mlsanityData to 7 Oct 2026

PACE: Anytime-Valid Acceptance Tests for Self-Evolving Agents

arXiv 2606.08106 ↗First posted 6 Jun 2026cs.AI cs.MAcited by 8

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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

  1. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  2. 2024Qwen2.5 Technical Reportcited by 4,713
  3. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,710
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  10. 2023Large Language Models as Optimizerscited by 830
  11. 2023DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelinescited by 803
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12 more
  1. 2023Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolutioncited by 521
  2. 2025GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learningcited by 449
  3. 2024Automated Design of Agentic Systemscited by 360
  4. 2024Agent Workflow Memorycited by 342
  5. 2025Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agentscited by 294
  6. 2024TextGrad: Automatic "Differentiation" via Textcited by 250
  7. 2022Game-theoretic statistics and safe anytime-valid inferencecited by 145
  8. 2015The Ladder: A Reliable Leaderboard for Machine Learning Competitionscited by 67
  9. 2025Can Large Reasoning Models Self-Train?cited by 66
  10. 2021There are natural scores: Full comment on Shafer, "Testing by betting: A strategy for statistical and scientific communication"cited by 49
  11. 2024G"odel Agent: A Self-Referential Agent Framework for Recursive Self-Improvementcited by 27
  12. 2017Online control of the false discovery rate with decaying memorycited by 11

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Citing papers per year: 2026: 8.

8 papers.