Self-Evolving Agents with Anytime-Valid Certificates
Quick Answer
The SEA architecture enables self-evolving agents to modify behavior while adhering to a fixed error budget, utilizing a versioned harness around a frozen base model.
Quick Take
It demonstrated significant performance improvements on the with models like GLM 5.2 and GPT, achieving deltas of +4 and +5 in evaluations. Future work will focus on reducing run-to-run variance and optimizing task-specific algorithms.
Key Points
- SEA confines self-modification to a steering adapter and a versioned harness.
- Five loop controllers ensure modifications are validated against a fixed error budget.
- Performance improvements of +4 and +5 were achieved on GLM 5.2 and GPT models.
- Mechanisms prevent regressions and confirm that modifications are effective.
- Future work will address run-to-run variance in evaluations.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Self-evolving agents violate the assumption behind most learning-theoretic guarantees: the data, evaluator, components, and hypothesis space are produced by the policy being updated. We present \textbf{SEA}, an architecture that confines self-modification to a small steering adapter and a versioned harness around a \emph{frozen} base model and admits each modification only through an anytime-valid gate that emits an auditable certificate against a fixed error budget. Five loop controllers compose published guarantees; because such gates can only \emph{select} among behaviors the frozen base already produces, five verifier-in-the-loop mechanisms -- best-of-$N$, micro-step search, self-authored reproduction oracles, search-layer control, and self-repair -- supply the dense, grader-free signal the gates require, computed from the issue text alone. On a $52$-instance SWE-bench Verified subset across four base models, base capability is the dominant, confound-free effect, and on two strong base models a deliberate no-op-composite control isolates the suite's contribution at $+4$ and $+5$ (\textsc{Glm}~5.2 $24\to28$; \textsc{Gpt} $29\to34$, the $65\%$ best), with event logs confirming that its mechanisms fire and prevent regressions. Results are single-run on expensive evaluations; confirming run-to-run variance and adapting the per-task algorithm mix are future work.
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2607.00871 [cs.AI] |
| (or arXiv:2607.00871v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.00871 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Biswa Sengupta [view email]
[v1]
Wed, 1 Jul 2026 12:34:52 UTC (63 KB)
— Originally published at arxiv.org
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