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papersTODAY 04:00 UTC

Paper Proposes Self-Certification of Representation Adequacy for Agents

A new arXiv paper examines a structural risk for agents that act on compressed summaries of their history: when the summary conflates histories that call for different optimal actions, no decision rule defined over that summary can avoid a persistent per-round loss. The authors propose sequential self-certification of representation adequacy, framed around achieving minimum task loss. The work is cross-listed in cs.AI and cs.LG.

arXiv AI agents compressed history summaries representation adequacy self-certification task loss minimization

COVERAGE · 2 REPORTS · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.AISelf-Certification of Representation Adequacy: Sequential Certification at Minimum Task Loss ↗TODAY 04:00 UTC

arXiv cs.LGSelf-Certification of Representation Adequacy: Sequential Certification at Minimum Task Loss ↗TODAY 04:00 UTC