Can Coding Agents Sustain Continual Evolution without Forgetting?
Story 17, from arXiv
A study shows that coding agents can lose performance on earlier tasks when evolving to new ones, highlighting the challenge of continual self-evolution without catastrophic forgetting.
Key points
- Existing harness evolution methods focus on single-task evolution, but real-world coding agents must handle multiple task types.
- Experiments show that evolving on a new dataset can severely degrade performance on previously learned tasks.
- Using DeepSeek-V4-Flash-0731, Meta-Harness improved SWE-Bench performance to 94.0% but dropped to 32.0% after further evolution on FeatureBench.
- The paper identifies insights for improving continual self-evolution of agent harnesses.
- Code and datasets are available for further exploration.
Why it matters
Software engineers using AI agents for coding tasks need to understand how these agents evolve over time. If agents forget past skills, it could lead to unreliable or inconsistent performance in real-world development workflows.
Counterpoint
The study focuses on specific datasets and models, so the results may not generalize to all coding tasks or agent architectures.
Sources
This summary was written by a language model and may contain errors. Read the original for the full story.