Autonomous codebase refactoring requires coding agents to restructure working software while strictly preserving externally observable behavior and prioritizing execution performance and compute efficiency over stylistic preference. Existing benchmarks: refactoring benchmarks assess functional correctness while overloo...
Shi-Jian Wang, Fan-Qing Meng, Jiajun Chen et al.· 0 citations
Recursive self-improvement requires carrying accepted changes into later improvement cycles, while studying agent-proposed changes also requires substantial research infrastructure. Existing settings often leave agents to rebuild routine infrastructure or restrict exploration to individual components. We introduce RSIG...
Fan-Qing Meng, Ling-Xiao Du, Hao-Cheng Lu et al.· 2 citations
Recursive self-improvement (RSI) aims to achieve compounding gains by having models improve themselves. While most existing RSI systems optimize external agent harnesses or prompts around a frozen base model, data-centric RSI directly updates the model's own parameters by training on agent-generated data. However, beca...
Zi-Qi Zhao, Fan-Qing Meng, Hao-Cheng Lu et al.· 0 citations
It is suggested that current agents can make useful data-centric discoveries but cannot yet translate feedback into consistent improvements, and RSIBench-Data provides a measurable, auditable testbed for the research capabilities required for recursive self-improvement.
Fan-Qing Meng, Ling-Xiao Du, Qi-Guang Chen et al.· arXiv.org· 17 citations· ⚡2
This survey examines agentic artifact creation, which is defined as stateful construction in which an AI system materially constructs or revises a deliverable and intermediate observations redirect later work, and formulate principles for keeping commitments and responsibility explicit, turning feedback into targeted r...
Tianfu Wang, Zhe-Zheng Hao, Xinchi Xia et al.· 1 citation
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