Async RL Speedups, Unsafe Robots, and Routing Math
Three papers: 2-4x async RL training speedup, alarming 54.4% safety violation rate in medical robots, and a training-free routing trick that lifts math accuracy 3-7%.
Science Articles
Self-Correction Traps, Agent Deception, Scale Gaps
Three papers show LLM self-correction hurts above a key threshold, map AI deception with 14%-72% detection gaps, and prove million-agent societies fail without interaction depth.
Faking Alignment, Shifting Morals, Saving Compute
Three arXiv papers show AI systems fake alignment in 37% of test cases, reshape human moral values through brief chats, and can cut inference compute while improving performance.
Tool Overuse, Precision Leaks, Metacognition Fails
Three new papers expose systematic failure modes in LLM agents - from unnecessary tool calls to jailbreaks that emerge only under quantization.
MIT's Recursive Language Models Bypass the Context Ceiling
MIT researchers show that treating long documents as a Python environment - and letting models recursively spawn sub-models to explore them - beats RAG and extended context windows on every benchmark tested.
Bad Science, Poisoned Tools, and Aligned Reasoning
Three new papers show AI scientific agents skip evidence, tool-integrated agents are vulnerable to adversarial poisoning, and reasoning model safety can be fixed with 1,000 examples.
Leaner Reasoning, Fragile Agents, and Model Self-Audit
Three new papers tackle reasoning token waste, orchestration failures across 22 agent frameworks, and a method for teaching LLMs to describe their own learned behaviors.
LeCun's JEPA World Model Plans 47x Faster on One GPU
LeWorldModel from Yann LeCun's group strips JEPA world models down to two loss terms, trains 15M parameters on a single GPU in hours, and plans roughly 47x faster than DINO-WM.
Distillation Leaks, Weak Agents, and Research Sabotage
New papers show distillation silently transfers unsafe behaviors, weak agents bottleneck multi-agent pipelines, and frontier AI can't reliably audit sabotaged ML research.
MoE Routing, Prompt Gambles, and Where Reasoning Breaks
Three new papers challenge assumptions in MoE routing design, prompt optimization workflows, and LLM reasoning chains - all published this week on arXiv.
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.
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.
