What question did this study set out to answer?

The goal is to develop a framework that allows AI agents to continually refine their context without altering their underlying model.

March 18, 2026ElectronicsOpen Access

Towards Self-Evolving Agents: A Dual-Process Framework for Continual Context Refinement

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Framework enables continual learning and context updates in AI agents, enhancing performance across tasks.

The goal is to develop a framework that allows AI agents to continually refine their context without altering their underlying model.
Introduced the Dual-Process Agent (DPA) framework for interaction episodes.
Utilized a fast System 1 for quick responses and a slow System 2 for reflection.
Maintained bulletized memory entries with statistics to prevent degradation.
Employed a curator gate to filter out unhelpful memory updates.
Conducted experiments on six diverse benchmarks to evaluate performance.
DPA consistently outperformed vanilla prompting and competitive baselines.
Achieved best overall performance across multiple reasoning and knowledge-intensive tasks.
Showcased effective continual context refinement in AI interactions.

Teng et al. (2026) studied this question.

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