8 citations · 8 across the 14 of their papers we have counts for
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cs.MA2026
GitHarness: Git Init Your Harness Working Memory for Perpetual User Requirements
Zhibang Yang, Xinke Jiang, Yuxuan Liu +9
LLM-based agents increasingly collaborate with users on long-horizon tasks, accumulating evidence, code, and drafts through extensive search, reasoning, and execution. As users ins…
cs.MA2026
Harness-RL: Black-Box Reinforcement Learning with Action-Args Decoupling for Central-Agent Multi-Agent Harnesses
Xinke Jiang, Zhixin Zhang, Zhibang Yang +6
Large language model agents increasingly solve long-horizon tasks through multi-agent harnesses in which a central agent coordinates specialized sub-agents, tools, and environments…
cs.MA2026
AgenticRag-R1: Agentic Reinforcement Learning with Stack Memory for Multi-Step Reasoning, Retrieval and Memorizing
Xinke Jiang, Yue Fang, Zhibang Yang +12
Retrieval-Augmented Generation (RAG) improves the factuality of large language models (LLMs), yet existing RAG systems often struggle with complex, multi-step reasoning that requir…