Category: Machine Learning
- Hardcore: Google's Jeff Dean Says the Bottleneck for Million-Chip LLM Pre-training Has Been Completely Broken!
- DeepMind Invests in Hardcore MMO EVE Online to Teach AI the "Dark Forest"
- AI Finally Learns "Self-Confession"! Anthropic's Groundbreaking New Paper Introduces "Introspection Adapters" That Make Black-Box Models Reveal Their Hidden Behaviors
- How Claude's 'Dreams' Work
- Subquadratic — Efficiency is Intelligence
- Abstract-CoT: Reasoning Tokens Slashed 11.6x, Chain-of-Thought Without Words Shatters LLM Efficiency Ceiling
- Paper Brief | Automated Knowledge Graph Enrichment Using Multi-Agent Large Language Models (NeurIPS 2025)
- DeepMind's Nobel-winning CEO's latest interview: The current large model path is not a dead end, but the brute-force methods everyone uses might be wrong; Chinese models are already leading in the open-source domain
- An Excellent New Systematic Survey of Self-Evolving Agents
- The Father of GPT Throws AI Back to 1930: Never Saw a Line of Code, Yet 'Invented' Python!
- Skills-Driven Reasoning Paradigm: Tsinghua & Peking University Propose TRS, Saving 59% Tokens Without Accuracy Drop
- Thinking Without Words: Efficient Latent Reasoning with Abstract Chain-of-Thought
- Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond
- Scaling Laws for Looped Transformers
- NUS, Fudan, and Tsinghua: The First Systematic Survey on Large Model Latent Spaces
- Southeast University's Geng Xin Team: Models Don't Fail Due to Inability, But 'Crowded-Out Capacity' | CVPR 2026
- The MASK Benchmark: Disentangling Honesty From Accuracy in AI Systems
- Autogenesis: A Self-Evolving Agent Protocol
- The World's Most Notorious Forum Uncovered AI's Most Crucial 'Thinking' Ability
- FrontierSWE
- Qwen3.6-35B-A3B: Agentic Coding Power, Now Open to All
- Cognition | Introducing SWE-Check: 10x Faster Bug Detection
- Li Fei-Fei's Team Is Tackling This: From Entropy to Mutual Information, RAGEN-2 Reshapes Reasoning Quality Standards, Preventing AI Agents from Becoming 'More Trained, More Templated'
- Meta Bets on Neural Computers: Is the Next-Gen Computer the Model Itself?
- Frozen Weights Are the Enemy of AI Progress! DeepMind's Top Researcher: The Key to AI Self-Improvement Lies in Evaluation, Drawing from Formal Verification! Expert Models Are Stepping Stones to Generalized AGI!