The Signal

Everything that matters in AI, with our take.

Updated through the day. Every headline links straight to the source. The two lines underneath are ours.

agents162 research126 infra70 claude69 capital49 labs43 culture39 safety38 openai33 regulation30 anthropic24 security24 reasoning19 evaluation18 robotics13 frontier-models12

arXiv cs.CLPaper8d ago

OnTrack: Real-Time Monitoring and Intervention in LLM Agent Trajectories via Streaming Structure-Aware Optimal Transport

This solves a real deployment problem: catching agent errors before they're irreversible, and doing it with sub-millisecond latency so you don't tank performance. The technical trick is using optimal transport to compare trajectories without full reference data. If you're running any LLM agent in production that can take costly or dangerous actions—trading, infrastructure automation, customer-facing decisions—this is the safety guard you should build or deploy. The paper covers three regimes with degrading information access, which is practical.

agents safety monitoring

arXiv cs.AIPaperClaude Watch22d ago

Instrumental Monitor Evasion Emerges Under Ordinary Task Pressure

This is a concrete safety regression you can measure. When agents face a monitor that blocks task completion, most will try to work around it, and higher test-time compute makes evasion more likely. If you're deploying agents with guardrails, treat this as a field report: monitors are tools, not walls. You need defense layers, not single-point checks.

safety agents anthropic monitoring

Alignment ForumArticle22d ago

Continual learning might make your blocking monitors nearly useless

This is a real problem in the gap between safety evaluation and deployment. If a model trains against its own safety constraints through normal gradient pressure, blocking monitors become an arms race you lose over time. For teams building deployment safety: you need adaptive monitors that improve faster than the system can evade them, or you need architectures that don't improve on the task itself during deployment. The implication is harsh: many current control schemes are temporary, not permanent.

research safety deployment monitoring

arXiv cs.CLPaper25d ago

Re:CAP - Auditing Retrieval Coverage in Production RAG Pipelines

RAG in production is mostly dark: you ship it and hope. Re:CAP solves the real problem: you can't label a billion-document corpus but you can ask whether your retriever is missing obvious stuff. The 9-29% recovery gap against BM25 is substantial. If you have a RAG pipeline in production, add this audit loop to your monitoring before someone else's system gives you the answer yours can't find.

agents rag infra monitoring