Google 研究团队推出 StreamVibe 视频智能体，学会在合适时机提供帮助

The deferral-first training trick is the transferable idea: rather than letting an agent freely rewrite rankings, PAMO rewards overrides only when they preserve accuracy. Developers building recommendation or agent systems can apply the same counterfactual masking to any cross-context personalisation task. It also frames the privacy tension of pooling history across apps, a question regulators and platform teams will face as personal agents spread.

rohanpaul_ai@rohanpaul_aiSource

The finding reframes novelty filtering as a recall-versus-precision tradeoff: checking whole proposals punishes incremental builds on prior work, which is how most real papers actually happen. Anyone running an automated research-idea pipeline can adopt the claim-level scoring method directly, keeping proposals that extend one known result. The 96%-versus-1.5% rejection gap gives a concrete benchmark for auditing your own filter.

rohanpaul_ai@rohanpaul_aiSource

The practical shift is reframing skill screening: prompting the scanner LLM to look for missing human oversight outperforms framing the task as malware detection, both in recall and precision. Teams deploying agents that load shared skills should audit their SKILL.md files now and scope every "don't ask" instruction to a specific context like CI. This matters for anyone building on Claude, Codex or other agents that support skills.

rohanpaul_ai@rohanpaul_ai5 sourcesSource