Can Coding Agents Sustain Continual Evolution without Forgetting?
READ THE ORIGINAL SOURCEOpen original sourcehuggingface.co · Opens in a new tabThe paper studies whether coding agent harnesses can keep evolving across multiple software engineering task datasets without catastrophic forgetting. Three SWE task datasets are organized into six evolution directions, and two harness evolution methods are applied continually. Results show that evolving on a later dataset can substantially degrade performance on earlier ones. For example, Meta-Harness reached 94.0% on SWE-Bench with DeepSeek-V4-Flash-0731, but fell to 32.0% after further evolution on FeatureBench by the fifth round. The authors report insights to guide future continual harness self-evolution.
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The paper studies whether coding agent harnesses can keep evolving across multiple software engineering task datasets without catastrophic forgetting. Three SWE task datasets are organized into six evolution directions, and two harness evolution methods are applied continually. Results show that evolving on a later dataset can substantially degrade performance on earlier ones. For example, Meta-Harness reached 94.0% on SWE-Bench with DeepSeek-V4-Flash-0731, but fell to 32.0% after further evolution on FeatureBench by the fifth round. The authors report insights to guide future continual harness self-evolution.
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- 03Where are the sources?
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- 04What could mislead?
The study covers two harness evolution methods, three datasets, and one model in the cited example; generality beyond these settings is not established. Source claims not independently checked.
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Key findings from the stored brief
- Evolution on a subsequent dataset substantially undermines performance on previously seen datasets.
- Meta-Harness reached 94.0% on SWE-Bench after evolution with DeepSeek-V4-Flash-0731.
- After further FeatureBench evolution, SWE-Bench performance dropped to 32.0% by round five, a 66% relative decrease.
The study covers two harness evolution methods, three datasets, and one model in the cited example; generality beyond these settings is not established. Source claims not independently checked.
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Why this may matter
Coding agents may need to handle multiple task types over time, so forgetting during harness evolution is a practical obstacle. The study documents this effect and offers directions for designing continual self-evolving agent harnesses.
Limits and open questions
The study covers two harness evolution methods, three datasets, and one model in the cited example; generality beyond these settings is not established. Source claims not independently checked.
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