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cs.CL2025
Scaling Laws for Code: Every Programming Language Matters
Jian Yang, Shawn Guo, Lin Jing +8
Code large language models (Code LLMs) are powerful but costly to train, with scaling laws predicting performance from model size, data, and compute. However, different programming…
cs.CL2025
ReForm: Reflective Autoformalization with Prospective Bounded Sequence Optimization
Guoxin Chen, Jing Wu, Xinjie Chen +6
Autoformalization, which translates natural language mathematics into machine-verifiable formal statements, is critical for using formal mathematical reasoning to solve math proble…
cs.CL2024★ 1 cited
CoAct: A Global-Local Hierarchy for Autonomous Agent Collaboration
Xinming Hou, Mingming Yang, Wenxiang Jiao +3
Existing LLMs exhibit remarkable performance on various NLP tasks, but still struggle with complex real-world tasks, even equipped with advanced strategies like CoT and ReAct. In t…