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The paper presents an open-source framework for building and comparing methods that let LLM agents improve their own prompts, skills, tools, and orchestration from execution feedback. Its main contribution is shared infrastructure for execution, evaluation, recovery, and lineage tracking, so that method comparisons are reproducible. The reported results are modest and mixed: the largest pass@1 gain is 13.5 points, method rankings shift across benchmarks, and some gains don't transfer to held-out tasks. Readers evaluating self-improving agent claims should weigh those caveats.
RSIHub: A Composable Framework for Agent-Evolution Methods
arXiv:2610.32253v1 Announce Type: new Abstract: Agent-evolution methods use execution feedback to improve prompts, skills, tools, and orchestration, but their implementations often rebuild the same experimental machinery. We present RSIHub, an open-source framework for building, comparing, and extending these methods on shared infrastructure. Recipes compose reusable operators and declare mutable resources; RSIHub supplies execution, evaluation, recovery, and lineage. Researchers can focus on evolution policy while evaluation stays outside the mutable surface and results remain tied to candidates and evaluation conditions. We instantiate AHE, Hyperagents, A-Evolve, and GEPA without modifying the framework core, keeping method-specific logic in recipes and operators. We evaluate them with MiniSWE and Codex on Terminal Bench 2 and $\tau^3$ Banking under fixed model and evaluator configurations. The largest observed overall pass@1 gain is 13.5 percentage points, while method rankings vary across settings and optimization gains do not always transfer to held-out tasks. RSIHub provides a common protocol for comparing evolution policies, inspectable candidate histories, and reusable components for developing new methods.
Abstract:Agent-evolution methods use execution feedback to improve prompts, skills, tools, and orchestration, but their implementations often rebuild the same experimental machinery. We present RSIHub, an open-source framework for building, comparing, and extending these methods on shared infrastructure. Recipes compose reusable operators and declare mutable resources; RSIHub supplies execution, evaluation, recovery, and lineage. Researchers can focus on evolution policy while evaluation stays outside the mutable surface and results remain tied to candidates and evaluation conditions. We instantiate AHE, Hyperagents, A-Evolve, and GEPA without modifying the framework core, keeping method-specific logic in recipes and operators. We evaluate them with MiniSWE and Codex on Terminal Bench 2 and $\tau^3$ Banking under fixed model and evaluator configurations. The largest observed overall pass@1 gain is 13.5 percentage points, while method rankings vary across settings and optimization gains do not always transfer to held-out tasks. RSIHub provides a common protocol for comparing evolution policies, inspectable candidate histories, and reusable components for developing new methods.
Artificial Intelligence (cs.AI)
arXiv:2610.32253 [cs.AI]
(or arXiv:2610.32253v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.32253 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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