Papers by Long Jiao
WebClipper: Efficient Evolution of Web Agents with Graph-based Trajectory Pruning (2026.acl-long)
Copied to clipboard
Junjie Wang, Zequn Xie, Dan Yang, Jie Feng, Yue Shen, Duolin Sun, Meixiu Long, Yihan Jiao, Zhehao Tan, Jian Wang, Peng Wei, Jinjie Gu
| Challenge: | Open-source web agents rely on long tool-call trajectories with cyclic reasoning loops and exploration of unproductive branches. |
| Approach: | They propose a framework that compresses web agent trajectories via graph-based pruning. |
| Outcome: | The proposed framework reduces tool-call rounds by 20% while improving accuracy and efficiency while maintaining the same level of performance as existing models. |
GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs (2026.findings-acl)
Copied to clipboard
Meixiu Long, Duolin Sun, Dan Yang, Yihan Jiao, Lei Liu, Jiahai Wang, Binbin Hu, Yue Shen, Jie Feng, Zhehao Tan, Junjie Wang, Lianzhen Zhong, Jian Wang, Peng Wei, Jinjie Gu
| Challenge: | Existing rankers excel in lexical-matching scenarios, while they struggle with complex queries requiring deep reasoning. |
| Approach: | They propose a new paradigm that balances flexibility and context awareness to unlock the full potential of groupwise reranking. |
| Outcome: | The proposed approach achieves a state-of-the-art 65.2 NDCG@10 on BRIGHT and surpasses baselines by 2.1 points on R2MED while delivering a 6.4 inference speedup. |
Ro-SLM: Onboard Small Language Models for Robot Task Planning and Operation Code Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in large language models (LLMs) provide robots with contextual reasoning abilities to comprehend human instructions. |
| Approach: | They propose a framework that enables reliable SLM-driven robot operation by distilling LLMs’ knowledge and reasoning. |
| Outcome: | The proposed framework enables reliable SLM-driven robot operation by distilling LLMs’ knowledge and reasoning. |
Towards Robust Ranker for Text Retrieval (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for text retrieval are based on a 'retrieval & rerank' pipeline, which uses a fast retriever to fetch a set of top document candidates, while a robust ranker is based upon a weak negative mining during contrastive learning. |
| Approach: | They propose a multi-adversarial training strategy that leverages multiple retrievers as generators to challenge a ranker. |
| Outcome: | The proposed model outperforms the existing de facto ranker training paradigms on the passage retrieval benchmarks using BM25-reranking, full-ranking and retriever distillation. |