Papers by Shouzheng Huang
ToolOmni: Enabling Open-World Tool Use via Agentic learning with Proactive Retrieval and Grounded Execution (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for large language models struggle to align user intent with tool semantics or generalize to unseen tools. |
| Approach: | They propose a framework that enables LLMs for open-world tool use by proactive retrieval and grounded execution within a reasoning loop. |
| Outcome: | The proposed framework surpasses baselines in retrieval and execution efficiency by +10.8%. |
Learning to Extract Rational Evidence via Reinforcement Learning for Retrieval-Augmented Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | Retrieval-Augmented Generation (RAG) is effective in Large Language Models (LLMs). However, retrieval noises undermine the quality of LLMs’ generation, necessitating the development of denoising mechanisms. |
| Approach: | They propose a model which integrates reasoning and extracting into one unified trajectory, followed by knowledge token masking to avoid information leakage. |
| Outcome: | Extensive experiments on five benchmark datasets show the superiority of EviOmni, which provides compact and high-quality evidence, enhances the accuracy of downstream tasks, and supports both traditional and agentic RAG systems. |