Beyond the Latest: Historical Capability Fragmentation and Recovery in Self-Evolving Agents

Self-evolving language model agents improve through iterative data generation, feedback, and model updates, yet capabilities do not necessarily accumulate across iterations. Later iterations may lose previously acquired capabilities, while historical iterations retain complementary ones that no single iteration fully covers, a phenomenon we call historical capability fragmentation. Meanwhile, identifying what to preserve from model updates is unreliable: repaired and damaged instances exhibit highly overlapping responses across model parameter rows, making model-parameter response strength insufficient to distinguish beneficial from harmful update effects. We introduce CapDyn-Match, a lightweight response-level recovery framework that selects among frozen historical responses without modifying model parameters. It combines (1) historical candidate scoring, using query–response representations and cross-iteration agreement to identify useful responses, and (2) conservative selection, using a margin gate with a training-derived fallback to avoid harmful switches under insufficient evidence. Across distinct self-evolution frameworks and 14 math, general reasoning, and code benchmarks, CapDyn-Match improves question-micro accuracy over the latest iteration by 3.13 and 3.05 points on two three-iteration Qwen3-4B trajectories. On a six-iteration Qwen3-1.7B trajectory, it improves all 14 benchmarks by up to 7.40 points. Leave-one-dataset-out evaluation further shows transfer to unseen benchmarks. These results reveal a broader limitation of self-evolution: acquiring new capabilities does not guarantee preserving existing ones, leaving useful capabilities distributed across model history rather than consolidated in the latest iteration. Code: https://anonymous.4open.science/r/CapDyn-Match-4FC2/