Source note3 min read
The Blind Curator: How a Biased Judge Silently Disables Skill Retirement in Self-Evolving Agents
Source
arXiv
arxiv.org/abs/2607.07436v1
Source
- Source name: arXiv
- Source kind: paper
- Authors / organization: Xing Zhang, Yanwei Cui, Guanghui Wang, Ziyuan Li, Wei Qiu, Bing Zhu, Peiyang He
- Venue: arXiv: cs.AI
- Published: 2026-07-08
- Collected: 2026-07-09
- URL: http://arxiv.org/abs/2607.07436v1
- PDF: https://arxiv.org/pdf/2607.07436v1
- DOI: UNKNOWN
Plain-Language Brief
The researchers explore the impact of biased judges in self-evolving agents, focusing on skill retirement, a mechanism that helps maintain the quality of an agent's knowledge base by retiring poorly performing skills. They show that a biased judge, commonly used in reference-free tasks, can disable this mechanism, leading to degraded performance in certain situations but not affecting overall metrics. The study suggests a method for operators to determine if their judges are biased before deployment.
What the Document Says
- Self-evolving agents retire bad skills by observing failures, but a biased judge can disable this process in reference-free tasks where LLM judges are used.
- The researchers use a corrupted-reward analysis and behavioral study to demonstrate that a biased judge can 'switch off' the curator, causing a mechanism failure.
- False-pass bias (failures being misclassified as passes) disables contribution-based retirement above a certain threshold.
- The disabled curator only negatively impacts performance in situations where it also starves skill synthesis.
- The study suggests a method for operators to determine if their judges are biased before deployment.
Key Concepts Mentioned
- Self-evolving agents
- Skill retirement
- Biased judge
- Reference-free tasks
- LLM judges
- Corrupted-reward analysis
- Behavioral study
- False-pass bias
- Contribution-based retirement
- Mechanism failure
- Eval quality
- Skill synthesis
- Defect-injection audit
Claims Made by the Document
- A biased judge can disable the skill retirement mechanism in self-evolving agents.
- False-pass bias disables contribution-based retirement above a certain threshold.
- The disabled curator only negatively impacts performance in situations where it also starves skill synthesis.
Evidence Used
- Corrupted-reward analysis and behavioral study on a reference-free report-writing testbed with a code-generation cross-check.
Methods / Approach
- Corrupted-reward analysis
- Behavioral study
Limitations Stated by the Authors
The study's limitations are not explicitly stated in the source.
Limitations Noted by the Agent
Agent interpretation — not from the source.
The model may not fully understand the technical nuances of the research, and it might miss certain subtleties or implications.
Open Questions
- What is the specific threshold for false-pass bias that disables contribution-based retirement?
- How can the impact of a disabled curator be mitigated in situations where it does not starve skill synthesis?
Notes for Future Q&A
May include interpretation. Labeled where it does.
The study focuses on the behavioral safety result, not the performance improvement. The model should not be asked about specific performance metrics or improvements.
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.
