arXiv:2610.37859v1 Announce Type: new Abstract: Language-model agents differ fundamentally from prior intelligent systems in their ability to learn from natural language in context. This capability enables agents to iteratively refine their answers in response to feedback, a prevalent mode of operation in modern agentic workflows. However, the community primarily ranks models by metrics such as Pa

So what

What this event means by reading role—not a longer recap.

BuilderIf you ship iterative agent loops, benchmark candidate models with Seq@K-style feedback retries rather than Pass@1, since rankings and cost-optimal configs diff
ResearcherSeq@K formalizes refinement-from-feedback as a distinct capability, so report it alongside Pass@K and study how feedback design changes model trajectories.

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Score dimensions

Higher total means read first. Each bar is one factor we use to rank the system pool. How we score

RelevanceHow tightly this is about AI.: 82
ImpactHow much this could change the field or the market.: 55
NoveltyHow new this is versus a recap.: 70
CredibilityHow much we trust the source.: 97
ActionabilityWhether a reader can do something with it.: 60