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Source note2 min read

The Blind Curator: How a Biased Judge Silently Disables Skill Retirement in Self-Evolving Agents

Source

arXiv

arxiv.org/abs/2607.07436v1

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Source

Plain-Language Brief

The authors investigate how a biased judge affects the retirement of skills in self-evolving agents, particularly in reference-free tasks. They show that a biased judge can cause a mechanism failure they call 'silent switch off of the curator'. This failure disables skill retirement past a certain threshold and degrades evaluation quality only under specific conditions.

What the Document Says

  • Self-evolving agents retire bad skills by observing failures, but this is contingent on an unbiased reward. However, in reference-free tasks, the LLM judges used are biased.
  • The authors demonstrate that a biased judge not only adds noise but also 'silently switches off the curator', making skill retirement impossible past a threshold.
  • They use corrupted-reward analysis and behavioral studies on a reference-free report-writing testbed with code generation to isolate this causal channel.
  • False-pass bias (failures passing as successes) is responsible for the mechanism failure, affecting all domains, failure rates, except near-zero-false-pass verifier-like graders.
  • The outcome depends on the regime: evaluation quality degrades only where corruption also starves skill synthesis. Otherwise, it remains steady.
  • The study contributes a behavioral safety result, not a performance one, and offers a cheap defect-injection audit to inform operators about their judge's bias before deployment.

Key Concepts Mentioned

  • self-evolving agents
  • skill retirement
  • biased judge
  • LLM judges
  • reference-free tasks
  • corrupted-reward analysis
  • mechanism failure
  • false-pass bias
  • evaluation quality
  • behavioral safety result

Claims Made by the Document

  • A biased judge can 'silently switch off the curator' in self-evolving agents.
  • False-pass bias disables skill retirement past a certain threshold.
  • Evaluation quality degrades only where corruption also starves skill synthesis.

Evidence Used

  • Behavioral studies and corrupted-reward analysis on reference-free report-writing testbed with code generation.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

A self-evolving agent retires its bad skills by watching them fail, so what happens when the judge cannot see the failures? Skill retirement is the structural constraint that keeps a growing library from drifting below the no-skill baseline, but its guarantee assumes an unbiased reward, which is false for the LLM judges that reference-free tasks force upon us. We show that a biased judge does not merely add noise; it \emph{silently switches off the curator}. We make this precise with a corrupted-reward analysis and, isolating the causal channel by injecting corruption on top of a deterministic reward, a behavioral study on a reference-free report-writing testbed with a code-generation cross-check. Symmetric noise leaves retirement intact, but \emph{false-pass} bias (failures slipping through as passes) disables contribution-based retirement past a sharp threshold that no amount of data can cross. Separating genuine retirement from cap-eviction churn shows this \emph{mechanism} failure is universal, holding across domains and failure rates and sparing only near-zero-false-pass, verifier-like graders. The downstream \emph{outcome}, though, is regime-dependent: eval quality degrades only where the same corruption also starves skill synthesis, and otherwise holds steady, so the disabled curator is \emph{silent}, surfacing in no aggregate metric. The contribution is a behavioral safety result, not a performance one. A cheap defect-injection audit then tells an operator, before deployment, which side of the threshold their judge occupies.