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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.
Three papers this week challenge how we think about MoE expert routing, LLM context management, and the limits of activation steering.
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.
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.
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.
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.
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.
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.
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.
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?
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.
New proofs show semantic memory must forget, SARL trains reasoning models without labels, and the Novelty Bottleneck explains why AI won't eliminate human work.
