PACE: Anytime-Valid Acceptance Tests for Self-Evolving Agents
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Abstract (arXiv)
Self-evolving agents improve by repeatedly proposing changes to their own prompts, skills, or workflows and keeping those that score higher on a small held-out set. Almost all effort has gone into the proposer that generates candidates; we argue the weak point is the acceptor, the rule that decides whether to commit a change. Applied hundreds of times against the same noisy dev estimate, the ubiquitous "keep it if the score went up" rule is uncontrolled adaptive multiple testing: the agent effectively p-hacks itself, accumulating false commits that make it churn and drift rather than improve. We recast committing as a sequential hypothesis test and propose PACE (Paired Anytime-valid Commit Evaluation), a training-free, anytime-valid commit gate. Each candidate is compared to the incumbent on identical instances and committed only when a testing-by-betting e-process accumulates decisive evidence, stopping early to save evaluations and controlling each candidate's false-commit probability at a user-set level even under optional stopping (a per-decision guarantee). On Qwen2.5 agents (0.5B-3B) self-evolving at the prompt level on GSM8K, SVAMP, and ARC-Challenge, greedy acceptance commits 30-42% false and 10-33% harmful edits when a genuine improvement is hidden among noisy proposals, while PACE commits the real one and essentially nothing else, matching greedy's held-out accuracy at sharply lower variance and about 18% lower evaluation cost. With no real gain available, greedy commits 13-21 spurious self-modifications per run (72-100% false) and degrades the most fragile agent by 4.9 points, while PACE holds at baseline. Reliability of self-evolution depends on the acceptor, not only on the proposer.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 24 of its 32 references that are in the map. First posted on arXiv 6 Jun 2026.
Builds on
24 of 32 references.
References · 24
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12 more
- 2023Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolutioncited by 521
- 2025GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learningcited by 449
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Built on by
Citing papers per year: 2026: 8.
8 papers.
- 2026Recursive Experiential-Working Memory Evolution for Long-Horizon Agent Harnessescited by 4
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- 2026Local Edits, Global Ripples: Replay-Informed Policy Adaptation for Workflow Synthesiscited by 0
- 2026Propose, Don't Judge: An Anytime-Valid Referee for LLM Agents That Mine Investment Factorscited by 0
- 2026Which Self-Improvements Should We Trust? Reliable Self-Improvement When Agents Reuse Their Benchmarkscited by 0
- 2026EvoSteer: Online Self-Evolving Graph Orchestration via Reference-Anchored Credit Assignmentcited by 0
- 2026MemCodex: Self-Programming Hierarchical Memory for Language Agentscited by 0
- 2026MemCodex: Self-Programming Hierarchical Memory for Language Agents30 Sep 2026
- 2026EvoSteer: Online Self-Evolving Graph Orchestration via Reference-Anchored Credit Assignment29 Sep 2026
- 2026Which Self-Improvements Should We Trust? Reliable Self-Improvement When Agents Reuse Their Benchmarks27 Sep 2026
- 2026Propose, Don't Judge: An Anytime-Valid Referee for LLM Agents That Mine Investment Factors22 Sep 2026
- 2026Local Edits, Global Ripples: Replay-Informed Policy Adaptation for Workflow Synthesis10 Sep 2026
- 2026Discovering Translation-Worthy Languages with E-Values6 Sep 2026
- 2026Recursive Experiential-Working Memory Evolution for Long-Horizon Agent Harnesses25 Aug 2026
- 2026Pay Only for Disagreement: Certified No-Regression Verdicts for Model Updates with Matching Label-Complexity Bounds22 Jul 2026