EvoSpace: Co-Evolving the Harness and Edit Space for Memory-Grounded Question Answering

The performance of LLM-based agents depends not only on their underlying models but also on the harnesses that orchestrate their interactions with the environment. Recently, researchers have focused on harness self-evolution, in which agents iteratively refine their harnesses using operational experience within a harness edit space. However, dynamically aligning this edit space with evolving adaptation needs remains a central challenge. A fixed harness edit space may preclude necessary updates, whereas allowing all permissible edits initially increases the complexity of selecting and combining updates. We introduce EvoSpace to address this challenge by jointly evolving the agent harness and its corresponding edit space. As the harness evolves, feedback-driven diagnosis guides EvoSpace to progressively expand the permissible updates across components, scopes, and operations. This enables changes beyond the initial space without exposing all combinations from the outset. Across two established benchmarks, EvoSpace improves over the compared memory systems. On LoCoMo, EvoSpace achieves average relative gains across F1 and BLEU-1 of 8.62% with qwen3.6-flash and 13.71% with GPT-5.1 over EvolveMem. From the experimental analysis, our key insight is that the harness edit space governs which adaptations are possible, making its dynamic alignment with evolving adaptation needs essential for effective self-evolution.