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🧠 AI🔴 BearishImportance 7/10
EvoClaw: Evaluating AI Agents on Continuous Software Evolution
arXiv – CS AI|Gangda Deng, Zhaoling Chen, Zhongming Yu, Haoyang Fan, Yuhong Liu, Yuxin Yang, Dhruv Parikh, Rajgopal Kannan, Le Cong, Mengdi Wang, Qian Zhang, Viktor Prasanna, Xiangru Tang, Xingyao Wang|
🤖AI Summary
Researchers introduce EvoClaw, a new benchmark that evaluates AI agents on continuous software evolution rather than isolated coding tasks. The study reveals a critical performance drop from >80% on isolated tasks to at most 38% in continuous settings across 12 frontier models, highlighting AI agents' struggle with long-term software maintenance.
Key Takeaways
- →EvoClaw benchmark tests AI agents on continuous software evolution using reconstructed Milestone DAGs from commit logs.
- →AI agent performance drops dramatically from >80% on isolated tasks to maximum 38% in continuous development scenarios.
- →The benchmark exposes critical vulnerabilities in AI agents' ability to manage technical debt and error propagation over time.
- →Existing benchmarks fail to capture temporal dependencies and real-world software evolution challenges.
- →DeepCommit pipeline reconstructs verifiable development milestones from noisy commit logs to enable realistic testing.
#ai-agents#software-development#benchmarking#evoclaw#continuous-evolution#technical-debt#ai-evaluation#coding-agents
Read Original →via arXiv – CS AI
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