Remember, Don't Re-read: Stateful ReAct Agents for Token-Efficient Autonomous Experimentation
This paper introduces a stateful ReAct agent built with LangGraph that avoids re-reading entire history at each iteration, cutting token costs by 90% in hyperparameter tuning and 52% in code optimization while maintaining performance. It provides a blueprint for practitioners to implement token-efficient autonomous experimentation. (Source: arXiv, June 2026)
[2606.14945] Remember, Don't Re-read: Stateful ReAct Agents for Token-Efficient Autonomous Experimentation
[Submitted on 12 Jun 2026]
Title:Remember, Don't Re-read: Stateful ReAct Agents for Token-Efficient Autonomous Experimentation
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Abstract:The autoresearch pattern enables autonomous experimentation by having a large language model (LLM) iteratively modify code to optimize a target metric. Its stateless design, however, reconstructs experimental context from scratch at every iteration, incurring $O(n)$ token cost per iteration and $O(n^{2})$ total. This work reformulates the pattern as a stateful ReAct agent using LangGraph, where typed persistent state carries experimental history across iterations via a tool-calling interface. Two benchmarks are evaluated: hyperparameter tuning (15 iterations, small per-iteration observations) and code performance optimization (40 iterations, large per-iteration observations containing full source code and benchmark results). On hyperparameter tuning, the stateful agent consumes 90\% fewer tokens (2{,}492 vs.\ 24{,}465). On code optimization, the stateful agent consumes 52\% fewer tokens (627K vs.\ 1{,}275K) while achieving comparable optimization quality on both tasks. The token reduction is structural: the stateless agent re-reads the full history at $O(n)$ cost per iteration, while the stateful agent operates within a fixed-size conversation window at $O(1)$ cost. This paper describes the architecture in sufficient detail for practitioners to implement a stateful autoresearch agent for their own workflows.
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2606.14945 [cs.LG]
(or arXiv:2606.14945v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.14945
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Faramarz Jabbarvaziri [view email] [v1] Fri, 12 Jun 2026 20:40:59 UTC (733 KB)
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