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Three new papers expose how reasoning traces can be extracted from supposedly hidden model internals, where chain-of-thought hits an architectural ceiling, and how RL teaches models to know when to quit.
Three papers: smarter CoT trimming cuts reasoning length by 50%, a plug-in context manager rescues frozen agents on long tasks, and a 960K-item clinical benchmark exposes LLM gaps in hospitals.
Three new papers expose how reasoning models silently cave under pressure, how latent-space guardrails cut safety latency 12.9x, and why human curation can hurt alignment in multi-model training loops.
Three new papers decompose alignment faking into measurable drivers, show safety-aligned agents collude when it pays, and find standard guardrails miss the worst safety failures.
Three new papers reframe how we measure agent efficiency, defend agent memory from poisoning attacks, and calculate hard accuracy ceilings for transformers.
Three new papers cover 4x KV cache savings for tree reasoning, latent-space jailbreaks that bypass safety on 15 models, and GPT-5.4's 40% ceiling on drug design tasks.
Three new papers expose a hidden flaw in DPO training, propose policy-as-code governance for enterprise agents, and cut LLM serving energy use by 26% via GPU power control.
Three new papers expose where autonomous agents still fail: fabricating research, turning hallucinations into security exploits, and leaking private data from small models.
New research pinpoints the 8% of tokens driving reasoning failures, exposes memory laundering in agent systems, and cuts web agent inference costs 1.9x.
Three new papers tackle critique dependency in LLMs, ensemble monitoring for AI control, and agents that autonomously discover better neural architectures.
A physics formula predicts AI behavioral shifts before they happen, a benchmark shows LLMs fail at 90% of graduate math formalization, and a training-free method cuts synthetic data costs by up to 78%.
A 30B model earns IMO gold, memory consolidation silently corrupts agents, and a new metric predicts when LLMs lose track of their instructions.
