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.CLPaper11d ago

Not Self-Decidable: LLMs Cannot Draw the Boundary of What an Agent Verifier Can Check

This is the core problem deployed agents face in regulated domains. Models collapse on regulatory text even though they agree on obvious cases, and they err consistently in the same direction on deployed systems, so picking one as the conservative choice doesn't work. If you're building agents in finance, healthcare, or law, this says you cannot rely on the model to route decisions correctly. You need a hard audit layer, not a soft one.

agents regulation safety deployment

arXiv cs.CLPaper15d ago

Mingbird: A Local-First Agent Harness Enabling Small Open Models to Complete Real Tasks

Open models are finally viable for real agent work if you use the right harness. Mingbird proves that failures attributed to model scale are often harness problems, which is actionable: if you're deploying small models locally, this is production-ready code that will save you months of debugging. The real-task evaluation (LRAB) is credible and the mechanisms are simple enough to retrofit. This is infrastructure that works.

research agents open-models deployment

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