Agent Phase Collapse, Reasoning Exits, Preference Gaps
Three new arXiv papers map capability cliffs in agent world models, the narrow benefit of learned reasoning stops, and a 56% accuracy ceiling when agents help users build preferences.
Science
Agent Languages, Sampling Ceilings, and Abstention
Three new papers on agents inventing symbolic languages to cut reasoning tokens by 3-6x, sampling ceilings that waste inference compute, and context-engineering to double agentic abstention rates.
Tandem Training, World Models, and Efficient Agents
Three new arXiv papers on making RL reasoning legible across models, fixing broken world model latent states, and training small agents to beat their teachers.
Refusal Gaps, Prompt Bleed, and Scaling's Logic Limit
Three new papers reveal how LLM safety hinges on persona training, how prompt modules interfere in deployed agents, and why scaling alone cannot reach symbolic reasoning.
Quantization's Hidden Tax, Cliff Tokens, Smarter Memory
Three new arXiv papers reveal hidden costs in quantized reasoning models, single-token failure triggers, and a new framework that cuts agent memory errors by up to 79%.
AI Diagnosis, Cache Efficiency, and Agent Security
Three papers from today's arXiv: a 32B medical model beats DeepSeek-R1 in rare disease diagnosis, a KV cache method keeps 97% accuracy with 3% memory, and a new benchmark red-teams agentic AI systems.
AI Research: Orchestration Beats Scale, Small Models Win
Sakana Fugu tops SWE-Bench Pro by routing tasks across rival LLMs, Microsoft's 9B browser agent beats OpenAI Operator, and a 3B model from Weibo matches DeepSeek V3.2 on math.
Emergent Alignment, Agent Memory, and Smarter Reasoning
Three arXiv papers: a conscience mechanism for ethical training, shared memory for agent populations, and selective verification that cuts test-time compute waste.
Faster Agents, Skewed Evals, and Brand Bias in LLMs
Three new papers: agents that compile runs into 8-13x faster state machines, benchmark scores that shift with compute budget, and big brands monopolizing LLM recommendations.
AI Engrams, Cognitive Debt, and Agent Trust
Three new papers tackle what lives inside a trained model, how AI dependence erodes human cognition, and whether AI teams can calibrate trust.
Agents Hit 89%, Evals Get a Schema, Memory Falls Short
Three papers from today's arXiv: workplace agents jumped from 43% to 89% task completion in two years, a 47-researcher coalition ships a unified eval schema, and agent memory only helps when similarity tops 0.8.
Tool Blindness, Tree Search, and the Road to ASI
Three new papers expose a 50-point gap in agent tool knowledge, show tree search tripling inference throughput, and map the research between AGI and superintelligence.
