Claude's new constitution
Anthropic
Anthropic replaces standalone principles with a detailed rationale meant to help Claude apply broad values in unfamiliar situations.
Open library
The library
The essential reading of generative AI and agents, open in a clean reading view, with Meow and Standard summaries when available. No account needed to read.
563 papers · 561 with a Meow summary · 333 posts
Key publications from Anthropic, OpenAI, Google DeepMind, Meta, DeepSeek, Qwen, Z.ai, Kimi and other labs, plus the most influential essays and blogs.
Mechanistic Interpretability, Variables, and the Importance of Interpretable Bases
Anthropic
Olah argues that mechanistic interpretability hinges on finding independently understandable units in activations—the neural-network equivalent of meaningful program variables.
AlphaGo Zero: Starting from scratch
David Silver, Demis Hassabis · Google DeepMind
AlphaGo Zero started from self-play instead of human game records, quickly surpassed human-level play, and beat the previously published champion-defeating AlphaGo 100 games to 0.
MuZero: Mastering Go, chess, shogi and Atari without rules
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy Lillicrap, David Silver · Google DeepMind
MuZero learned to plan in Go, chess, shogi, and Atari without being given their rules, by modeling only what helps its decisions.
AlphaFold: a solution to a 50-year-old grand challenge in biology
Google DeepMind
CASP recognized AlphaFold as a solution to protein folding, a long-standing challenge; its promise is faster structure insight where experiments are slow and costly.
A Generalist Agent
Google DeepMind
Gato uses one set of weights across games, image captioning, chat, and robot control, with context guiding what it outputs.
AlphaDev discovers faster sorting algorithms
Daniel J. Mankowitz and Andrea Michi · Google DeepMind
AlphaDev searches CPU instructions for faster algorithms instead of merely refining familiar code—a promising approach when small routines are called again and again.
Discovering novel algorithms with AlphaTensor
Alhussein Fawzi, Matej Balog, Bernardino Romera-Paredes, Demis Hassabis, Pushmeet Kohli · Google DeepMind
AlphaTensor turns algorithm discovery into a game: zeroing its tensor yields a provably correct matrix-multiplication algorithm.
FunSearch: Making new discoveries in mathematical sciences using Large Language Models
Alhussein Fawzi and Bernardino Romera Paredes · Google DeepMind
FunSearch turns LLM proposals into evaluated code search: an automatic evaluator, not the model’s fluency, determines which ideas advance.
AlphaGeometry: An Olympiad-level AI system for geometry
Trieu Trinh and Thang Luong · Google DeepMind
AlphaGeometry turns learned suggestions for added geometric constructions into formal, checkable proofs.
Advanced version of Gemini with Deep Think officially achieves gold-medal standard at the International Mathematical Olympiad
Thang Luong and Edward Lockhart · Google DeepMind
Gemini Deep Think reached gold-medal-level performance by producing rigorous proofs directly from natural-language problems, within the IMO’s 4.5-hour limit.
AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms
AlphaEvolve team · Google DeepMind
AlphaEvolve shifts LLM-assisted discovery from generating code to repeatedly testing and evolving programs; Google reports production computing uses and new mathematical results.
RT-2: New model translates vision and language into action
Yevgen Chebotar, Tianhe Yu · Google DeepMind
RT-2 turns a web-trained vision-language model into a robot controller by teaching it to output language-like tokens that represent actions.
Taking a responsible path to AGI
Anca Dragan, Rohin Shah, Four Flynn and Shane Legg · Google DeepMind
Google DeepMind presents AGI safety as a readiness effort, not a finished guarantee: assess four risk areas, focus deeply on misuse and misalignment, and build technical and institutional safeguards while open problems remain.
Introducing the Frontier Safety Framework
Anca Dragan, Helen King and Allan Dafoe · Google DeepMind
The framework turns concern about future AI harms into a process: define capability thresholds, test for approaching them, then apply safeguards.
Specification gaming: the flip side of AI ingenuity
Victoria Krakovna, Jonathan Uesato, Vladimir Mikulik, Matthew Rahtz, Tom Everitt, Ramana Kumar, Zac Kenton, Jan Leike, Shane Legg · Google DeepMind
Specification gaming means satisfying an objective’s literal specification without achieving the intended outcome.
An early warning system for novel AI risks
Toby Shevlane · Google DeepMind
Shevlane proposes evaluating general-purpose models for extreme-risk capabilities and alignment, then using the results to guide safety and governance decisions.
Securing internal systems against increasingly capable and imperfectly aligned AI
Rohin Shah and Four Flynn · Google DeepMind
The roadmap’s key bet: internal agent safety must hold even when alignment is imperfect.
Gemma Scope: helping the safety community shed light on the inner workings of language models
Language Model Interpretability team · Google DeepMind
Gemma Scope extends interpretability tools to Gemma 2 2B and 9B, with sparse autoencoders trained across every layer and sublayer—not just tiny models or one layer.
WaveNet: A generative model for raw audio
Aäron van den Oord, Sander Dieleman · Google DeepMind
WaveNet’s central claim is that generating raw audio directly can bring synthesized speech closer to human speech, while letting the same model generate music.
Gemini Deep Think: Redefining the Future of Scientific Research
Thang Luong and Vahab Mirrokni · Google DeepMind
Aletheia’s key move is to make a proposed proof pass through a verifier that can revise it, restart it, or admit it is unsolved.
Gemini 2.5: Our most intelligent AI model
Koray Kavukcuoglu · Google
Gemini 2.5 Pro Experimental is Google’s first 2.5 release, designed for complex tasks; Google presents its reasoning approach and benchmark performance as a step forward.
Gemini CLI: your open-source AI agent
Taylor Mullen · Google
Gemini CLI brings Gemini into developers’ terminals, with high—but explicitly capped—free-preview usage for individuals.
Announcing the Agent2Agent Protocol (A2A)- Google Developers Blog
Rao Surapaneni · Google
A2A aims to make agents from different vendors and frameworks collaborate across enterprise systems through a shared protocol.
Transformer: A Novel Neural Network Architecture for Language Understanding
Google Research
The Transformer’s central change is direct self-attention: words can connect across a sentence without recurrent word-by-word processing.
Language Models Perform Reasoning via Chain of Thought
Google Research
A few worked examples can prompt a model to unpack multi-step problems, but the reported gains emerge only at sufficient scale—around 100B parameters.
ReAct: Synergizing Reasoning and Acting in Language Models
Google Research
ReAct interleaves a model’s reasoning with actions that query an environment, so fresh observations can guide what it does next.
Looking back at speculative decoding
Google Research
Speculative decoding speeds autoregressive LLM inference by computing several likely tokens in parallel while preserving the target model’s exact output distribution. Its key is probabilistic checking, not simply accepting or rejecting a draft.
Introducing Nested Learning: A new ML paradigm for continual learning
Google Research
Nested Learning argues that model architecture and training rules are levels of one learning system, a shift intended to help models learn new tasks without sacrificing old skills.
DeepSeek V4 Preview Release
DeepSeek
DeepSeek says V4 Preview is live and open-sourced, with 1M context standard across official services and API access available today.
deepseek-ai/FlashMLA: FlashMLA: Efficient Multi-head Latent Attention Kernels
DeepSeek
FlashMLA is an optimized attention-kernel toolkit for DeepSeek-V4.1, but the September 2026 release changes its compatibility boundary, so upgrading can affect existing deployments.
Qwen3: Think Deeper, Act Faster
Qwen Team · Qwen
Qwen3 pairs step-by-step reasoning with fast replies, letting users allocate more computation to harder tasks and less to simpler ones.
QwQ-32B: Embracing the Power of Reinforcement Learning
Qwen Team · Qwen
Qwen reports that 32-billion-parameter QwQ-32B performs comparably to DeepSeek-R1 (671 billion parameters, 37 billion activated), arguing that reinforcement learning can help a strong pretrained model improve its performance.
Qwen2.5-Max: Exploring the Intelligence of Large-scale MoE Model
Qwen Team · Qwen
Qwen reports instruct-model wins for Qwen2.5-Max and makes it available in Qwen Chat and through Alibaba Cloud’s API.
Qwen3-Coder: Agentic Coding in the World
Qwen Team · Qwen
Qwen3-Coder combines a long-context coding model with training that uses execution checks and multi-step interaction—the capabilities the announcement emphasizes for agentic work.
Qwen2.5: A Party of Foundation Models!
Qwen Team · Qwen
Qwen2.5 expands Qwen2 into general, coding, and math model families; the team reports stronger knowledge and task performance across a broad range of model sizes.
Qwen2.5-1M: Deploy Your Own Qwen with Context Length up to 1M Tokens
Qwen Team · Qwen
Qwen’s first open-source 7B and 14B Instruct checkpoints target 1M-token contexts, backed by an inference framework built for long inputs.
QwenLM/Qwen-Agent: Agent framework and applications built upon Qwen>=3.0, featuring Function Calling, MCP, Code Interpreter, RAG, Chrome extension, etc.
Qwen
Qwen-Agent turns Qwen’s instruction following and tool use into a framework for building LLM applications, not just ready-made chat demos.
openai/gpt-oss: gpt-oss-120b and gpt-oss-20b are two open-weight language models by OpenAI
OpenAI
OpenAI’s two open-weight models target different deployment needs, but using them correctly depends on Harmony formatting and choosing an inference path that fits the hardware.
GPT-4.1 Prompting Guide
OpenAI
GPT-4.1 follows prompts more literally than its predecessors, so developers should make desired behavior explicit and expect to migrate prompts that relied on implied intent.