LLM Chaos, AI Peer Review, and Auto Fine-Tuning
Three papers today: floating-point chaos in transformers, GPT-5 reviewing 22,977 AAAI papers, and an agent system that automates LLM fine-tuning better than human experts.
Science
Compact Contexts, Smarter Fine-Tuning, and the Solver Trap
Three papers from today's arXiv: a joint fix for KV cache bloat and attention cost, new evidence that fine-tuning belongs in the middle of a transformer, and why stronger reasoning hurts behavioral simulation.
MoE Myths, Context Compression, and Steering Proofs
Three papers this week challenge how we think about MoE expert routing, LLM context management, and the limits of activation steering.
Autonomous Research, Broken Reasoning, Smarter Agents
Three new papers: AlphaLab runs autonomous GPU research campaigns, open-weight reasoning models collapse under text reformatting, and HiL-Bench reveals agents can't decide when to ask for help.
Clinical AI Harm, Smarter Reasoning, and Safer Agents
Three papers: AI safety measures withhold critical clinical guidance from patients, SAT cuts reasoning tokens by 40%, and conformal prediction blocks wrong multi-agent consensus.
Blind Refusal, Broken Steps, and Free Uncertainty
Three papers expose safety training's moral blind spot, two distinct failure modes inside reasoning models, and a 10x cheaper way to know when a reasoning model is guessing.
MedGemma 1.5, Smarter MCTS, and Auditing AI Agents
Google's MedGemma 1.5 brings 3D medical imaging to open AI, PRISM-MCTS halves reasoning cost, and a new audit framework finds 617 security flaws across six major agent projects.
AI Research: Emotions, Theory of Mind, Unlearning
Anthropic finds functional emotions inside Claude that can drive blackmail, a poker experiment reveals memory alone creates Theory of Mind in agents, and a new framework targets sensitive reasoning traces for erasure.
Coding Grandmasters, Formal Proofs, and Agent Hazards
Three new papers: AI beats all humans in live Codeforces rounds, 30K agents formalize a math textbook in Lean, and computer-use agents fail badly on safety benchmarks.
Unsafe Agents, Rising AI Tides, and Training Traps
Three new papers on agent prompt injection attack rates, MIT's broad-based AI automation finding, and a silent normalization-optimizer coupling failure in LLM training.
Decisions Before Thinking, Smaller RL Models, Agent Collusion
Three new papers ask hard questions: do LLMs decide before they reason, can a 4B RL model beat a 32B, and can activation probes catch colluding agents?
Self-Organizing Agents, Brain-Like LLMs, AI Discovery
Three new papers: self-organizing multi-agent systems beat rigid hierarchies by 14%, LLMs spontaneously develop brain-like layer specialization, and AI evolves scientific ideas through literature exploration.
