With the popularization of Electronic Health Records (EHR), the emergency system has stored a large number of historical dispatch records, which can provide valuable insights for the optimization of current pre-hospital care. However, the inconvenient interaction manner of cur-rent information retrieval systems hinders researchers from exploring these historical records. To address this issue, we propose a novel framework that leverages the language understanding and code generation ability of Large Language Models (LLMs) to build an information retrieval system with Visualization-oriented Natural-language-based Inter-faces (V-NLI). To incorporate both domain-specific and task-related prior knowledge, we generate the instruction datasets based on the ability of closed-source LLMs in a multi-stage manner and conduct supervised fine-tuning on open-source LLMs. We also devised various mechanisms for augmenting the capabilities of open-source LLMs in query interpretation and code generation. To validate the effectiveness and generalizability of our framework, we conducted experiments on a public dataset NLV. More significantly, we performed more detailed experiments on a dataset including over 1 mil-lion pre-hospital emergency historical records in ten years. The performance of our method surpasses all baseline methods and achieves comparable results even with some SOTA closed-source models.
Xin Gao, Zheng-Ye Zhu, Xin-Yu Ma et al.· Tsinghua Science and Technol...· 0 citations
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. Training the central policy in such a harness raises two challenges. First, an action label is a low-cardinality decision, whereas its args form a high-dimensional conditional sequence; optimizing both with a shared sequence-level signal can produce conflicting gradients. Second, dynamic scheduling creates interdependent sessions with branches, parallel calls, and rewritten contexts, which cannot be faithfully reduced to one flat token sequence. We introduce Harness-RL, a structured reinforcement learning framework that combines Conflict-Aware Policy Optimization (CAPO) with interface-level black-box trajectory construction. The black-box component captures Interface Call Records, builds per-session prefix trees, and aligns outcome and process rewards with trainable tokens. CAPO uses forward activations to identify parameter partitions associated with action and args tokens, then routes their policy gradients to the corresponding subspaces. Harness-RL supports both central-only and joint multi-agent training. Across seven multi-hop question answering and agentic retrieval benchmarks, it reaches average F1 scores of 42.93 and 47.79 with Qwen2.5-1.5B and Qwen2.5-3B, respectively, while ablations validate the contribution of CAPO and favor central-only optimization in the evaluated setting. Our code is available at https://github.com/jiangxinke/Harness-RL.
Xinke Jiang, Zhixin Zhang, Zhibang Yang et al.· 0 citations
Retrieval-Augmented Generation (RAG) improves the factuality of large language models (LLMs), yet existing RAG systems often struggle with complex, multi-step reasoning that requires adaptive retrieval and continuous revision of intermediate contexts. Recent reinforcement learning (RL)-based agentic RAG methods partially alleviate this issue, but typically rely on coarse-grained action spaces and trajectory-level rewards, resulting in weak reward assignment and a bias toward short-horizon, stereotyped reasoning template. To address, we propose AgenticRag-R1, a RL framework that deeply integrates reasoning, retrieval, and memory via a memory stack and fine-grained action space, supported by hierarchical action-aware rewards and an information-aware trajectory rejection strategy to enable effective long-horizon learning. Experiments across a diverse set of multi-hop, open-domain, and agentic reasoning benchmarks, spanning multiple backbone model sizes, demonstrate that AgenticRag-R1 consistently outperforms strong baselines. Moreover, AgenticRag-R1 learns more robust, interpretable, and memory-aware reasoning behaviors, highlighting the effect of fine-grained action modeling and information-aware optimization for long-horizon reasoning. Our code is anonymous available at https://github.com/jiangxinke/Harness-RL/tree/AgenticRAG-R1-Whitebox.
Xinke Jiang, Yue Fang, Zhibang Yang et al.· 1 citation
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