The internal pain signal in the large language models tested in a study co-authored by Cameron Berg was distinct from fear and generic negative valence, firing specifically for harm directed at the model rather than the user

Overview Debates Related claims Topics Sources

441e85161Claim

On October 23, 2026, Beijing-based Moonshot AI released Kimi K3.1 Pro, the first model in its K3.1 series, featuring a 1-million-token context window

441e85161Claim

Global publishers are increasingly challenging artificial intelligence developers using copyrighted material without permission for large language model training under fair-use arguments

441e85161Claim

Kimi K3.1 Pro is a 2.8-trillion total parameter Mixture-of-Experts model that activates 104 billion parameters across 16 of 896 experts using a Stable LatentMoE framework

441e85161Claim

Moonshot AI released Kimi K3.1 Pro Highspeed alongside Kimi K3.1 Pro, which runs two to three times faster according to official company statements

441e85161Claim

Kimi K3.1 Pro builds on the base K3 architecture utilizing Kimi Delta Attention and Attention Residuals, trained with joint reinforcement learning across hundreds of downstream domains

441e85161Claim

Chinese AI company MiniMax released MiniMax M3.1 Flash on October 19, 2026, featuring 428 billion total parameters, about 23 billion active parameters in a mixture-of-experts architecture, and a 1,000,000-token context window.

441e85161Claim

MiniMax reported that MiniMax M3.1 Flash operates about three times faster than MiniMax M3, with median output speeds consistently exceeding 170 tokens per second.

441e85161Claim

MiniMax M3.1 Flash API pricing is set at $0.15 per million input tokens and $0.60 per million output tokens, with cached input reads priced at $0.015 per million tokens.

441e85161Claim

MiniMax confirmed that an earlier build of MiniMax M3.1 Flash operated anonymously under the name Space Bunny, processing over 200 trillion tokens prior to its official launch.

441e85161Claim

MiniMax reported that MiniMax M3.1 Flash can reach peak generation speeds exceeding 200 tokens per second.

441e85161Claim

LiteLLM, developed by BerriAI, provides a shared interface for LLM providers including OpenAI, Anthropic, and Google Gemini, receiving approximately 23 million weekly downloads.

441e85161Claim

The complaints allege Meta Platforms Inc. operated a book-scanning program called Project Vellum starting around mid-2025 that purchased and scanned physical books to train its Muse Spark and Muse Glimmer AI models.

441e85161Claim

The authors' complaints allege Meta Platforms Inc. downloaded e-books from pirate shadow libraries, including LibGen, Z-Library, Anna's Archive, and the Books3 dataset, to train its large language models.

441e85161Claim

Artex was developed by Chinese engineer Li Puhua in July 2026 as a penetration testing tool that automates security assessments using large language models.

441e85161Claim

Wiz researchers noted that while AI helped identify the ancient PHP vulnerabilities CVE-2026-109618 and CVE-2026-109619, large language models also lower the barrier for attackers to adapt exploits across different server configurations

441e85161Claim

Chinese AI startup DeepSeek trained the debut version of its artificial intelligence model for approximately $6 million, compared to roughly $100 million spent by OpenAI to train GPT-4

441e85161Claim

A hacking campaign by a Chinese-speaking hacker deployed autonomous AI agents using a mixture of DeepSeek and Anthropic's Claude Code models

441e85161Claim

On October 17, 2026, DeepSeek released DeepSeek-V4.1-Flash-1017, an updated checkpoint of its V4.1-Flash artificial intelligence model accessible via DeepSeek's website, mobile app, and API

441e85161Claim

DeepSeek-V4.1-Flash-1017 completes tasks faster and produces shorter outputs than the earlier September 2026 release of DeepSeek-V4.1-Flash

441e85161Claim

On October 23, 2026, Beijing-based Moonshot AI released Kimi K3.1 Pro, the first model in its K3.1 series, featuring a 1-million-token context window

Global publishers are increasingly challenging artificial intelligence developers using copyrighted material without permission for large language model training under fair-use arguments

Kimi K3.1 Pro is a 2.8-trillion total parameter Mixture-of-Experts model that activates 104 billion parameters across 16 of 896 experts using a Stable LatentMoE framework

Moonshot AI released Kimi K3.1 Pro Highspeed alongside Kimi K3.1 Pro, which runs two to three times faster according to official company statements

Kimi K3.1 Pro builds on the base K3 architecture utilizing Kimi Delta Attention and Attention Residuals, trained with joint reinforcement learning across hundreds of downstream domains

Chinese AI company MiniMax released MiniMax M3.1 Flash on October 19, 2026, featuring 428 billion total parameters, about 23 billion active parameters in a mixture-of-experts architecture, and a 1,000,000-token context window.

MiniMax reported that MiniMax M3.1 Flash operates about three times faster than MiniMax M3, with median output speeds consistently exceeding 170 tokens per second.

MiniMax M3.1 Flash API pricing is set at $0.15 per million input tokens and $0.60 per million output tokens, with cached input reads priced at $0.015 per million tokens.

MiniMax confirmed that an earlier build of MiniMax M3.1 Flash operated anonymously under the name Space Bunny, processing over 200 trillion tokens prior to its official launch.

MiniMax reported that MiniMax M3.1 Flash can reach peak generation speeds exceeding 200 tokens per second.

LiteLLM, developed by BerriAI, provides a shared interface for LLM providers including OpenAI, Anthropic, and Google Gemini, receiving approximately 23 million weekly downloads.

The complaints allege Meta Platforms Inc. operated a book-scanning program called Project Vellum starting around mid-2025 that purchased and scanned physical books to train its Muse Spark and Muse Glimmer AI models.

The authors' complaints allege Meta Platforms Inc. downloaded e-books from pirate shadow libraries, including LibGen, Z-Library, Anna's Archive, and the Books3 dataset, to train its large language models.

Artex was developed by Chinese engineer Li Puhua in July 2026 as a penetration testing tool that automates security assessments using large language models.

Wiz researchers noted that while AI helped identify the ancient PHP vulnerabilities CVE-2026-109618 and CVE-2026-109619, large language models also lower the barrier for attackers to adapt exploits across different server configurations

Chinese AI startup DeepSeek trained the debut version of its artificial intelligence model for approximately $6 million, compared to roughly $100 million spent by OpenAI to train GPT-4

A hacking campaign by a Chinese-speaking hacker deployed autonomous AI agents using a mixture of DeepSeek and Anthropic's Claude Code models

On October 17, 2026, DeepSeek released DeepSeek-V4.1-Flash-1017, an updated checkpoint of its V4.1-Flash artificial intelligence model accessible via DeepSeek's website, mobile app, and API

DeepSeek-V4.1-Flash-1017 completes tasks faster and produces shorter outputs than the earlier September 2026 release of DeepSeek-V4.1-Flash

DeepSeek-V4.1-Flash incorporates Compressed Sparse Attention 2 and an FP4 global key-value cache that reduces memory footprint to 890 bytes per token