Remember, Don't Re-read: Stateful ReAct Agents for Token-Efficient Autonomous Experimentation
arXiv:2606.14945v1 Announce Type: new 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 (1
The proliferation of more autonomous AI systems (AI agents) is driving research into making them more efficient and scalable, directly addressing current limitations.
This development significantly reduces the operational cost and improves the scalability of AI agents, making their deployment more practical for complex, iterative tasks.
AI agents operating at lower token costs per iteration become more viable for real-world applications that require extensive experimentation or long chains of reasoning.
- · AI agent developers
- · Cloud computing providers (reduced egress/ingress for customers)
- · Businesses adopting autonomous experimentation
- · Inefficient AI agent frameworks
- · High-cost LLM API providers (without competitive pricing adjustments)
Stateful AI agents become more economically feasible for various enterprise applications due to reduced operational costs.
This efficiency could accelerate the development and deployment of more sophisticated autonomous research and development capabilities across industries.
The widespread adoption of token-efficient autonomous experimentation could lead to faster innovation cycles and a greater reliance on AI for complex problem-solving.
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Read at arXiv cs.LG