10 citations · 16 across the 7 of their papers we have counts for
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cs.SE2026
SWE-Fuse: Empowering Software Agents via Issue-free Trajectory Learning and Entropy-aware RLVR Training
Xin-Cheng Wen, Binbin Chen, Haoxuan Lan +3
Large language models (LLMs) have transformed the software engineering landscape. Recently, numerous LLM-based agents have been developed to address real-world software issue fixin…
cs.MA2026
Epistemic Gain, Aleatoric Cost: Uncertainty Decomposition in Multi-Agent Debate for Math Reasoning
Dan Qiao, Binbin Chen, Fengyu Cai +7
Multi-Agent Debate (MAD) has shown promise in improving reasoning and reducing hallucinations, yet it remains unclear how information exchange shapes individual reasoning behavior.…
cs.AI2026
Reasoning and Tool-use Compete in Agentic RL:From Quantifying Interference to Disentangled Tuning
Yu Li, Mingyang Yi, Xiuyu Li +6
Agentic Reinforcement Learning (ARL) trains large language models to interleave reasoning with external tool execution to solve complex tasks. Most existing ARL methods train a sin…