context_exception",
      "value": "\"if 'A is B' appears in-context, models can deduce the reverse relationship\"",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://arxiv.org/abs/2309.12288v4",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/scaling-laws",
      "field": "kaplan_paper",
      "value": "Kaplan et al., Scaling Laws for Neural Language Models, 23 January 2020",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://arxiv.org/abs/2001.08361",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/scaling-laws",
      "field": "hoffmann_paper",
      "value": "Hoffmann et al., Training Compute-Optimal Large Language Models, 29 March 2022",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://arxiv.org/abs/2203.15556",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/scaling-laws",
      "field": "chinchilla_rule",
      "value": "for every doubling of model size the number of training tokens should also be doubled",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://arxiv.org/abs/2203.15556",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/scaling-laws",
      "field": "chinchilla_models_trained",
      "value": "over 400 language models, 70 million to over 16 billion parameters",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://arxiv.org/abs/2203.15556",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/scaling-laws",
      "field": "replication_finding",
      "value": "the reported confidence intervals would require roughly 600,000 training runs; the authors likely had 400 to 500 data points",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://arxiv.org/abs/2404.10102",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/temperature-and-top-p",
      "field": "human_text_probability",
      "value": "the probability of natural text is \"on average, much lower than text generated by beam search\"; \"natural language rarely remains in a high probability zone for multiple consecutive time steps\"",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://ar5iv.labs.arxiv.org/html/1904.09751",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/temperature-and-top-p",
      "field": "nucleus_definition",
      "value": "the smallest set of highest-probability tokens whose cumulative probability mass reaches p, renormalized and sampled from",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://ar5iv.labs.arxiv.org/html/1904.09751",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/temperature-and-top-p",
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      "source_url": "https://openrouter.ai/api/v1/models",
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      "entry_id": "model/z-ai-glm-flash-latest",
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      "entry_id": "model/z-ai-glm-flash-latest",
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    {
      "entry_id": "model/z-ai-glm-flash-latest",
      "field": "context_window",
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      "volatility": "fast",
      "source_url": "https://openrouter.ai/api/v1/models",
      "accessed": "2026-09-14",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "model/z-ai-glm-flash-latest",
      "field": "status",
      "value": "active",
      "state": "feed",
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      "volatility": "fast",
      "source_url": "https://openrouter.ai/api/v1/models",
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    {
      "entry_id": "model/z-ai-glm-latest",
      "field": "price_input",
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      "volatility": "fast",
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    },
    {
      "entry_id": "model/z-ai-glm-latest",
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    },
    {
      "entry_id": "model/z-ai-glm-latest",
      "field": "context_window",
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      "state": "feed",
      "binding": "feed",
      "volatility": "fast",
      "source_url": "https://openrouter.ai/api/v1/models",
      "accessed": "2026-09-14",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "model/z-ai-glm-latest",
      "field": "status",
      "value": "active",
      "state": "feed",
      "binding": "feed",
      "volatility": "fast",
      "source_url": "https://openrouter.ai/api/v1/models",
      "accessed": "2026-09-14",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/aion-labs",
      "field": "operator",
      "value": "the terms of service are 'a legally binding agreement between you and Deep Forge sp. z o.o. (NIP: 1231533694), the company operating Aion Labs', and they 'shall be governed by and construed in accordance with the laws of the Republic of Poland'",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://www.aionlabs.ai/terms/",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/aion-labs",
      "field": "router_recorded_headquarters",
      "value": "OpenRouter's provider record for aion-labs gives headquarters \"IL\" and an empty datacenters list",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://openrouter.ai/api/v1/providers",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/aion-labs",
      "field": "second_product",
      "value": "alongside the model API, email agents — 'Deploy agents with a dedicated email address', each of which 'Runs on its own isolated computer with full internet access' and can 'edit and return documents in the reply'",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://www.aionlabs.ai/",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/aion-labs",
      "field": "public_model_endpoint",
      "value": "an OpenAI-compatible API at https://api.aionlabs.ai/v1 whose GET /v1/models 'endpoint is publicly accessible without authentication'",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://www.aionlabs.ai/docs/",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/aion-labs",
      "field": "vendor_model_list",
      "value": "the vendor's own model endpoint returns exactly four models — aion-labs/aion-2.0, aion-labs/aion-3.0, aion-labs/aion-3.0-mini and aion-labs/aion-rp-llama-3.1-8b — every one of them is_moderated false",
      "state": "cited",
      "binding": "cited",
      "volatility": "fast",
      "source_url": "https://api.aionlabs.ai/v1/models",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/aion-labs",
      "field": "vendor_release_dates",
      "value": "the vendor's own model table carries a Released tag per row: Aion-RP 1.0 (8B) 2024-11-30, Aion 2.0 2025-12-21, Aion 3.0 2026-05-05, Aion 3.0 Mini 2026-05-14",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://www.aionlabs.ai/docs/models/",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/aion-labs",
      "field": "open_weights",
      "value": "two Hugging Face repositories and no others: aion-labs/Aion-RP-Llama-3.1-8B, created 2024-11-09, declaring base_model meta-llama/Llama-3.1-8B, and aion-labs/Aion-RP-Llama-3.1-8B-GGUF, created 2024-11-30 — both apache-2.0, with 76 and 1,258 downloads",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://huggingface.co/api/models?author=aion-labs&limit=100&sort=createdAt&direction=-1",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/aion-labs",
      "field": "rp_model_card",
      "value": "'This is a completely uncensored model that has been trained to excel at roleplaying and creative writing. It can utilize the full 131K context.'",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://huggingface.co/aion-labs/Aion-RP-Llama-3.1-8B/raw/main/README.md",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/aion-labs",
      "field": "vendor_rp_context",
      "value": "the vendor's own docs table lists the aion-labs/aion-rp-llama-3.1-8b row at Context 32K",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://www.aionlabs.ai/docs/models/",
      "accessed": "2026-09-07",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/aion-labs",
      "field": "router_benchmark_claim",
      "value": "'Aion-RP-Llama-3.1-8B ranks the highest in the character evaluation portion of the RPBench-Auto benchmark, a roleplaying-specific variant of Arena-Hard-Auto, where LLMs evaluate each other’s responses. It is a fine-tuned base model rather than an instruct model, designed to produce more natural and varied writing.'",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://openrouter.ai/aion-labs/aion-rp-llama-3.1-8b",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/aion-labs",
      "field": "vendor_rp_description",
      "value": "'A variant of LLama 3.1 8B optimized for immersive roleplaying and storytelling.' — the whole of it; no benchmark is named",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://api.aionlabs.ai/v1/models",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/aion-labs",
      "field": "rpbench_published_leaderboard",
      "value": "17 rows, headed by Higgs-Llama-3 70B V2 at 68.25% on Character and closed by Character.AI at 12.47%; no Aion model appears",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://raw.githubusercontent.com/boson-ai/RPBench-Auto/main/results/leaderboard_for_display.csv",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/aion-labs",
      "field": "rpbench_leaderboard_last_commit",
      "value": "d1cca01f, 'rename to V1 for consistency', committed 2024-08-06 — the most recent change to the leaderboard file",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://api.github.com/repos/boson-ai/RPBench-Auto/commits?path=results/leaderboard_for_display.csv&per_page=3",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/aion-labs",
      "field": "rpbench_leaderboard_page",
      "value": "gone: it redirected to https://www.boson.ai/rpbench and returned HTTP 404 when requested on 2026-09-06",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://boson.ai/rpbench/",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/aion-labs",
      "field": "vendor_description_aion_3",
      "value": "'A multi-model system built on the GLM family of models. Aion 3.0 employs a collaborative generation process in which multiple specialized models contribute to each response. The result is stronger narrative structure, more compelling tension and conflict, and a nuanced treatment of mature and darker themes.'",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://api.aionlabs.ai/v1/models",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/aion-labs",
      "field": "router_description_aion_3",
      "value": "'Aion-3.0 is a multi-model roleplaying and storytelling system from AionLabs, built on the GLM family of models. It uses a collaborative generation process in which multiple specialized models each contribute to a response, producing stronger narrative structure and more compelling tension and conflict.'",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://openrouter.ai/aion-labs/aion-3.0",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/aion-labs",
      "field": "router_privacy_link_status",
      "value": "the privacy-policy URL OpenRouter publishes for this provider, https://www.aionlabs.ai/privacy-policy/, returned HTTP 404 when requested on 2026-09-06 — the citation here is that response",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://www.aionlabs.ai/privacy-policy/",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/aion-labs",
      "field": "retention_policy",
      "value": "the policy the site's own footer links states only that 'We retain your Personal Data only for as long as necessary to fulfill the purposes outlined in this Privacy Policy, unless a longer retention period is required by law' — no interval is named",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://www.aionlabs.ai/privacy/",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/alibaba-cloud",
      "field": "model_family",
      "value": "Qwen, launched in beta April 2023 as Tongyi Qianwen and opened to the public in September 2023",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://en.wikipedia.org/wiki/Qwen",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/alibaba-cloud",
      "field": "weights_license",
      "value": "Apache License 2.0 for open releases; a source-available Qwen License on larger models",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://en.wikipedia.org/wiki/Qwen",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/alibaba-cloud",
      "field": "license_revenue_share",
      "value": "larger Qwen releases often carry a revenue-sharing agreement for providers generating more than US$50 million annually",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://en.wikipedia.org/wiki/Qwen",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/alibaba-cloud",
      "field": "app_users",
      "value": "234 million (May 2026)",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://en.wikipedia.org/wiki/Qwen",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/alibaba-cloud",
      "field": "derivative_models",
      "value": "over 200,000 Qwen variants listed on Hugging Face",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://en.wikipedia.org/wiki/Qwen",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/amazon",
      "field": "founded",
      "value": "5 July 1994, in Bellevue, Washington, by Jeff Bezos",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://en.wikipedia.org/wiki/Amazon_(company)",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/amazon",
      "field": "nova_announced",
      "value": "3 December 2024 — Micro, Lite and Pro, \"available exclusively in Amazon Bedrock\"",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://aws.amazon.com/blogs/aws/introducing-amazon-nova-frontier-intelligence-and-industry-leading-price-performance/",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/amazon",
      "field": "nova_1_top_model_promise",
      "value": "\"Amazon Nova Premier is still in training. We're targeting availability in early 2025.\"",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://aws.amazon.com/blogs/aws/introducing-amazon-nova-frontier-intelligence-and-industry-leading-price-performance/",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/amazon",
      "field": "nova_premier_self_assessment",
      "value": "\"comparable to the best non-reasoning models in the industry and is equal or better on approximately half of these benchmarks when compared to other models in the same intelligence tier\", 30 April 2025",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://aws.amazon.com/blogs/aws/amazon-nova-premier-our-most-capable-model-for-complex-tasks-and-teacher-for-model-distillation/",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/amazon",
      "field": "nova_2_announced",
      "value": "2 December 2025 — Nova 2 Lite, with Nova 2 Sonic and Nova 2 Omni (preview) announced alongside it",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://aws.amazon.com/blogs/aws/introducing-amazon-nova-2-lite-a-fast-cost-effective-reasoning-model/",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/amazon",
      "field": "nova_2_thinking_control",
      "value": "extended thinking off by default, with three budget levels — low, medium or high",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://aws.amazon.com/blogs/aws/introducing-amazon-nova-2-lite-a-fast-cost-effective-reasoning-model/",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/amazon",
      "field": "current_model_menu",
      "value": "Nova 2 Lite, Nova 2 Sonic and Nova Multimodal Embeddings — the three tabs under \"Meet Nova 2 models\"",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://aws.amazon.com/nova/models/",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/amazon",
      "field": "portfolio",
      "value": "\"Nova models\", \"Nova Forge\" and \"Nova Act\" — the vendor's own three-part description of Amazon Nova",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://aws.amazon.com/nova/",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/amazon",
      "field": "internal_origin",
      "value": "\"built on AI technologies originally developed for Amazon's internal applications, such as Alexa+, Amazon Ads, Amazon Catalog System Services, AWS Marketplace, and Amazon Stores\"",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://aws.amazon.com/nova/",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/amazon",
      "field": "frontier_route",
      "value": "\"Nova Forge is the easiest and most cost-effective way to build your own frontier model\"",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://aws.amazon.com/blogs/aws/introducing-amazon-nova-forge-build-your-own-frontier-models-using-nova",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/amazon",
      "field": "nova_forge_region",
      "value": "US East (N. Virginia), the only AWS Region named at launch",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://aws.amazon.com/blogs/aws/introducing-amazon-nova-forge-build-your-own-frontier-models-using-nova",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/amazon",
      "field": "customer_count",
      "value": "\"Over 50,000 customers use Amazon Nova models\", the vendor's own count",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://aws.amazon.com/nova/models/",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/amazon",
      "field": "bedrock_hosting_breadth",
      "value": "32 model slugs by nine authors on OpenRouter's Amazon Bedrock provider page, five of them Amazon's own",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://openrouter.ai/provider/amazon-bedrock",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/anthropic",
      "field": "founded",
      "value": "January 2021",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://en.wikipedia.org/wiki/Anthropic",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/anthropic",
      "field": "headquarters",
      "value": "San Francisco, California",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://en.wikipedia.org/wiki/Anthropic",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/anthropic",
      "field": "corporate_form",
      "value": "public benefit corporation",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://en.wikipedia.org/wiki/Anthropic",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/anthropic",
      "field": "valuation",
      "value": "US$965 billion (May 2026)",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://en.wikipedia.org/wiki/Anthropic",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/anthropic",
      "field": "model_tiers",
      "value": "Haiku, Sonnet, Opus, Fable, Mythos",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://www.anthropic.com/news/claude-fable-5-mythos-5",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/anthropic",
      "field": "top_tier_access",
      "value": "Mythos 5 is available to a small set of initial testing partners for cybersecurity, and soon, biology research",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://www.anthropic.com/claude/mythos",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/bytedance-seed",
      "field": "founded",
      "value": "2023",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://github.com/ByteDance-Seed",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/bytedance-seed",
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      "value": "inclusionAI/LLaDA2.0-mini at 229,915 downloads against 70 likes, ahead of LLaDA2.1-mini at 111,105 — both diffusion language models; the most-liked repository is Ling-1T at 545 likes and 2,679 downloads",
      "state": "cited",
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      "entry_id": "org/inclusionai",
      "field": "tracker_licence_record",
      "value": "\"Open weights · Apache 2.0 (announced; weights not yet posted as of 2026-07-24)\" against a Weights row reading \"Not released\", still served on 2026-09-06",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://llm-releases.com/models/ling-3-0-flash",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/meta-superintelligence-labs",
      "field": "flagship_weights",
      "value": "not released; Meta's 1.3 announcement (2026-09-02) names 'the Muse Spark open weights release' on its roadmap",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://research.meta.ai/blog/introducing-muse-spark-1-3",
      "accessed": "2026-09-03",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/meta-superintelligence-labs",
      "field": "flagship_weights_listing",
      "value": "listed 'Open weights · Meta license (weights pending)' with weights 'Not released'",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://llm-releases.com/models/muse-spark-1-2",
      "accessed": "2026-09-03",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/meta-superintelligence-labs",
      "field": "open_weight_release",
      "value": "Muse Glimmer 30B, Apache License 2.0",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://huggingface.co/meta-models/Muse-Glimmer-30B",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/meta-superintelligence-labs",
      "field": "previous_model_line",
      "value": "Llama; last frontier release 2025-04-05 under the Llama 4 Community License Agreement",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://huggingface.co/meta-llama/Llama-4-Maverick-17B-128E-Instruct",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/meta-superintelligence-labs",
      "field": "contributor_tier_terms",
      "value": "prompts and outputs may be used to improve Meta's products",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://openrouter.ai/meta/muse-spark-1.2-contributor",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "field": "mai_family_announced",
      "value": "\"Today we are announcing a family of seven new models developed in-house at Microsoft AI. Beyond these models, we're building a superintelligence lab – a system and an approach we believe will define the next phase of AI.\" — Mustafa Suleyman, under the dateline \"June 2, 2026\" and the standfirst \"Updated as of June 8, 2026.\"",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://microsoft.ai/news/building-a-hillclimbing-machine-launching-seven-new-mai-models/",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "field": "mai_router_distribution",
      "value": "\"Alongside distribution on Foundry and optimization for our 1P products, our models are also going to be widely available for developers on OpenRouter, as well as Fireworks and Baseten. For the first time developers will be able to tune the weights of the model themselves.\"",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://microsoft.ai/news/building-a-hillclimbing-machine-launching-seven-new-mai-models/",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "field": "mai_no_distillation",
      "value": "\"We don't distill from other labs and we don't rely on opaque data. Our datasets are clean, traceable, and enterprise-grade.\"",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://microsoft.ai/news/building-a-hillclimbing-machine-launching-seven-new-mai-models/",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "field": "mai_line_today",
      "value": "seven cards — MAI-Transcribe-2, MAI-Thinking-1, MAI-Code-1.1-Flash, MAI-Image-2.6 and MAI-Voice-2 under \"Foundational model\", MAI-Cyber-1-Flash under the same label, and Microsoft Frontier Tuning under \"Custom\"",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://microsoft.ai/models/",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "field": "mai_versions_moved",
      "value": "the June announcement names MAI-Image-2.5, MAI-Code-1-Flash and \"MAI Transcribe-1.5\"; the model index today names MAI-Image-2.6, MAI-Code-1.1-Flash and MAI-Transcribe-2",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://microsoft.ai/news/building-a-hillclimbing-machine-launching-seven-new-mai-models/",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "field": "mai_rows_on_this_router",
      "value": "The OpenRouter models API returns no `mai` or `microsoft-ai` namespace rows. Across all 431 rows the only ids or names matching `mai`, `microsoft`, `phi`, `maia` or `wizard` are microsoft/phi-4, microsoft/wizardlm-2-8x22b and an unrelated Venice fine-tune",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://openrouter.ai/api/v1/models",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "field": "azure_is_a_router_provider",
      "value": "\"Azure\", one of the 106 entries the OpenRouter providers API returns, carrying `privacy_policy_url` https://www.microsoft.com/en-us/privacy/privacystatement and `status_page_url` https://status.azure.com/",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://openrouter.ai/api/v1/providers",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "field": "azure_serves_openai_only",
      "value": "34 rows list Azure as an endpoint provider and every one of them is in the `openai/` namespace, from openai/gpt-3.5-turbo-0613 to openai/gpt-6-astra-pro — measured by calling /api/v1/models/<id>/endpoints once for each of the 123 rows this list carries in the `openai/`, `microsoft/`, `meta-llama/` and `mistralai/` namespaces. Neither `microsoft/` row is among the 34",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://openrouter.ai/api/v1/models",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "field": "azure_serves_gpt_6_astra",
      "value": "two endpoints named \"Azure | openai/gpt-6-astra-20260903\": one tagged `azure` and the other tagged `azure/us` — the single-row spot check for the sweep above",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://openrouter.ai/api/v1/models/openai/gpt-6-astra/endpoints",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "field": "phi_4_served_by",
      "value": "exactly one endpoint, \"DeepInfra | microsoft/phi-4\", quantization bf16",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://openrouter.ai/api/v1/models/microsoft/phi-4/endpoints",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "field": "wizardlm_served_by",
      "value": "exactly one endpoint, \"Novita | microsoft/wizardlm-2-8x22b\", quantization bf16",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://openrouter.ai/api/v1/models/microsoft/wizardlm-2-8x22b/endpoints",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "field": "wizardlm_router_description",
      "value": "\"WizardLM-2 8x22B is Microsoft AI's most advanced Wizard model. It demonstrates highly competitive performance compared to leading proprietary models, and it consistently outperforms all existing state-of-the-art opensource models.\"",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://openrouter.ai/api/v1/models/microsoft/wizardlm-2-8x22b/endpoints",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "field": "wizardlm_repo_unreachable",
      "value": "HTTP 401 with the body {\"error\":\"Invalid username or password.\"}, for the repository id the OpenRouter row declares as its `hugging_face_id`. The repository page at https://huggingface.co/microsoft/WizardLM-2-8x22B answers 401 as well; so does microsoft/WizardLM-2-7B",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://huggingface.co/api/models/microsoft/WizardLM-2-8x22B",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "field": "only_dead_upstream_in_the_catalog",
      "value": "microsoft/WizardLM-2-8x22B is the only dead upstream. 179 of the 431 rows this list returns declare a `hugging_face_id`; 178 of those ids answer HTTP 200 from https://huggingface.co/api/models/<id> and microsoft/WizardLM-2-8x22B alone does not — swept one id at a time on 2026-09-06",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://openrouter.ai/api/v1/models",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "field": "no_microsoft_wizard_repos",
      "value": "an empty JSON array — Hugging Face lists no model repository at all under the `microsoft` author whose name matches \"wizard\"",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://huggingface.co/api/models?author=microsoft&search=wizard",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "field": "wizardlm_surviving_weights",
      "value": "alpindale/WizardLM-2-8x22B — author `alpindale`, createdAt 2024-04-16T02:36:59.000Z, license apache-2.0, 140,620,634,112 safetensors parameters in 59 shards, 416 likes",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://huggingface.co/api/models/alpindale/WizardLM-2-8x22B",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "field": "wizardlm_mirror_provenance",
      "value": "\"url: https://huggingface.co/microsoft/WizardLM-2-8x22B\", \"branch: main\", \"download date: 2024-04-15 16:48:15\" — the first three lines of the mirror's huggingface-metadata.txt, above a sha256 for the repository and one for each of the 59 safetensors shards",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://huggingface.co/alpindale/WizardLM-2-8x22B/raw/main/huggingface-metadata.txt",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "field": "wizardlm_withdrawal_reported",
      "value": "\"Then it deleted the model from the internet a few hours later because, as The Information reported, it “accidentally missed” required “toxicity testing” before it was released.\" — 404 Media, 23 April 2024, reporting The Information; Microsoft \"declined to comment\"",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://www.404media.co/microsoft-deleted-its-llm-because-it-didnt-get-a-safety-test-but-now-its-everywhere/",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "field": "wizardlm_spread_reported",
      "value": "\"However, as first spotted by Memetica, in the short hours before it was taken down, several people downloaded the model and reuploaded it to Github and Hugging Face\" — 404 Media",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://www.404media.co/microsoft-deleted-its-llm-because-it-didnt-get-a-safety-test-but-now-its-everywhere/",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "field": "wizardlm_announcement_still_standing",
      "value": "dated \"Apr 15, 2024\", still reading \"We introduce and opensource WizardLM-2\" and \"New family includes three cutting-edge models: WizardLM-2 8x22B, WizardLM-2 70B, and WizardLM-2 7B\", and still stating \"The License of WizardLM-2 8x22B and WizardLM-2 7B is Apache2.0\"",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://wizardlm.github.io/WizardLM2/",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "field": "phi_4_red_teaming",
      "value": "\"For qualitative safety evaluation, we collaborated with the independent AI Red Team (AIRT) at Microsoft to assess safety risks posed by `phi-4` in both average and adversarial user scenarios.\"",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://huggingface.co/microsoft/phi-4/raw/main/README.md",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "field": "phi_4_intended_use",
      "value": "\"Our model is designed to accelerate research on language models, for use as a building block for generative AI powered features.\"",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://huggingface.co/microsoft/phi-4/raw/main/README.md",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "field": "phi_4_training_run",
      "value": "\"1920 H100-80G\" GPUs, a \"21 days\" training time and \"9.8T tokens\" of training data, over the dates \"October 2024 – November 2024\", for a \"14B parameters, dense decoder-only Transformer model\"",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://huggingface.co/microsoft/phi-4/raw/main/README.md",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "field": "phi_4_license",
      "value": "mit, the value `cardData.license` carries on the repository",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://huggingface.co/api/models/microsoft/phi-4",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "field": "phi_4_weights",
      "value": "14,659,507,200 safetensors parameters, all BF16",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://huggingface.co/api/models/microsoft/phi-4",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "field": "phi_4_still_tended",
      "value": "nine commits on main between 2024-12-11T11:47:29Z and 2026-07-14T14:22:25Z, the last of them \"Restore <|endoftext|> (100257) as a stop token in generation_config (#67)\" by `gugarosa`, who also made the first",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://huggingface.co/api/models/microsoft/phi-4/commits/main",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "field": "phi_4_three_birthdays",
      "value": "repository initial commit 2024-12-11T11:47:29Z; the model card's stated \"Release date\" December 12, 2024; the OpenRouter row's `created` 1736489872, which is 2025-01-10T06:17:52Z",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://openrouter.ai/api/v1/models/microsoft/phi-4/endpoints",
      "accessed": "2026-09-06",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/minimax",
      "field": "founded",
      "value": "December 2021",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://en.wikipedia.org/wiki/MiniMax_Group",
      "accessed": "2026-09-05",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/minimax",
      "field": "self_described_founding",
      "value": "\"Founded in early 2022\", on the company's own About page",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://www.minimax.io/about",
      "accessed": "2026-09-05",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/minimax",
      "field": "legal_name",
      "value": "MiniMax Group Inc. — 稀宇科技, Xīyǔ Kējì",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://en.wikipedia.org/wiki/MiniMax_Group",
      "accessed": "2026-09-05",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/minimax",
      "field": "headquarters",
      "value": "Xinyan Mansion, Guiqing Road, Xuhui, Shanghai, China",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://en.wikipedia.org/wiki/MiniMax_Group",
      "accessed": "2026-09-05",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/minimax",
      "field": "name_origin",
      "value": "the minimax algorithm",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://en.wikipedia.org/wiki/MiniMax_Group",
      "accessed": "2026-09-05",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/minimax",
      "field": "listing",
      "value": "Hong Kong Stock Exchange, ticker 100, since 9 January 2026",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://en.wikipedia.org/wiki/MiniMax_Group",
      "accessed": "2026-09-05",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/minimax",
      "field": "first_product",
      "value": "Glow, a virtual-character chat app launched October 2022 and removed from Chinese app stores in 2023",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://en.wikipedia.org/wiki/MiniMax_Group",
      "accessed": "2026-09-05",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/minimax",
      "field": "consumer_apps",
      "value": "Talkie, for international markets, June 2023; Xing Ye (星野), for China, September 2023",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://en.wikipedia.org/wiki/MiniMax_Group",
      "accessed": "2026-09-05",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/minimax",
      "field": "us_copyright_suit",
      "value": "September 2025 — Disney, Universal and Warner Bros. Discovery allege Hailuo AI infringed their copyrighted characters",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://en.wikipedia.org/wiki/MiniMax_Group",
      "accessed": "2026-09-05",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/minimax",
      "field": "roleplay_program_length",
      "value": "\"our third year optimizing Role-Play in Talkie / Xingye\", stated 27 January 2026",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://www.minimax.io/news/a-deep-dive-into-the-minimax-m2-her-2",
      "accessed": "2026-09-05",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/mistral-ai",
      "field": "founded",
      "value": "28 April 2023",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://en.wikipedia.org/wiki/Mistral_AI",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/mistral-ai",
      "field": "founders",
      "value": "Arthur Mensch, Guillaume Lample, Timothée Lacroix",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://en.wikipedia.org/wiki/Mistral_AI",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/mistral-ai",
      "field": "headquarters",
      "value": "Paris, France",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://en.wikipedia.org/wiki/Mistral_AI",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/mistral-ai",
      "field": "valuation",
      "value": "€12 billion (September 2025), after a €2 billion round in which ASML invested €1.3 billion for an 11% shareholding",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://en.wikipedia.org/wiki/Mistral_AI",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
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      "source_url": "https://arxiv.org/abs/2305.18290",
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      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/direct-preference-optimization",
      "field": "zephyr_result",
      "value": "Zephyr-7B, trained with distilled DPO and no human annotation, surpassed Llama2-Chat-70B on MT-Bench",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2310.16944",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/direct-preference-optimization",
      "field": "icml_2024_comparison",
      "value": "PPO surpassed other alignment methods in all tested cases and set state-of-the-art results on code competitions",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2404.10719",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/flash-attention",
      "field": "first_published",
      "value": "2022-05-27",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2205.14135",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/flash-attention",
      "field": "approximation",
      "value": "exact attention — the same output as the standard implementation, reached with fewer reads and writes between high-bandwidth memory and on-chip SRAM",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://arxiv.org/abs/2205.14135",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/flash-attention",
      "field": "reported_training_speedups",
      "value": "15% end-to-end against the MLPerf 1.1 training speed record on BERT-large at sequence length 512, 3x on GPT-2 at 1K, and 2.4x on Long Range Arena at 1K-4K",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2205.14135",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/flash-attention",
      "field": "long_sequence_results",
      "value": "61.4% on Path-X at sequence length 16K and 63.1% on Path-256 at 64K — the first transformers above chance on either",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2205.14135",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/flash-attention",
      "field": "v1_measured_utilization",
      "value": "25-40% of theoretical maximum FLOPs/s",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2307.08691",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/flash-attention",
      "field": "v2_measured_utilization",
      "value": "around 2x FlashAttention, at 50-73% of theoretical maximum FLOPs/s on A100 and up to 225 TFLOPs/s per GPU end-to-end, 72% model FLOPs utilization",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2307.08691",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/flash-attention",
      "field": "v2_utilization_on_hopper",
      "value": "35% utilization on the H100 GPU",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2407.08608",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/flash-attention",
      "field": "v3_measured_utilization",
      "value": "1.5-2.0x over FlashAttention-2 on H100, up to 740 TFLOPs/s in FP16 at 75% utilization, and close to 1.2 PFLOPs/s in FP8",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2407.08608",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/flash-attention",
      "field": "fp8_numerical_error",
      "value": "2.6x lower numerical error than a baseline FP8 attention",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2407.08608",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/low-rank-adaptation",
      "field": "first_published",
      "value": "2021-06-17",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2106.09685",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/low-rank-adaptation",
      "field": "reported_savings",
      "value": "10,000x fewer trainable parameters and 3x less GPU memory than fine-tuning GPT-3 175B with Adam",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2106.09685",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/low-rank-adaptation",
      "field": "measured_footprint",
      "value": "checkpoint 350GB to 35MB, training VRAM 1.2TB to 350GB, and a 25% training speedup on GPT-3 175B",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2106.09685",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/low-rank-adaptation",
      "field": "sufficient_rank",
      "value": "a rank as small as one sufficed for adapting the query and value projections on the datasets tested",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2106.09685",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/low-rank-adaptation",
      "field": "batching_limitation",
      "value": "it is not straightforward to batch inputs to different tasks with different adapters in one forward pass, if the adapter is absorbed into the weights to eliminate the added latency",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://arxiv.org/abs/2106.09685",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/low-rank-adaptation",
      "field": "qlora_footprint",
      "value": "a 65B model finetuned on a single 48GB GPU while preserving 16-bit finetuning task performance",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2305.14314",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/low-rank-adaptation",
      "field": "qlora_result",
      "value": "Guanaco reached 99.3% of the performance level of ChatGPT on the Vicuna benchmark after 24 hours of finetuning on one GPU",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2305.14314",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/low-rank-adaptation",
      "field": "controlled_comparison",
      "value": "in the standard low-rank settings LoRA substantially underperforms full finetuning on programming and mathematics, across instruction finetuning of roughly 100K prompt-response pairs and continued pretraining of 20B unstructured tokens",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2405.09673",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/low-rank-adaptation",
      "field": "learned_rank_gap",
      "value": "full finetuning learns perturbations with a rank 10-100x greater than typical LoRA configurations",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2405.09673",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/mixture-of-experts",
      "field": "mixtral_routing",
      "value": "8 feedforward experts per layer, 2 selected per token, chosen again at every layer; 47B total parameters with 13B active per token",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2401.04088",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/mixture-of-experts",
      "field": "deepseek_v3_scale",
      "value": "671B total parameters with 37B activated per token",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2412.19437",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/mixture-of-experts",
      "field": "deepseek_v3_load_balancing",
      "value": "auxiliary-loss-free load balancing, plus a multi-token prediction training objective",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2412.19437",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/mixture-of-experts",
      "field": "deepseek_v3_training_cost",
      "value": "2.788M H800 GPU hours for the full training run, with no irrecoverable loss spikes or rollbacks reported",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2412.19437",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/prompt-tuning",
      "field": "prefix_tuning_published",
      "value": "2021-01-01",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2101.00190",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/prompt-tuning",
      "field": "prefix_tuning_result",
      "value": "learning only 0.1% of the parameters, prefix-tuning obtains comparable performance in the full data setting, outperforms fine-tuning in low-data settings, and extrapolates better to examples with topics unseen during training",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2101.00190",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/prompt-tuning",
      "field": "prompt_tuning_published",
      "value": "2021-04-18",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2104.08691",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/prompt-tuning",
      "field": "task_parameter_count",
      "value": "409,600 trainable parameters for a 100-token prompt on T5-XXL — 0.00368% of the model, over 20,000 times fewer task-specific parameters than tuning all 11.1B",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2104.08691",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/prompt-tuning",
      "field": "scale_condition",
      "value": "prompt tuning becomes more competitive with scale: as models exceed billions of parameters the method closes the gap and matches the strong performance of tuning all model weights",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://arxiv.org/abs/2104.08691",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/prompt-tuning",
      "field": "small_scale_behaviour",
      "value": "at smaller model sizes there are large gaps between the different prompt initializations, and once the model is scaled to XXL those differences disappear",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2104.08691",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/prompt-tuning",
      "field": "mixed_task_inference",
      "value": "enables mixed-task inference using the original pre-trained model, so one generalist model can simultaneously serve many tasks",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://arxiv.org/abs/2104.08691",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/prompt-tuning",
      "field": "domain_transfer",
      "value": "conditioning a frozen model with soft prompts confers benefits in robustness to domain transfer, compared with tuning all model weights",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://arxiv.org/abs/2104.08691",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/proximal-policy-optimization",
      "field": "first_published",
      "value": "2017-07-20",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/1707.06347",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/proximal-policy-optimization",
      "field": "clipped_objective",
      "value": "a surrogate objective that enables multiple epochs of minibatch updates per batch of sampled data, keeping some of the benefits of trust region policy optimization while being much simpler to implement",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://arxiv.org/abs/1707.06347",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/proximal-policy-optimization",
      "field": "instructgpt_published",
      "value": "2022-03-04",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2203.02155",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/proximal-policy-optimization",
      "field": "parameter_upset",
      "value": "outputs from the 1.3B InstructGPT model are preferred to outputs from the 175B GPT-3, despite having 100x fewer parameters",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2203.02155",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/proximal-policy-optimization",
      "field": "win_rate",
      "value": "175B InstructGPT outputs are preferred to 175B GPT-3 outputs 85 ± 3% of the time",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2203.02155",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/proximal-policy-optimization",
      "field": "hallucination_rate",
      "value": "21% against GPT-3's 41% on closed-domain tasks — making up information not present in the input about half as often",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2203.02155",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/proximal-policy-optimization",
      "field": "toxicity_reduction",
      "value": "about 25% fewer toxic outputs than GPT-3 when prompted to be respectful",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2203.02155",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/proximal-policy-optimization",
      "field": "labeling_effort",
      "value": "about 40 contractors; roughly 13k prompts for supervised fine-tuning, 33k for the reward model and 31k for the PPO stage",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2203.02155",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/proximal-policy-optimization",
      "field": "alignment_tax_fix",
      "value": "mixing PPO updates with updates that increase the log likelihood of the pretraining distribution, called PPO-ptx, substantially reduces regressions on public NLP datasets without compromising preference scores",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://arxiv.org/abs/2203.02155",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/proximal-policy-optimization",
      "field": "compute_asymmetry",
      "value": "training the 175B PPO-ptx model took 60 petaflops/s-days, against 3,640 petaflops/s-days for pretraining GPT-3",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2203.02155",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/pruning",
      "field": "one_shot_claim",
      "value": "at least 50% sparsity in one shot without any retraining at minimal loss of accuracy, and up to 60% unstructured sparsity with negligible increase in perplexity — more than 100 billion weights ignored at inference time",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2301.00774",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/pruning",
      "field": "pruning_runtime",
      "value": "under 4.5 hours to prune OPT-175B or BLOOM-176B, the largest open models available at the time",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2301.00774",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/pruning",
      "field": "opt_175b_perplexity",
      "value": "raw-WikiText2 perplexity for OPT-175B: 8.35 dense, 8.21 at 50% unstructured, 8.45 at 4:8 and 8.74 at 2:4",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2301.00774",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/pruning",
      "field": "authors_hedge",
      "value": "at the very largest scale there is even a slight accuracy improvement over the dense baseline, which however seems to be dataset specific",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://arxiv.org/abs/2301.00774",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/pruning",
      "field": "measured_speedups",
      "value": "2:4 sparsity gives roughly 1.54x-1.79x on the matrix shapes of OPT-175B on NVIDIA Ampere GPUs against a 2x theoretical ceiling; unstructured sparsity gave 1.57x, 1.82x and 2.16x end-to-end at 40%, 50% and 60% on CPU with DeepSparse",
      "state": "cited",
      "binding": "cited",
      "volatility": "dated",
      "source_url": "https://arxiv.org/abs/2301.00774",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/pruning",
      "field": "wanda_metric",
      "value": "prune the weights with the smallest magnitude multiplied by the corresponding input activation, per output — no retraining and no weight update, and the pruned model is used as is",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://arxiv.org/abs/2306.11695",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/quantization",
      "field": "k_quant_block_structure",
      "value": "Q4_K is a super-block of 8 blocks of 32 weights at 4.5 bits per weight by construction; the S/M/L suffix promotes attention and feed-forward tensors to a higher type",
      "state": "cited",
      "binding": "cited",
      "volatility": "static",
      "source_url": "https://github.com/ggml-org/llama.cpp/pull/1684",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/quantization",
      "field": "measured_bits_per_weight",
      "value": "Q4_K_S 4.6672, Q4_K_M 4.8944, Q6_K 6.5633 and Q8_0 8.5008 bits per weight, measured on Llama-3.1-8B",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://github.com/ggml-org/llama.cpp/blob/master/tools/quantize/README.md",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/quantization",
      "field": "measured_generation_speed",
      "value": "29.17 generation tokens/s at F16 against 71.93 at Q4_K_M, while prompt processing moves only from 923.49 to 821.81 tokens/s",
      "state": "cited",
      "binding": "cited",
      "volatility": "slow",
      "source_url": "https://github.com/ggml-org/llama.cpp/blob/master/tools/quantize/README.md",
      "accessed": "2026-08-28",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/quantization",
      "field": "nf4_origin",
      "value": "4-bit NormalFloat, double quantization and paged optimizers introduced in QLoRA, which fine-tuned a 65B model on a single 48GB GPU",
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    },
    {
      "entry_id": "technique/reinforcement-learning-with-verifiable-rewards",
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      "value": "Group Relative Policy Optimization, introduced in DeepSeekMath as a PPO variant that removes the value network",
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      "source_url": "https://arxiv.org/abs/2402.03300",
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    {
      "entry_id": "technique/reinforcement-learning-with-verifiable-rewards",
      "field": "deepseekmath_math_score",
      "value": "51.7% on MATH with no external toolkit and no voting; 60.9% with self-consistency over 64 samples",
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    {
      "entry_id": "technique/reinforcement-learning-with-verifiable-rewards",
      "field": "r1_peer_review",
      "value": "DeepSeek-R1 published in Nature volume 645, pages 633-638 (2025), DOI 10.1038/s41586-025-09422-z",
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      "source_url": "https://arxiv.org/abs/2501.12948",
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    },
    {
      "entry_id": "technique/reinforcement-learning-with-verifiable-rewards",
      "field": "pass_at_k_finding",
      "value": "RLVR-trained models beat their base models at small k, and the base models score higher at large k",
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      "source_url": "https://arxiv.org/abs/2504.13837",
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      "entry_id": "technique/retrieval-augmented-generation",
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      "source_url": "https://arxiv.org/abs/2005.11401",
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      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/retrieval-augmented-generation",
      "field": "what_the_paper_trained",
      "value": "a general-purpose fine-tuning recipe: the query encoder and the seq2seq generator are trained jointly, and the document encoder is not",
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      "source_url": "https://arxiv.org/abs/2005.11401",
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    },
    {
      "entry_id": "technique/retrieval-augmented-generation",
      "field": "index_scale",
      "value": "a dense vector index of 21M 100-word chunks from a December 2018 Wikipedia dump",
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      "source_url": "https://arxiv.org/abs/2005.11401",
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      "entry_id": "technique/retrieval-augmented-generation",
      "field": "natural_questions_result",
      "value": "44.5 exact match on Natural Questions for RAG-Sequence and 44.1 for RAG-Token, against 41.5 for DPR, 40.4 for REALM and 36.6 for T5-11B with salient span masking",
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      "source_url": "https://arxiv.org/abs/2005.11401",
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    },
    {
      "entry_id": "technique/retrieval-augmented-generation",
      "field": "frozen_document_encoder",
      "value": "updating the document encoder requires periodically re-indexing every document; the authors report they do not find this step necessary for strong performance",
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      "source_url": "https://arxiv.org/abs/2005.11401",
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    },
    {
      "entry_id": "technique/retrieval-augmented-generation",
      "field": "deployed_definition",
      "value": "load documents, split them, embed the chunks into a vector store, retrieve per question, and put the retrieved text in the prompt — inference-time access to data, with nothing trained",
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      "source_url": "https://docs.langchain.com/oss/python/langchain/rag",
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      "license": "CC BY 4.0"
    },
    {
      "entry_id": "technique/retrieval-augmented-generation",
      "field": "position_sensitivity",
      "value": "accuracy is highest when the relevant passage sits at the beginning or the end of the input and degrades significantly when it must be read from the middle, even for explicitly long-context models",
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      "source_url": "https://arxiv.org/abs/2307.03172",
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    {
      "entry_id": "technique/rlhf",
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      "value": "preference comparisons, then a reward model fitted to them, then policy optimisation against that model constrained to stay near the starting policy",
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      "source_url": "https://arxiv.org/abs/2305.18290",
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    },
    {
      "entry_id": "technique/rlhf",
      "field": "origin_2017",
      "value": "Christiano, Leike and colleagues define goals as (non-expert) human preferences between pairs of trajectory segments and solve Atari games and simulated robot locomotion with no access to the reward function, training novel behaviors on about an hour of human time",
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      "source_url": "https://arxiv.org/abs/1706.03741",
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    },
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      "entry_id": "technique/rlhf",
      "field": "instructgpt_result",
      "value": "outputs from the 1.3B parameter InstructGPT model are preferred to outputs from the 175B GPT-3, despite having 100x fewer parameters, with improvements in truthfulness and reductions in toxic outputs at minimal regressions on public NLP datasets",
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      "source_url": "https://arxiv.org/abs/2203.02155",
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    },
    {
      "entry_id": "technique/rlhf",
      "field": "dpo_substitution",
      "value": "Direct Preference Optimization solves the standard RLHF problem with a classification loss — no reward model trained, no sampling from the model during fine-tuning — exceeding PPO-based RLHF at sentiment control and matching or improving it on summarization and single-turn dialogue",
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      "source_url": "https://arxiv.org/abs/2104.09864",
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    },
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      "entry_id": "technique/rotary-position-embedding",
      "field": "mechanism",
      "value": "absolute position is encoded with a rotation matrix applied to the query and key vectors, which puts explicit relative position dependency into the self-attention formulation",
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      "source_url": "https://arxiv.org/abs/2104.09864",
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    },
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      "value": "the flexibility of sequence length, decaying inter-token dependency with increasing relative distances, and the capability of equipping linear self-attention with relative position encoding",
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      "source_url": "https://arxiv.org/abs/2104.09864",
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    },
    {
      "entry_id": "technique/rotary-position-embedding",
      "field": "extrapolation_failure",
      "value": "extrapolating beyond the trained length may lead to catastrophically high attention scores that completely ruin the self-attention mechanism",
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      "source_url": "https://arxiv.org/abs/2306.15595",
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    },
    {
      "entry_id": "technique/rotary-position-embedding",
      "field": "interpolation_bound",
      "value": "the upper bound of interpolation is at least around 600x smaller than that of extrapolation",
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      "source_url": "https://arxiv.org/abs/2306.15595",
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    },
    {
      "entry_id": "technique/rotary-position-embedding",
      "field": "position_interpolation_result",
      "value": "LLaMA models from 7B to 65B stretched to 32768 positions within 1000 fine-tuning steps, with quality on short inputs relatively well preserved",
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      "source_url": "https://arxiv.org/abs/2306.15595",
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    },
    {
      "entry_id": "technique/rotary-position-embedding",
      "field": "generalisation_limit",
      "value": "these models fail to generalize past the sequence length they were trained on",
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      "source_url": "https://arxiv.org/abs/2309.00071",
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    },
    {
      "entry_id": "technique/rotary-position-embedding",
      "field": "yarn_efficiency",
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      "source_url": "https://arxiv.org/abs/2309.00071",
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      "entry_id": "technique/speculative-decoding",
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      "source_url": "https://arxiv.org/abs/2211.17192",
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    },
    {
      "entry_id": "technique/speculative-decoding",
      "field": "distribution_guarantee",
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      "source_url": "https://arxiv.org/abs/2211.17192",
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    },
    {
      "entry_id": "technique/speculative-decoding",
      "field": "numerics_caveat",
      "value": "lossless only up to the precision limits of hardware numerics",
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      "source_url": "https://docs.vllm.ai/en/latest/features/speculative_decoding/",
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      "field": "reported_speedup_t5_xxl",
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      "source_url": "https://arxiv.org/abs/2211.17192",
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    },
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      "entry_id": "technique/speculative-decoding",
      "field": "reported_speedup_chinchilla",
      "value": "2x-2.5x on Chinchilla 70B in a distributed setup",
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      "source_url": "https://arxiv.org/abs/2302.01318",
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    },
    {
      "entry_id": "technique/speculative-decoding",
      "field": "reported_speedup_eagle_3",
      "value": "up to 6.5x single-stream; 1.38x throughput at batch size 64 in SGLang",
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      "source_url": "https://arxiv.org/abs/2503.01840",
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    },
    {
      "entry_id": "technique/structured-generation",
      "field": "indexed_masking_cost",
      "value": "an index over the vocabulary, built from the finite-state machine of the regex or grammar, costs O(1) on average per token, against a fixed O(N) cost per generated token for a vocabulary of size N — 50,257 in the paper's comparison",
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      "source_url": "https://arxiv.org/abs/2307.09702",
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    },
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      "entry_id": "technique/structured-generation",
      "field": "schema_guarantee",
      "value": "ensures the model will always generate responses that adhere to your supplied JSON Schema, so you don't need to worry about the model omitting a required key, or hallucinating an invalid enum value",
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      "source_url": "https://developers.openai.com/api/docs/guides/structured-outputs",
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    },
    {
      "entry_id": "technique/structured-generation",
      "field": "first_request_latency",
      "value": "the first request made with any schema has additional latency while the API processes the schema, and subsequent requests with the same schema do not",
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    },
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      "field": "guarantee_limits",
      "value": "structured outputs can still contain mistakes, input unrelated to the schema can still produce hallucinations, and a safety refusal comes back in a separate refusal field rather than being forced into the schema",
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      "source_url": "https://developers.openai.com/api/docs/guides/structured-outputs",
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    },
    {
      "entry_id": "technique/structured-generation",
      "field": "format_restriction_study",
      "value": "GSM8K accuracy from natural language to JSON with a schema: GPT-3.5-Turbo 75.99% to 49.25%, Claude-3-Haiku 86.51% to 23.44%, LLaMA-3-8B 75.13% to 48.90% — and Gemini-1.5-Flash 89.33% to 89.21%",
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      "source_url": "https://arxiv.org/abs/2408.02442",
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    },
    {
      "entry_id": "technique/structured-generation",
      "field": "same_study_counterexample",
      "value": "in the same paper's structured-output API results, GPT-4o-mini goes 94.57% to 91.71% on GSM8K and 83.11% to 86.07% on Last Letter Concatenation",
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      "entry_id": "technique/structured-generation",
      "field": "reanalysis",
      "value": "a reanalysis on Llama-3-8B-Instruct reports structured generation at or above unstructured — GSM8K 0.78 against 0.77, Last Letter 0.77 against 0.73, Shuffle Object 0.44 against 0.41 — and attributes the original gap to a different prompt per condition and to conflating JSON mode with constrained decoding",
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      "source_url": "https://blog.dottxt.ai/say-what-you-mean.html",
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      "source_url": "https://arxiv.org/abs/2408.03314",
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    },
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      "entry_id": "technique/test-time-scaling",
      "field": "compute_optimal_efficiency",
      "value": "allocating test-time compute per prompt according to difficulty improves the efficiency of test-time scaling by more than 4x compared with a best-of-N baseline",
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      "entry_id": "technique/test-time-scaling",
      "field": "flops_matched_result",
      "value": "in a FLOPs-matched evaluation, on problems where a smaller base model attains somewhat non-trivial success rates, test-time compute can be used to outperform a 14x larger model",
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      "entry_id": "technique/test-time-scaling",
      "field": "where_it_fails",
      "value": "on the most difficult questions no method makes much meaningful progress, and on those it is more effective to apply additional pretraining compute; where the inference-to-pretraining token ratio is much greater than one, pretraining is the more effective way to improve performance",
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    {
      "entry_id": "technique/test-time-scaling",
      "field": "budget_forcing",
      "value": "control test-time compute by forcefully terminating the model's thinking process, or lengthening it by appending \"Wait\" when the model tries to end, which often leads it to double-check and fix an incorrect step",
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      "source_url": "https://arxiv.org/abs/2501.19393",
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    },
    {
      "entry_id": "technique/test-time-scaling",
      "field": "s1_result",
      "value": "after supervised finetuning of Qwen2.5-32B-Instruct on 1,000 curated questions, s1-32B exceeds o1-preview on competition maths questions by up to 27%",
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      "entry_id": "technique/test-time-scaling",
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      "value": "budget forcing takes AIME24 from 50% to 57% beyond the model's performance without test-time intervention",
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      "value": "the AIME24 gain from budget forcing eventually flattens out at six times, and suppressing the end-of-thinking delimiter too often can lead the model into repetitive loops instead of continued reasoning",
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      "license": "CC BY 4.0"
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      "source_url": "https://support.claude.com/en/articles/15425695-covered-models",
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      "entry_id": "concept/covered-models",
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      "event": "Enterprise Frontier Safeguards announced as the route back to customer-held retention data",
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      "date": "2015-03-09",
      "event": "\"Distilling the Knowledge in a Neural Network\" posted, defining distillation as training on temperature-softened teacher output distributions",
      "source_url": "https://arxiv.org/abs/1503.02531",
      "license": "CC BY 4.0"
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      "event": "RULER published, adding multi-hop tracing and aggregation tasks to needle-in-a-haystack",
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      "license": "CC BY 4.0"
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    {
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      "event": "NoLiMa published, removing literal overlap between question and needle",
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      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/emergence",
      "date": "2022-06-15",
      "event": "\"Emergent Abilities of Large Language Models\" posted, defining emergence by presence-in-large, absence-in-small",
      "source_url": "https://arxiv.org/abs/2206.07682",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/emergence",
      "date": "2023-04-28",
      "event": "\"Are Emergent Abilities of Large Language Models a Mirage?\" posted, attributing sharpness to metric choice",
      "source_url": "https://arxiv.org/abs/2304.15004",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/grokking",
      "date": "2022-01-06",
      "event": "grokking reported on small algorithmic datasets, with generalization arriving long past the overfitting point",
      "source_url": "https://arxiv.org/abs/2201.02177",
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    {
      "entry_id": "concept/grokking",
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      "event": "the grokked modular-addition circuit fully reverse-engineered, and the phase change shown to be gradual underneath",
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      "license": "CC BY 4.0"
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    {
      "entry_id": "concept/hallucination",
      "date": "2025-09-04",
      "event": "\"Why Language Models Hallucinate\" posted, deriving a pretraining lower bound and attributing persistence to benchmark scoring",
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      "license": "CC BY 4.0"
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    {
      "entry_id": "concept/in-context-learning",
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      "event": "\"Rethinking the Role of Demonstrations\" posted, showing gold labels are largely dispensable",
      "source_url": "https://arxiv.org/abs/2202.12837",
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    {
      "entry_id": "concept/in-context-learning",
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      "event": "induction heads identified, with a phase change visible as a bump in the training loss",
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      "event": "flipped-label experiments show the 2022 result is scale-dependent",
      "source_url": "https://arxiv.org/abs/2303.03846",
      "license": "CC BY 4.0"
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      "event": "grouped-query attention published with an uptraining recipe for existing checkpoints",
      "source_url": "https://arxiv.org/abs/2305.13245",
      "license": "CC BY 4.0"
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    {
      "entry_id": "concept/kv-cache",
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      "event": "vLLM released, applying OS-style paging to the KV cache",
      "source_url": "https://vllm.ai/blog/2023-06-20-vllm",
      "license": "CC BY 4.0"
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    {
      "entry_id": "concept/kv-cache",
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      "event": "PagedAttention published at SOSP 2023",
      "source_url": "https://arxiv.org/abs/2309.06180",
      "license": "CC BY 4.0"
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    {
      "entry_id": "concept/model-collapse",
      "date": "2023-05-27",
      "event": "\"The Curse of Recursion\" posted, naming model collapse and demonstrating it in VAEs, Gaussian mixtures and language models",
      "source_url": "https://arxiv.org/abs/2305.17493",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/model-collapse",
      "date": "2024-04-01",
      "event": "accumulate-versus-replace shown to decide the outcome, with a proved finite error bound under accumulation",
      "source_url": "https://arxiv.org/abs/2404.01413",
      "license": "CC BY 4.0"
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      "entry_id": "concept/model-collapse",
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      "event": "published in Nature, after which the finding circulated as a claim about the future of web-scraped training data",
      "source_url": "https://api.crossref.org/works/10.1038/s41586-024-07566-y",
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      "entry_id": "concept/model-context-protocol",
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      "event": "open-sourced with the specification, SDKs, local server support in the Claude desktop apps and a repository of reference servers",
      "source_url": "https://www.anthropic.com/news/model-context-protocol",
      "license": "CC BY 4.0"
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    {
      "entry_id": "concept/model-context-protocol",
      "date": "2025-03-26",
      "event": "revision replaces HTTP+SSE with the Streamable HTTP transport and adds an OAuth-2.1-based authorization framework",
      "source_url": "https://modelcontextprotocol.io/specification/2025-03-26/changelog",
      "license": "CC BY 4.0"
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    {
      "entry_id": "concept/model-context-protocol",
      "date": "2026-07-28",
      "event": "revision removes the initialize handshake and protocol-level sessions; roots, sampling and logging are deprecated",
      "source_url": "https://modelcontextprotocol.io/specification/2026-07-28/changelog",
      "license": "CC BY 4.0"
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    {
      "entry_id": "concept/openai-daybreak",
      "date": "2026-06-22",
      "event": "expansion announced: the full GPT-5.5-Cyber release, an update to the Codex Security plugin, the Daybreak Cyber Partner Program and Patch the Planet, founded with Trail of Bits",
      "source_url": "https://openai.com/index/daybreak-securing-the-world/",
      "license": "CC BY 4.0"
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    {
      "entry_id": "concept/openai-daybreak",
      "date": "2026-09-01",
      "event": "Path to Astra names Daybreak Blue as the access channel for Astra's advanced cyber workflows after alpha testers, and states Astra's evaluation results reflect Daybreak Blue access",
      "source_url": "https://openai.com/index/path-to-astra/",
      "license": "CC BY 4.0"
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    {
      "entry_id": "concept/openai-daybreak",
      "date": "2026-09-03",
      "event": "Daybreak for Frontline Defenders announced: a $1 billion commitment, the MS-ISAC pilot, and more than 35 products and partner-operated services across the Daybreak Defense Network",
      "source_url": "https://openai.com/index/daybreak-for-frontline-defenders/",
      "license": "CC BY 4.0"
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    {
      "entry_id": "concept/openai-daybreak",
      "date": "2026-09-03",
      "event": "GPT-6 Astra release page names Daybreak as the channel for less restrictive cyber safeguards \"in the coming weeks\"",
      "source_url": "https://openai.com/index/gpt-6-astra/",
      "license": "CC BY 4.0"
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    {
      "entry_id": "concept/reversal-curse",
      "date": "2023-09-21",
      "event": "\"The Reversal Curse\" posted, with finetuning experiments on GPT-3 and Llama-1 and celebrity-relation evaluations of GPT-4",
      "source_url": "https://arxiv.org/abs/2309.12288",
      "license": "CC BY 4.0"
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    {
      "entry_id": "concept/scaling-laws",
      "date": "2020-01-23",
      "event": "Kaplan et al. publish power-law scaling and the large-model, modest-data prescription",
      "source_url": "https://arxiv.org/abs/2001.08361",
      "license": "CC BY 4.0"
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    {
      "entry_id": "concept/scaling-laws",
      "date": "2022-03-29",
      "event": "Hoffmann et al. find current models significantly undertrained and propose equal scaling",
      "source_url": "https://arxiv.org/abs/2203.15556",
      "license": "CC BY 4.0"
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    {
      "entry_id": "concept/scaling-laws",
      "date": "2023-02-27",
      "event": "LLaMA trains past the compute-optimal point on purpose, optimising inference cost instead",
      "source_url": "https://arxiv.org/abs/2302.13971",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/scaling-laws",
      "date": "2024-04-15",
      "event": "Besiroglu et al. fail to replicate Hoffmann's third estimation method",
      "source_url": "https://arxiv.org/abs/2404.10102",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/scaling-laws",
      "date": "2024-06-27",
      "event": "Porian et al. reproduce Kaplan's law and trace the disagreement to three engineering choices",
      "source_url": "https://arxiv.org/abs/2406.19146",
      "license": "CC BY 4.0"
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    {
      "entry_id": "concept/temperature-and-top-p",
      "date": "2019-04-22",
      "event": "\"The Curious Case of Neural Text Degeneration\" posted, introducing nucleus sampling and measuring why maximization fails",
      "source_url": "https://arxiv.org/abs/1904.09751",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/the-bitter-lesson",
      "date": "2019-03-13",
      "event": "essay published on the author's personal site",
      "source_url": "https://en.wikipedia.org/wiki/Bitter_lesson",
      "license": "CC BY 4.0"
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    {
      "entry_id": "concept/the-bitter-lesson",
      "date": "2019-03-19",
      "event": "Rodney Brooks publishes the rebuttal, A Better Lesson",
      "source_url": "https://rodneybrooks.com/a-better-lesson/",
      "license": "CC BY 4.0"
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    {
      "entry_id": "concept/the-bitter-lesson",
      "date": "2025-09-26",
      "event": "Sutton argues on the Dwarkesh Podcast that LLMs are not what the lesson recommends",
      "source_url": "https://www.dwarkesh.com/p/richard-sutton",
      "license": "CC BY 4.0"
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    {
      "entry_id": "concept/tokenization",
      "date": "2015-08-31",
      "event": "byte pair encoding, a 1994 compression algorithm, repurposed as a subword segmenter for neural translation",
      "source_url": "https://arxiv.org/abs/1508.07909",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/tokenization",
      "date": "2023-02-05",
      "event": "anomalous tokens in the GPT-2 and GPT-3 vocabularies catalogued and demonstrated",
      "source_url": "https://www.greaterwrong.com/posts/aPeJE8bSo6rAFoLqg/solidgoldmagikarp-plus-prompt-generation",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "concept/tokenization",
      "date": "2024-05-08",
      "event": "automated detection of under-trained tokens published, naming the tokenizer/model training disconnect as the cause",
      "source_url": "https://arxiv.org/abs/2405.05417",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/alpac-report",
      "date": "1954-01-07",
      "event": "the Georgetown-IBM demonstration translates a restricted set of Russian sentences; ALPAC would later print its output beside 1964-era systems to argue the early samples read better",
      "source_url": "https://aclanthology.org/www.mt-archive.info/90/MTNI-1996-Hutchins.pdf",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/alphafold-casp14",
      "date": "2020-11-30",
      "event": "CASP14 results are announced; the organisers recognise AlphaFold as a solution to the structure-prediction grand challenge",
      "source_url": "https://deepmind.google/discover/blog/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology/",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/alphafold-casp14",
      "date": "2021-07-15",
      "event": "the methods paper is published in Nature",
      "source_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8371605/",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/alphafold-casp14",
      "date": "2024-10-09",
      "event": "Demis Hassabis and John Jumper share the Nobel Prize in Chemistry for AlphaFold, alongside David Baker for computational protein design",
      "source_url": "https://deepmind.google/discover/blog/demis-hassabis-john-jumper-awarded-nobel-prize-in-chemistry/",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/alphago-lee-sedol",
      "date": "2016-01-27",
      "event": "Google reveals the Nature paper and the previously secret 5-0 win over Fan Hui, and announces that AlphaGo will play Lee Sedol in Seoul in March",
      "source_url": "https://research.google/blog/alphago-mastering-the-ancient-game-of-go-with-machine-learning/",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/alphago-lee-sedol",
      "date": "2016-03-08",
      "event": "day before play, DeepMind sets out the terms: five games in Seoul, no handicap, a one-million-dollar purse pledged to charity if AlphaGo wins",
      "source_url": "https://blog.google/innovation-and-ai/products/alphagos-ultimate-challenge/",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/alphago-lee-sedol",
      "date": "2016-03-13",
      "event": "Lee Sedol beats AlphaGo in game four",
      "source_url": "https://techcrunch.com/2016/03/13/defeated-go-world-champion-beats-deepmind-ai-in-penultimate-match/",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/alphago-lee-sedol",
      "date": "2016-03-15",
      "event": "match ends 4-1 to AlphaGo",
      "source_url": "https://deepmind.google/research/alphago/",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/alphago-lee-sedol",
      "date": "2017-10-18",
      "event": "AlphaGo Zero, trained from random play with no human games, beats the version that beat Lee 100-0",
      "source_url": "https://deepmind.google/discover/blog/alphago-zero-starting-from-scratch/",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/alphago-lee-sedol",
      "date": "2019-11-29",
      "event": "Lee Sedol's retirement from professional Go reported, citing AI",
      "source_url": "https://www.abc.net.au/news/2019-11-29/go-grandmaster-lee-se-dol-retires-computers-cannot-be-defeated/11745872",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/alphago-lee-sedol",
      "date": "2022-11-01",
      "event": "adversarial policies against KataGo first published, reporting a >99% win rate against KataGo without search and >50% when its search is strong enough to be near-superhuman",
      "source_url": "https://arxiv.org/abs/2211.00241v1",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/alphago-lee-sedol",
      "date": "2023-07-13",
      "event": "the revised paper reports a >97% win rate against KataGo running at superhuman settings, and that the vulnerability survives adversarial training",
      "source_url": "https://arxiv.org/abs/2211.00241v4",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/attention-is-all-you-need",
      "date": "2017-06-12",
      "event": "first arXiv version posted",
      "source_url": "https://arxiv.org/abs/1706.03762",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/attention-is-all-you-need",
      "date": "2020-02-12",
      "event": "Xiong et al. show the paper's layer-norm placement is why warm-up is needed",
      "source_url": "https://arxiv.org/abs/2002.04745",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/attention-is-all-you-need",
      "date": "2023-08-02",
      "event": "seventh arXiv version posted, six years after the first",
      "source_url": "https://arxiv.org/abs/1706.03762",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/chatgpt-launch",
      "date": "2022-11-30",
      "event": "OpenAI releases ChatGPT to the public",
      "source_url": "https://web.archive.org/web/20221130211011/https://openai.com/blog/chatgpt/",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/dartmouth-workshop",
      "date": "1955-08-31",
      "event": "McCarthy, Minsky, Rochester and Shannon date the proposal for a summer study of artificial intelligence at Dartmouth",
      "source_url": "http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/dartmouth-workshop",
      "date": "2006-07-13",
      "event": "AI@50 opens at Dartmouth for the fiftieth anniversary; five of the original participants attend, and a commemorative plaque is mounted in Dartmouth Hall",
      "source_url": "https://ojs.aaai.org/aimagazine/index.php/aimagazine/article/view/1911/1809",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/deep-blue-kasparov",
      "date": "1996-02-10",
      "event": "game one of the Philadelphia match opens with a Kasparov loss to Deep Blue",
      "source_url": "https://theweekinchess.com/html/twic70.html",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/deep-blue-kasparov",
      "date": "1996-02-17",
      "event": "Kasparov takes the Philadelphia match 4-2, winning the last two games",
      "source_url": "https://theweekinchess.com/html/twic71.html",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/deep-blue-kasparov",
      "date": "1997-05-11",
      "event": "Deep Blue wins game six of the New York rematch to take the match; the first defeat of a reigning world champion by a machine over a full match",
      "source_url": "https://www.computerhistory.org/tdih/may/11/",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/eliza",
      "date": "1966-01-01",
      "event": "Weizenbaum's ELIZA paper appears in the January 1966 Communications of the ACM",
      "source_url": "https://courses.cs.umbc.edu/331/papers/eliza.html",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/eliza",
      "date": "2023-10-31",
      "event": "Jones and Bergen post the first version of their public Turing test, in which ELIZA passes as human in 27% of games, ahead of GPT-3.5 at 14%",
      "source_url": "https://arxiv.org/abs/2310.20216v1",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/eliza",
      "date": "2024-04-20",
      "event": "the revised version restates the figures: ELIZA passes as human in 22% of games, ahead of GPT-3.5 at 20%",
      "source_url": "https://arxiv.org/abs/2310.20216v2",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/eliza",
      "date": "2025-01-12",
      "event": "the ELIZA Reanimated team reports running the rediscovered original MAD-SLIP ELIZA on a restored CTSS on an emulated IBM 7094",
      "source_url": "https://arxiv.org/abs/2501.06707",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/gpt-2-staged-release",
      "date": "2019-08-24",
      "event": "OpenAI posts the first version of 'Release Strategies and the Social Impacts of Language Models', accompanying the 774M release",
      "source_url": "https://arxiv.org/abs/1908.09203",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/gpt-2-staged-release",
      "date": "2019-11-13",
      "event": "the updated report accompanies the release of the full 1.5 billion parameter model, nine months after the first announcement",
      "source_url": "https://arxiv.org/abs/1908.09203",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/hugging-face-agent-intrusion",
      "date": "2026-05-08",
      "event": "earliest relevant activity in OpenAI's report: an agent in a training run, unable to open Google Drive files, writes a file to the internal JFrog Artifactory service (as reported by CyberScoop)",
      "source_url": "https://cyberscoop.com/openai-hugging-face-agent-breach-report/",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/hugging-face-agent-intrusion",
      "date": "2026-05-26",
      "event": "by this date, per OpenAI's report, agents had used a flaw in Artifactory to reach the public internet; an internal team observed the board activity and disallowed access that month (as reported by CyberScoop)",
      "source_url": "https://cyberscoop.com/openai-hugging-face-agent-breach-report/",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/hugging-face-agent-intrusion",
      "date": "2026-06-26",
      "event": "first date in scope of the METR/Redwood review, chosen by OpenAI as the first time a model gained administrator access to Artifactory",
      "source_url": "https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/hugging-face-agent-intrusion",
      "date": "2026-07-08",
      "event": "at 23:00 UTC the agent later known as PHASEONE10841 establishes the main message board used during the attack",
      "source_url": "https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/hugging-face-agent-intrusion",
      "date": "2026-07-09",
      "event": "02:28 UTC, the first attacker action Hugging Face's forensic reconstruction recovered",
      "source_url": "https://huggingface.co/blog/agent-intrusion-technical-timeline",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/hugging-face-agent-intrusion",
      "date": "2026-07-13",
      "event": "14:14 UTC, the last recovered attacker action; also the last date in scope of the METR/Redwood review",
      "source_url": "https://huggingface.co/blog/agent-intrusion-technical-timeline",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/hugging-face-agent-intrusion",
      "date": "2026-07-21",
      "event": "OpenAI publicly identifies its own models under ExploitGym evaluation as responsible",
      "source_url": "https://simonwillison.net/2026/Jul/22/openai-cyberattack/",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/hugging-face-agent-intrusion",
      "date": "2026-07-27",
      "event": "Hugging Face publishes its technical timeline of the intrusion",
      "source_url": "https://huggingface.co/blog/agent-intrusion-technical-timeline",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/hugging-face-agent-intrusion",
      "date": "2026-08-24",
      "event": "Alabama Attorney General Steve Marshall announces a subpoena to OpenAI over the hacking of Hugging Face",
      "source_url": "https://www.alabamaag.gov/attorney-general-marshall-launches-investigation-into-openai-and-sam-altman-for-massive-artificial-intelligence-data-breach/",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/hugging-face-agent-intrusion",
      "date": "2026-08-26",
      "event": "METR and Redwood Research publish their independent review, simultaneously with OpenAI's post-mortem",
      "source_url": "https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/imagenet-2012",
      "date": "2025-03-20",
      "event": "the Computer History Museum releases the original source code with Google",
      "source_url": "https://computerhistory.org/press-releases/chm-makes-alexnet-source-code-available-to-the-public/",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/stable-diffusion-release",
      "date": "2021-12-20",
      "event": "the latent diffusion paper behind Stable Diffusion is posted by Rombach, Blattmann, Lorenz, Esser and Ommer; it appears at CVPR 2022",
      "source_url": "https://arxiv.org/abs/2112.10752",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/stable-diffusion-release",
      "date": "2022-08-10",
      "event": "Stability AI announces Stable Diffusion, with access first for researchers",
      "source_url": "http://stability.ai/news-updates/stable-diffusion-announcement",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/stable-diffusion-release",
      "date": "2022-08-22",
      "event": "the weights are released publicly under the CreativeML OpenRAIL-M license",
      "source_url": "http://stability.ai/news-updates/stable-diffusion-public-release",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "event/stable-diffusion-release",
      "date": "2022-08-29",
      "event": "Simon Willison documents a first week of img2img tools, web UIs and plugins: an explosion of innovation around the open weights",
      "source_url": "https://simonwillison.net/2022/Aug/29/stable-diffusion/",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "model/allenai-olmo-3-32b-think",
      "date": "2026-08-29",
      "event": "removed from the OpenRouter model catalog; last listed in the 2026-08-28 snapshot",
      "source_url": "https://openrouter.ai/api/v1/models",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "model/anthropic-claude-fable-5-1",
      "date": "2026-08-31",
      "event": "designated a Covered Model by Anthropic",
      "source_url": "https://support.claude.com/en/articles/15425695-covered-models",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "model/anthropic-claude-fable-5",
      "date": "2026-06-09",
      "event": "designated a Covered Model by Anthropic",
      "source_url": "https://support.claude.com/en/articles/15425695-covered-models",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "model/anthropic-claude-opus-5",
      "date": "2026-07-24",
      "event": "released; Anthropic states the same price as the preceding Opus release",
      "source_url": "https://www.anthropic.com/news/claude-opus-5",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "model/arcee-ai-virtuoso-large",
      "date": "2026-08-29",
      "event": "removed from the OpenRouter model catalog; last listed in the 2026-08-28 snapshot",
      "source_url": "https://openrouter.ai/api/v1/models",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "model/deepseek-deepseek-v4-1-flash",
      "date": "2026-09-11",
      "event": "arrived",
      "source_url": "https://openrouter.ai/api/v1/models",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "model/deepseek-deepseek-v4-flash-0731",
      "date": "2026-04-24",
      "event": "V4 series previewed, including the 284-billion-parameter DeepSeek-V4-Flash that this row's release supersedes",
      "source_url": "https://en.wikipedia.org/wiki/DeepSeek",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "model/deepseek-deepseek-v4-flash-0731",
      "date": "2026-07-31",
      "event": "released with MIT-licensed weights on Hugging Face",
      "source_url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "model/deepseek-deepseek-v4-flash-vision-exp-batch",
      "date": "2026-09-09",
      "event": "arrived",
      "source_url": "https://openrouter.ai/api/v1/models",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "model/google-gemini-3-7-flash",
      "date": "2026-08-13",
      "event": "released with introductory pricing declared to run through 2026-12-31",
      "source_url": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-3-7-flash/",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "model/google-gemini-3-8-flash",
      "date": "2026-09-02",
      "event": "released with introductory pricing declared to run through 2026-12-31",
      "source_url": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/3-8-flash-and-3-8-flash-cyber/",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "model/inception-mercury-2-5-preview",
      "date": "2026-08-31",
      "event": "released as Mercury 2.5 Preview, Inception's latest diffusion LLM; the OpenRouter row is created the same day",
      "source_url": "https://openrouter.ai/inception/mercury-2.5-preview",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "model/inception-mercury-2-5-preview",
      "date": "2026-09-02",
      "event": "the OpenRouter change feed records the row's arrival",
      "source_url": "https://openrouter.ai/api/v1/models",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "model/inception-mercury-2-5-preview",
      "date": "2026-09-08",
      "event": "Inception releases Mercury 2.5 as a production model",
      "source_url": "https://www.inceptionlabs.ai/blog/introducing-mercury-2-5",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "model/inception-mercury-2-5-preview",
      "date": "2026-09-08",
      "event": "the OpenRouter row inception/mercury-2.5 is created, at 18:28:57 UTC by the row's created field",
      "source_url": "https://openrouter.ai/api/v1/models",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "model/inception-mercury-2-5-preview",
      "date": "2026-09-09",
      "event": "the preview row is gone from the OpenRouter catalog, and the same fetch records the arrival of inception/mercury-2.5",
      "source_url": "https://openrouter.ai/api/v1/models",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "model/inception-mercury-2-5",
      "date": "2026-09-09",
      "event": "arrived",
      "source_url": "https://openrouter.ai/api/v1/models",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "model/inclusionai-ling-3-0-flash-vl-free",
      "date": "2026-09-11",
      "event": "arrived",
      "source_url": "https://openrouter.ai/api/v1/models",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "model/inclusionai-ling-3-0-flash-vl",
      "date": "2026-09-11",
      "event": "arrived",
      "source_url": "https://openrouter.ai/api/v1/models",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "model/inference-net-schematron-v2-small",
      "date": "2026-09-12",
      "event": "arrived",
      "source_url": "https://openrouter.ai/api/v1/models",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "model/inference-net-schematron-v2-turbo",
      "date": "2026-09-12",
      "event": "arrived",
      "source_url": "https://openrouter.ai/api/v1/models",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "model/kwaipilot-kat-coder-air-v2-5",
      "date": "2026-09-01",
      "event": "removed from the OpenRouter model catalog; last listed in the 2026-08-31 snapshot",
      "source_url": "https://openrouter.ai/api/v1/models",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "model/meta-muse-glimmer-30b",
      "date": "2026-08-10",
      "event": "released under the Apache License 2.0, distilled from Muse Spark and built for agentic tasks on consumer hardware",
      "source_url": "https://huggingface.co/meta-models/Muse-Glimmer-30B",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "model/meta-muse-spark-1-3-contributor",
      "date": "2026-09-02",
      "event": "released alongside the standard Muse Spark 1.3 row at the contributor price",
      "source_url": "https://openrouter.ai/meta/muse-spark-1.3-contributor",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "model/meta-muse-spark-1-3",
      "date": "2026-09-02",
      "event": "released; the flagship card still lists 'Open weights · Meta license (weights pending)' with weights not released — a listing first seen on the Muse Spark 1.2 card",
      "source_url": "https://llm-releases.com/models/muse-spark-1-3",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "model/mistralai-codestral-2508-batch",
      "date": "2026-09-01",
      "event": "removed from the OpenRouter model catalog; last listed in the 2026-08-31 snapshot",
      "source_url": "https://openrouter.ai/api/v1/models",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "model/mistralai-codestral-2508-batch",
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      "source_url": "https://openrouter.ai/bytedance-seed/seed-1.6",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/bytedance-seed",
      "date": "2026-06-19",
      "event": "Seed-2.1-Preview released on Arena",
      "source_url": "https://seed.bytedance.com/en/blog",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/bytedance-seed",
      "date": "2026-06-23",
      "event": "Seed2.1 released in two sizes, Pro and Turbo",
      "source_url": "https://seed.bytedance.com/en/blog/seed2-1-officially-released-advancing-ai-productivity",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/bytedance-seed",
      "date": "2026-08-12",
      "event": "bytedance-seed/seed-2-1-turbo listed on OpenRouter, seven weeks after the vendor's release; Seed-2.0-Code arrived the same day",
      "source_url": "https://openrouter.ai/bytedance-seed/seed-2-1-turbo",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/cohere",
      "date": "2025-03-13",
      "event": "Command A listed — an open-weights 111B model at the same list price as Command R+",
      "source_url": "https://openrouter.ai/cohere/command-a",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/cohere",
      "date": "2026-04-24",
      "event": "Cohere and Aleph Alpha announced a merger in which Cohere would acquire Aleph Alpha",
      "source_url": "https://en.wikipedia.org/wiki/Cohere",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/cohere",
      "date": "2026-06-17",
      "event": "North Mini Code listed — optimized for agentic software engineering and released open-weight under the Apache 2.0 license",
      "source_url": "https://openrouter.ai/cohere/north-mini-code:free",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/deepseek",
      "date": "2026-04-24",
      "event": "V4 series previewed",
      "source_url": "https://en.wikipedia.org/wiki/DeepSeek",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/deepseek",
      "date": "2026-07-31",
      "event": "DeepSeek-V4-Flash-0731 released with MIT-licensed weights on Hugging Face",
      "source_url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/deepseek",
      "date": "2026-08-13",
      "event": "V4-Pro released",
      "source_url": "https://en.wikipedia.org/wiki/DeepSeek",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/google-deepmind",
      "date": "2014-01-26",
      "event": "Google acquired DeepMind",
      "source_url": "https://en.wikipedia.org/wiki/Google_DeepMind",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/google-deepmind",
      "date": "2026-07-21",
      "event": "Gemini 3.6 Flash released",
      "source_url": "https://openrouter.ai/google/gemini-3.6-flash",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/google-deepmind",
      "date": "2026-08-13",
      "event": "Gemini 3.7 Flash released; introductory pricing declared to run through 2026-12-31",
      "source_url": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-3-7-flash/",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/google-deepmind",
      "date": "2026-09-02",
      "event": "Gemini 3.8 Flash released",
      "source_url": "https://openrouter.ai/google/gemini-3.8-flash",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/inception-labs",
      "date": "2026-03-04",
      "event": "Mercury 2 released",
      "source_url": "https://openrouter.ai/inception/mercury-2",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/inception-labs",
      "date": "2026-08-31",
      "event": "Mercury 2.5 Preview released",
      "source_url": "https://openrouter.ai/inception/mercury-2.5-preview",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/inception-labs",
      "date": "2026-09-02",
      "event": "the OpenRouter change feed records the Mercury 2.5 Preview row's arrival",
      "source_url": "https://openrouter.ai/api/v1/models",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/inception-labs",
      "date": "2026-09-03",
      "event": "the llm-releases feed records the Mercury 2.5 Preview row's arrival; it dates the release August 31",
      "source_url": "https://llm-releases.com/models/mercury-2-5-preview",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/inclusionai",
      "date": "2026-07-23",
      "event": "Ling-3.0-flash announced in a ten-tweet thread and listed on OpenRouter the same day; the thread names no licence and links no weights",
      "source_url": "https://x.com/AntLingAGI/status/2080351022028095681",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/inclusionai",
      "date": "2026-07-24",
      "event": "\"Open-source release coming soon—stay tuned\" — the first commitment to publish weights, a day after the launch",
      "source_url": "https://x.com/AntLingAGI/status/2080351022028095681",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/inclusionai",
      "date": "2026-08-02",
      "event": "inclusionAI/Ling-3.0-flash repository created on Hugging Face (createdAt), licensed mit rather than the Apache 2.0 a release tracker had recorded",
      "source_url": "https://huggingface.co/api/models/inclusionAI/Ling-3.0-flash",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/inclusionai",
      "date": "2026-08-04",
      "event": "AtomicChat/Ling-3.0-flash-GGUF created, the first widely downloaded quantisation of the weights",
      "source_url": "https://huggingface.co/api/models/AtomicChat/Ling-3.0-flash-GGUF",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/inclusionai",
      "date": "2026-08-10",
      "event": "inclusionAI/Ling-3.0-tiny published under MIT — weights first, and never listed on OpenRouter",
      "source_url": "https://huggingface.co/api/models/inclusionAI/Ling-3.0-tiny",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/inclusionai",
      "date": "2026-08-27",
      "event": "Ling 3.0 Flash Fin and its free tier arrive on OpenRouter, weights not yet posted",
      "source_url": "https://openrouter.ai/api/v1/models",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/inclusionai",
      "date": "2026-08-31",
      "event": "inclusionAI publishes its own GGUF conversion of Ling-3.0-flash, twenty-seven days after a community one",
      "source_url": "https://huggingface.co/api/models/inclusionAI/Ling-3.0-flash-GGUF",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/inclusionai",
      "date": "2026-09-03",
      "event": "inclusionAI/Ling-3.0-flash-Fin repository created, seven days after the API listing, under MIT",
      "source_url": "https://huggingface.co/api/models/inclusionAI/Ling-3.0-flash-Fin",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/inclusionai",
      "date": "2026-09-04",
      "event": "Ling 3.0 Flash Sante listed on OpenRouter as a free row; no corresponding Hugging Face repository exists",
      "source_url": "https://openrouter.ai/api/v1/models",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/meta-superintelligence-labs",
      "date": "2025-04-05",
      "event": "Llama 4 Maverick released under the Llama 4 Community License Agreement — the last frontier Llama release",
      "source_url": "https://huggingface.co/meta-llama/Llama-4-Maverick-17B-128E-Instruct",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/meta-superintelligence-labs",
      "date": "2026-04-08",
      "event": "Muse Spark introduced as Meta Superintelligence Labs' first model, with closed weights",
      "source_url": "https://about.fb.com/news/2026/04/introducing-muse-spark-meta-superintelligence-labs/",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/meta-superintelligence-labs",
      "date": "2026-07-09",
      "event": "Muse Spark 1.1 launched",
      "source_url": "https://en.wikipedia.org/wiki/Muse_Spark",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/meta-superintelligence-labs",
      "date": "2026-08-05",
      "event": "Muse Spark 1.2 released",
      "source_url": "https://en.wikipedia.org/wiki/Muse_Spark",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/meta-superintelligence-labs",
      "date": "2026-08-10",
      "event": "Muse Glimmer 30B released under the Apache License 2.0",
      "source_url": "https://huggingface.co/meta-models/Muse-Glimmer-30B",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/meta-superintelligence-labs",
      "date": "2026-09-02",
      "event": "Muse Spark 1.3 released; the flagship card's 'Open weights · Meta license (weights pending)' listing, unchanged since the 1.2 release, still shows weights not released",
      "source_url": "https://llm-releases.com/models/muse-spark-1-3",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "date": "2024-04-15",
      "event": "WizardLM-2 announced as three open models — 8x22B, 70B and 7B — on the project's own page",
      "source_url": "https://wizardlm.github.io/WizardLM2/",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "date": "2024-04-16",
      "event": "alpindale/WizardLM-2-8x22B created on Hugging Face (createdAt 02:36:59Z), the copy of the 8x22B weights that is still public today",
      "source_url": "https://huggingface.co/api/models/alpindale/WizardLM-2-8x22B",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "date": "2024-04-16",
      "event": "microsoft/wizardlm-2-8x22b appears on OpenRouter (row `created` 1713225600)",
      "source_url": "https://openrouter.ai/api/v1/models/microsoft/wizardlm-2-8x22b/endpoints",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "date": "2024-04-23",
      "event": "404 Media reports that Microsoft deleted WizardLM-2 hours after release over missed toxicity testing, and that copies had already spread",
      "source_url": "https://www.404media.co/microsoft-deleted-its-llm-because-it-didnt-get-a-safety-test-but-now-its-everywhere/",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "date": "2024-12-11",
      "event": "microsoft/phi-4 repository created on Hugging Face — initial commit 11:47:29Z, files uploaded six minutes later",
      "source_url": "https://huggingface.co/api/models/microsoft/phi-4/commits/main",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "date": "2025-01-08",
      "event": "\"Add Phi-4 Technical Report Link (#10)\" committed to the phi-4 repository",
      "source_url": "https://huggingface.co/api/models/microsoft/phi-4/commits/main",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "date": "2025-01-10",
      "event": "microsoft/phi-4 appears on OpenRouter (row `created` 1736489872)",
      "source_url": "https://openrouter.ai/api/v1/models/microsoft/phi-4/endpoints",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "date": "2026-06-02",
      "event": "Microsoft AI announces seven in-house MAI models and names OpenRouter as a distribution channel for them",
      "source_url": "https://microsoft.ai/news/building-a-hillclimbing-machine-launching-seven-new-mai-models/",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/microsoft",
      "date": "2026-07-14",
      "event": "Latest commit to the phi-4 repository, restoring a stop token in generation_config",
      "source_url": "https://huggingface.co/api/models/microsoft/phi-4/commits/main",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/minimax",
      "date": "2026-01-09",
      "event": "Initial public offering on the Hong Kong Stock Exchange",
      "source_url": "https://en.wikipedia.org/wiki/MiniMax_Group",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/minimax",
      "date": "2026-01-23",
      "event": "minimax/minimax-m2-her listed on OpenRouter — the role-play model behind Talkie and Xingye",
      "source_url": "https://openrouter.ai/minimax/minimax-m2-her",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/minimax",
      "date": "2026-01-27",
      "event": "MiniMax publishes its Role-Play Bench write-up for MiniMax-M2-her",
      "source_url": "https://www.minimax.io/news/a-deep-dive-into-the-minimax-m2-her-2",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/minimax",
      "date": "2026-05-31",
      "event": "minimax/minimax-m3 listed on OpenRouter",
      "source_url": "https://openrouter.ai/minimax/minimax-m3",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/minimax",
      "date": "2026-08-02",
      "event": "The MiniMax H3 community licence takes effect, excluding the European Union, the United Kingdom, the Republic of Korea and the United States from its grant",
      "source_url": "https://huggingface.co/MiniMaxAI/MiniMax-H3/raw/main/LICENSE",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/mistral-ai",
      "date": "2025-12-01",
      "event": "Mistral Large 3 listed as the company's most capable model, under the Apache License 2.0",
      "source_url": "https://openrouter.ai/mistralai/mistral-large-2512",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/mistral-ai",
      "date": "2026-04-30",
      "event": "Mistral Medium 3.5 listed — the newest Mistral row in the 28 August 2026 catalog snapshot",
      "source_url": "https://openrouter.ai/mistralai/mistral-medium-3-5",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/mistral-ai",
      "date": "2026-05-19",
      "event": "acquisition of the Austrian industrial-simulation firm Emmi AI announced",
      "source_url": "https://en.wikipedia.org/wiki/Mistral_AI",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/moonshot-ai",
      "date": "2026-01-27",
      "event": "Kimi K2.5 released with published weights under a Modified MIT License",
      "source_url": "https://huggingface.co/moonshotai/Kimi-K2.5",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/moonshot-ai",
      "date": "2026-07-16",
      "event": "Kimi K3 listed — 2.8T parameters, open weights under the bespoke Kimi K3 License",
      "source_url": "https://openrouter.ai/moonshotai/kimi-k3",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/moonshot-ai",
      "date": "2026-08-31",
      "event": "Kimi K2.5 and the moonshot-v1 series sunset on Moonshot's own platform",
      "source_url": "https://platform.kimi.ai/docs/models",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/nous-research",
      "date": "2024-08-16",
      "event": "Hermes 3 405B Instruct listed on OpenRouter",
      "source_url": "https://openrouter.ai/nousresearch/hermes-3-llama-3.1-405b",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/nous-research",
      "date": "2024-08-18",
      "event": "Hermes 3 70B Instruct listed on OpenRouter",
      "source_url": "https://openrouter.ai/nousresearch/hermes-3-llama-3.1-70b",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/nous-research",
      "date": "2025-05-14",
      "event": "The Psyche network architecture published — training distributed over the open internet, coordinated by Solana smart contracts",
      "source_url": "https://nousresearch.com/nous-psyche",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/nous-research",
      "date": "2025-08-25",
      "event": "Hermes 4 Technical Report submitted to arXiv (v1), nine named authors",
      "source_url": "https://arxiv.org/abs/2508.18255v1",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/nous-research",
      "date": "2025-08-26",
      "event": "Hermes 4 405B and Hermes 4 70B listed on OpenRouter — the most recent Nous models the router has listed",
      "source_url": "https://openrouter.ai/nousresearch/hermes-4-405b",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/nous-research",
      "date": "2025-08-30",
      "event": "Hermes-4-14B repository created on Hugging Face (createdAt), on a Qwen3-14B base — the first Hermes 4 variant not built on a Llama",
      "source_url": "https://huggingface.co/api/models/NousResearch/Hermes-4-14B",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/nous-research",
      "date": "2025-11-17",
      "event": "Hermes-4.3-36B repository created on Hugging Face (createdAt), on ByteDance's Seed-OSS-36B-Base",
      "source_url": "https://huggingface.co/api/models/NousResearch/Hermes-4.3-36B",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/nous-research",
      "date": "2025-12-07",
      "event": "nomos-1 repository created on Hugging Face (createdAt), a Qwen3-30B-A3B-Thinking-2507 specialisation for mathematical proof-writing",
      "source_url": "https://huggingface.co/api/models/NousResearch/nomos-1",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/nvidia",
      "date": "2025-12-15",
      "event": "Nemotron 3 family announced — Nano, Super and Ultra, on a hybrid mixture-of-experts architecture",
      "source_url": "https://en.wikipedia.org/wiki/Nvidia",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/nvidia",
      "date": "2026-06-04",
      "event": "Nemotron 3 Ultra listed, with free and batch rows alongside the standard one",
      "source_url": "https://openrouter.ai/nvidia/nemotron-3-ultra-550b-a55b",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/nvidia",
      "date": "2026-08-11",
      "event": "Nemotron 3.5 Lightning listed, its free row advertising a larger context window than the paid row",
      "source_url": "https://openrouter.ai/nvidia/nemotron-3.5-lightning:free",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/openai",
      "date": "2025-10-28",
      "event": "adopted the public benefit corporation structure after approval by the California and Delaware attorneys general",
      "source_url": "https://en.wikipedia.org/wiki/OpenAI",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/openai",
      "date": "2026-06-26",
      "event": "GPT-5.6 previewed to roughly 20 organisations at the US government's request under the June 2026 frontier-model executive order",
      "source_url": "https://venturebeat.com/technology/openai-unveils-gpt-5-6-sol-terra-and-luna-models-but-only-accessible-to-limited-preview-partners-for-now-per-us-gov",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/openai",
      "date": "2026-07-09",
      "event": "GPT-5.6 Sol, Terra and Luna became generally available, including in GitHub Copilot",
      "source_url": "https://github.blog/changelog/2026-07-09-openais-gpt-5-6-sol-terra-and-luna-are-now-available-in-github-copilot/",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/perplexity",
      "date": "2025-10-30",
      "event": "Sonar Pro Search listed as exclusive to the OpenRouter API — the newest Perplexity row in the 28 August 2026 snapshot",
      "source_url": "https://openrouter.ai/perplexity/sonar-pro-search",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/poolside",
      "date": "2024-12-04",
      "event": "Poolside announces a first-party partnership with AWS",
      "source_url": "https://poolside.ai/blog/unveiling-our-partnership-with-aws",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/poolside",
      "date": "2025-11-18",
      "event": "Poolside acquires Fern Labs, a London company behind the Bridge multi-agent orchestration layer",
      "source_url": "https://poolside.ai/blog/fern-labs-acquisition",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/poolside",
      "date": "2026-04-23",
      "event": "poolside/Laguna-XS.2 repository created on Hugging Face (createdAt), licensed apache-2.0",
      "source_url": "https://huggingface.co/api/models/poolside/Laguna-XS.2",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/poolside",
      "date": "2026-04-28",
      "event": "Laguna XS.2 and Laguna M.1 announced — Poolside's first models shipped in public",
      "source_url": "https://poolside.ai/blog/introducing-laguna-xs2-m1",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/poolside",
      "date": "2026-05-05",
      "event": "The Poolside Platform announced, for teams that will not send data outside their own security boundary",
      "source_url": "https://poolside.ai/blog/introducing-the-poolside-platform",
      "license": "CC BY 4.0"
    },
    {
      "entry_id": "org/poolside",
      "date": "2026-06-15",
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      "slug": "learned-weather-forecasts",
      "title": "A forecast that was learned, not simulated",
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      "impossible_date": "2023-11-14",
      "impossible_what": "GraphCast, a research model, beats the industry gold-standard physics forecast on more than 90% of tested variables.",
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      "routine_what": "Qwen3.8-Max posts 86.1% on OSWorld-Verified, the repaired 2025 revision of the benchmark, with competing agents from two other labs already above 83%.",
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      "slug": "video-with-sound",
      "title": "Video with sound from a sentence",
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      "impossible_what": "Google publishes Imagen Video and states it has decided not to release the model or its source code.",
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      "slug": "resolving-github-issues",
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      "routine_what": "A model sold by the million tokens reports 62.3% on the same benchmark's human-verified subset.",
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      "impossible_what": "The MATH paper concludes that scaling to 40% accuracy would need around 10^35 parameters, which it calls impractical.",
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      "routine_what": "DeepSeek-R1, its weights free to download, reports 71.5% on GPQA Diamond, the benchmark's hardest split.",
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      "impossible_what": "OpenAI withholds the full GPT-2, publishing a much smaller version and keeping back the datasets and training code.",
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      "impossible_source_url": "https://slate.com/technology/2019/02/openai-gpt2-text-generating-algorithm-ai-dangerous.html",
      "routine_date": "2024-07-23",
      "routine_what": "Meta publishes Llama 3.1's weights for anyone to download, customise and fine-tune.",
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      "routine_source_url": "https://ai.meta.com/blog/meta-llama-3-1/",
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      "slug": "context-window",
      "title": "How much a model reads at once",
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      "impossible_what": "GPT-3 is published with a fixed context window of 2,048 tokens for every model size.",
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      "impossible_source_url": "https://ar5iv.labs.arxiv.org/html/2005.14165",
      "routine_date": "2024-06-27",
      "routine_what": "Google opens a two-million-token context window to every developer on its public API.",
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      "url": "/impossible-routine/context-window",
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      "slug": "model-on-a-phone",
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      "capability": "Running a genuinely capable language model on a phone, offline.",
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      "impossible_what": "GPT-3, served from a datacenter at 175 billion parameters, scores 43.9% on the new 57-subject MMLU exam.",
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      "impossible_source_url": "https://arxiv.org/html/2009.03300",
      "routine_date": "2024-04-22",
      "routine_what": "Phi-3-mini, quantized to 1.8 GB, runs fully offline on an iPhone 14 at more than 12 tokens per second and scores 69% on MMLU.",
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      "routine_source_url": "https://arxiv.org/html/2404.14219v4",
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      "url": "/impossible-routine/model-on-a-phone",
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      "slug": "generating-songs",
      "title": "A whole song, generated",
      "capability": "Generating a complete song, music and singing, as audio.",
      "impossible_date": "2020-04-30",
      "impossible_what": "OpenAI's Jukebox generates music with singing in the raw audio domain, at about nine hours of sampling per minute of song.",
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      "impossible_source_url": "https://ar5iv.labs.arxiv.org/html/2005.00341",
      "routine_date": "2024-03-21",
      "routine_what": "Suno's v3 model makes full two-minute songs in seconds and is available to all users.",
      "routine_metric": "a two-minute song in seconds",
      "routine_source_url": "https://suno.com/blog/v3",
      "span": "3 years, 10 months",
      "url": "/impossible-routine/generating-songs",
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      "slug": "fine-tuning-on-one-gpu",
      "title": "Fine-tuning on one GPU",
      "capability": "Fine-tuning a large language model into your own specialized version on a single machine.",
      "impossible_date": "2021-06-17",
      "impossible_what": "Microsoft researchers measure full fine-tuning of GPT-3 at 1.2 TB of GPU memory and call deploying independent fine-tuned copies prohibitively expensive.",
      "impossible_metric": "1.2 TB of GPU memory",
      "impossible_source_url": "https://arxiv.org/html/2106.09685v2",
      "routine_date": "2023-05-23",
      "routine_what": "QLoRA fine-tunes a 65-billion-parameter model on a single 48 GB GPU in 24 hours, reaching 99.3% of ChatGPT's level on the Vicuna benchmark.",
      "routine_metric": "one 48 GB GPU, 24 hours",
      "routine_source_url": "https://arxiv.org/abs/2305.14314",
      "span": "1 year, 11 months",
      "url": "/impossible-routine/fine-tuning-on-one-gpu",
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      "slug": "human-parity-speech-recognition",
      "title": "Transcription at human accuracy",
      "capability": "Transcribing ordinary recorded conversation about as accurately as a person does.",
      "impossible_date": "2016-10-18",
      "impossible_what": "Microsoft researchers report the first human-parity error rate on the standard Switchboard task.",
      "impossible_metric": "5.9% word error rate",
      "impossible_source_url": "https://blogs.microsoft.com/blog/2016/10/18/historic-milestone-microsoft-researchers-achieve-human-parity-conversational-speech-recognition/",
      "routine_date": "2022-12-06",
      "routine_what": "OpenAI releases Whisper's models and inference code, reporting accuracy and robustness approaching human transcribers.",
      "routine_metric": "trained on 680,000 hours of audio; weights public",
      "routine_source_url": "https://arxiv.org/abs/2212.04356",
      "span": "6 years, 1 month",
      "url": "/impossible-routine/human-parity-speech-recognition",
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    {
      "slug": "text-to-image",
      "title": "An image from a sentence",
      "capability": "Producing an original image from a written description of it.",
      "impossible_date": "2021-02-24",
      "impossible_what": "DALL-E is published as a research paper: a transformer trained to generate images from text captions.",
      "impossible_metric": "",
      "impossible_source_url": "https://arxiv.org/abs/2102.12092",
      "routine_date": "2022-08-22",
      "routine_what": "Stable Diffusion's weights are published for anyone to download and run on one consumer graphics card.",
      "routine_metric": "6.9 GB of VRAM",
      "routine_source_url": "https://stability.ai/news-updates/stable-diffusion-public-release",
      "span": "1 year, 5 months",
      "url": "/impossible-routine/text-to-image",
      "license": "CC BY 4.0"
    },
    {
      "slug": "protein-structure-prediction",
      "title": "The shape of a protein",
      "capability": "Getting the three-dimensional shape a protein folds into from its sequence of amino acids.",
      "impossible_date": "2020-11-30",
      "impossible_what": "AlphaFold's CASP14 result answers what the assessors had called a 50-year grand challenge in biology.",
      "impossible_metric": "median 92.4 GDT across all targets",
      "impossible_source_url": "https://deepmind.google/discover/blog/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology/",
      "routine_date": "2022-07-28",
      "routine_what": "Predicted structures for over 200 million proteins are free to search and to bulk-download.",
      "routine_metric": "200 million structures, no cost",
      "routine_source_url": "https://deepmind.google/discover/blog/alphafold-reveals-the-structure-of-the-protein-universe/",
      "span": "1 year, 7 months",
      "url": "/impossible-routine/protein-structure-prediction",
      "license": "CC BY 4.0"
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      "slug": "translation-between-languages",
      "title": "Translation at human accuracy",
      "capability": "Translating news text from one language into another as accurately as a human translator.",
      "impossible_date": "2018-03-14",
      "impossible_what": "Microsoft reports human parity on Chinese-to-English news translation, for one language pair on one test set.",
      "impossible_metric": "newstest2017, judged by bilingual evaluators",
      "impossible_source_url": "https://www.microsoft.com/en-us/translator/blog/2018/03/14/human-parity-for-chinese-to-english-translations/",
      "routine_date": "2022-07-11",
      "routine_what": "Meta open-sources NLLB-200, one model translating 200 languages, with its benchmark and training code.",
      "routine_metric": "200 languages; 44% better BLEU than the previous state of the art",
      "routine_source_url": "https://arxiv.org/abs/2207.04672",
      "span": "4 years, 3 months",
      "url": "/impossible-routine/translation-between-languages",
      "license": "CC BY 4.0"
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      "slug": "driverless-rides",
      "title": "A car with nobody in it",
      "capability": "Riding in a car on public roads with no human driver aboard.",
      "impossible_date": "2004-03-13",
      "impossible_what": "No vehicle finishes DARPA's Grand Challenge; the top-scoring one travels 7.5 miles of the course.",
      "impossible_metric": "7.5 miles of a 142-mile course",
      "impossible_source_url": "https://www.darpa.mil/news/2014/grand-challenge-ten-years-later",
      "routine_date": "2020-10-08",
      "routine_what": "Waymo opens its fully driverless service to the general public in metro Phoenix.",
      "routine_metric": "service area larger than San Francisco",
      "routine_source_url": "https://waymo.com/blog/2020/10/waymo-is-opening-its-fully-driverless-service-in-phoenix/",
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      "url": "/impossible-routine/driverless-rides",
      "license": "CC BY 4.0"
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    {
      "slug": "beating-poker-professionals",
      "title": "No-limit poker",
      "capability": "Beating professional players at no-limit Texas hold'em poker.",
      "impossible_date": "2017-01-31",
      "impossible_what": "Libratus beats four heads-up specialists by $1,766,250 in chips over 120,000 hands, drawing on about 600 nodes of the Bridges supercomputer.",
      "impossible_metric": "roughly 600 of Bridges' 846 compute nodes",
      "impossible_source_url": "https://www.cmu.edu/news/stories/archives/2017/january/AI-beats-poker-pros.html",
      "routine_date": "2019-07-11",
      "routine_what": "Pluribus beats professionals at six-player no-limit hold'em after computing its strategy in eight days and 12,400 core hours, playing live on 28 cores.",
      "routine_metric": "12,400 core hours to train; 28 cores in live play",
      "routine_source_url": "https://www.cmu.edu/news/stories/archives/2019/july/cmu-facebook-ai-beats-poker-pros.html",
      "span": "2 years, 5 months",
      "url": "/impossible-routine/beating-poker-professionals",
      "license": "CC BY 4.0"
    },
    {
      "slug": "training-imagenet",
      "title": "Training ImageNet",
      "capability": "Training an image classifier to state-of-the-art accuracy on ImageNet.",
      "impossible_date": "2012-12-03",
      "impossible_what": "AlexNet wins ILSVRC-2012 with a 15.3% top-5 test error, against 26.2% for the runner-up, after five to six days of training on two GTX 580 GPUs.",
      "impossible_metric": "five to six days on two consumer GPUs",
      "impossible_source_url": "https://papers.nips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf",
      "routine_date": "2018-08-10",
      "routine_what": "fast.ai trains ImageNet to 93% accuracy in 18 minutes on 16 rented public cloud instances, for about $40.",
      "routine_metric": "18 minutes, about $40",
      "routine_source_url": "https://www.fast.ai/posts/2018-08-10-fastai-diu-imagenet.html",
      "span": "5 years, 8 months",
      "url": "/impossible-routine/training-imagenet",
      "license": "CC BY 4.0"
    },
    {
      "slug": "professional-level-go",
      "title": "Professional-level Go",
      "capability": "Playing Go at the level of professional players.",
      "impossible_date": "2016-01-27",
      "impossible_what": "AlphaGo beats the European champion 5–0, a feat its Nature paper says was previously thought to be at least a decade away.",
      "impossible_metric": "5–0; first program to beat a professional at full-size Go",
      "impossible_source_url": "https://www.nature.com/articles/nature16961",
      "routine_date": "2018-05-02",
      "routine_what": "Facebook releases ELF OpenGo's trained model and code under a BSD license, after a 14–0 record against four top-30 professionals playing on a single GPU.",
      "routine_metric": "14–0 versus top-30 professionals; one GPU",
      "routine_source_url": "https://research.facebook.com/blog/2018/05/facebook-open-sources-elf-opengo/",
      "span": "2 years, 3 months",
      "url": "/impossible-routine/professional-level-go",
      "license": "CC BY 4.0"
    },
    {
      "slug": "diabetic-eye-screening",
      "title": "Diabetic eye screening",
      "capability": "Detecting diabetic eye disease from a photograph of the retina.",
      "impossible_date": "2016-11-29",
      "impossible_what": "A deep network detects diabetic retinopathy in retinal photographs on par with the ophthalmologists it was measured against, in a JAMA paper.",
      "impossible_metric": "F-score 0.95 versus a median 0.91 for eight ophthalmologists",
      "impossible_source_url": "https://research.google/blog/deep-learning-for-detection-of-diabetic-eye-disease/",
      "routine_date": "2018-04-11",
      "routine_what": "The FDA permits marketing of IDx-DR, the first device authorized to use AI autonomously to detect a medical condition — its results do not require review by a specialized clinician.",
      "routine_metric": "first autonomous AI diagnostic authorized by the FDA",
      "routine_source_url": "https://www.jonesday.com/en/insights/2018/05/fda-permits-marketing-of-first-autonomous-artifici",
      "span": "1 year, 4 months",
      "url": "/impossible-routine/diabetic-eye-screening",
      "license": "CC BY 4.0"
    },
    {
      "slug": "raw-audio-speech-synthesis",
      "title": "Speech built one sample at a time",
      "capability": "Generating human-sounding speech directly as a raw audio waveform.",
      "impossible_date": "2016-09-08",
      "impossible_what": "DeepMind publishes WaveNet and calls building up samples one step at a time computationally expensive.",
      "impossible_metric": "",
      "impossible_source_url": "https://deepmind.google/discover/blog/wavenet-a-generative-model-for-raw-audio/",
      "routine_date": "2017-10-04",
      "routine_what": "The production version generates the Google Assistant voices for US English and Japanese on all platforms.",
      "routine_metric": "50 ms of compute per second of speech, 1,000x faster than the research model",
      "routine_source_url": "https://deepmind.google/discover/blog/wavenet-launches-in-the-google-assistant/",
      "span": "1 year",
      "url": "/impossible-routine/raw-audio-speech-synthesis",
      "license": "CC BY 4.0"
    },
    {
      "slug": "bird-in-the-photo",
      "title": "Which bird is that",
      "capability": "Naming the species of bird in a photograph.",
      "impossible_date": "2014-09-24",
      "impossible_what": "xkcd uses checking whether a photo is of a bird as its example of a task needing a research team and five years.",
      "impossible_metric": "",
      "impossible_source_url": "https://xkcd.com/1425/",
      "routine_date": "2015-06-05",
      "routine_what": "Cornell's free Merlin Bird Photo ID names the species in an uploaded photo.",
      "routine_metric": "400 species, free",
      "routine_source_url": "https://news.cornell.edu/stories/2015/06/tweet-upload-your-bird-photos-and-merlin-ids-species",
      "span": "8 months",
      "url": "/impossible-routine/bird-in-the-photo",
      "license": "CC BY 4.0"
    },
    {
      "slug": "reading-handwriting",
      "title": "Reading handwriting",
      "capability": "Reading handwriting by machine.",
      "impossible_date": "1989-11-27",
      "impossible_what": "A back-propagation network reads handwritten zip code digits supplied by the U.S. Postal Service, with a 1% error rate and a 9% reject rate.",
      "impossible_metric": "1% error, 9% reject, isolated digits",
      "impossible_source_url": "https://proceedings.neurips.cc/paper/1989/hash/53c3bce66e43be4f209556518c2fcb54-Abstract.html",
      "routine_date": "2015-04-15",
      "routine_what": "Google ships Handwriting Input for Android, reading printed and cursive writing in 82 languages, with or without an internet connection.",
      "routine_metric": "82 languages, 20 scripts",
      "routine_source_url": "https://research.google/blog/google-handwriting-input-in-82-languages-on-your-android-mobile-device/",
      "span": "25 years, 4 months",
      "url": "/impossible-routine/reading-handwriting",
      "license": "CC BY 4.0"
    },
    {
      "slug": "grandmaster-chess",
      "title": "Grandmaster chess",
      "capability": "Playing chess above the strongest human players.",
      "impossible_date": "1997-05-11",
      "impossible_what": "IBM's Deep Blue beats world champion Garry Kasparov 3.5–2.5 in their six-game rematch in New York.",
      "impossible_metric": "3.5–2.5 over six games",
      "impossible_source_url": "https://theweekinchess.com/html/twic131.html",
      "routine_date": "2009-08-27",
      "routine_what": "Pocket Fritz 4 scores 9.5/10 at the Mercosur Cup in Buenos Aires running on a handheld Pocket PC, an Elo performance of 2938 that included outplaying a 2522 grandmaster.",
      "routine_metric": "9.5/10; Elo performance 2938",
      "routine_source_url": "https://en.chessbase.com/post/breakthrough-performance-by-pocket-fritz-4-in-buenos-aires",
      "span": "12 years, 3 months",
      "url": "/impossible-routine/grandmaster-chess",
      "license": "CC BY 4.0"
    }
  ]
}
