Papers by Yutao Zhu

R^3AG: Retriever Routing for Retrieval-Augmented Generation (2026.acl-long)

Challenge:	Large language models (LLMs) are expected to be trained to act as agents in various real-world environments, but access to real systems is often restricted and manually built sandboxes are hard to scale.
Approach:	They propose an automated framework for scalable tool-interaction environments via programmatic synthesis that synthesizes 191 environments and about 7K scenarios and applies them to Supervised Fine-Tuning and Reinforcement Learning for Qwen3 series models.
Outcome:	The proposed framework significantly improves LLMs’ ability to solve tasks in complex environments involving multi-turn, multi-tool interactions.

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Tong Zhao, Yutao Zhu, Yucheng Tian, Zhicheng Dou

Hence, Socrates is mortal: A Benchmark for Natural Language Syllogistic Reasoning (2023.findings-acl)

Challenge:	Retrieval-augmented generation (RAG) is often bottlenecked by the “one-size-fits-all” retrieval paradigm, as different queries exhibit distinct preferences for different retrievers.
Approach:	They propose a novel routing framework that explicitly models the dynamic alignment between queries and retriever capabilities and decomposes retriever capability into two learnable dimensions: retrieval quality and generation utility.
Outcome:	Experiments on knowledge-intensive tasks show that R3AG outperforms both the best individual retrievers and state-of-the-art static routing methods.

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Yongkang Wu, Meng Han, Yutao Zhu, Lei Li, Xinyu Zhang, Ruofei Lai, Xiaoguang Li, Yuanhang Ren, Zhicheng Dou, Zhao Cao

FinSight: Towards Real-World Financial Deep Research (2026.acl-long)

Challenge:	SylloBase is a benchmark for syllogistic reasoning, a critical capability widely required in natural language understanding tasks, such as text entailment and question answering.
Approach:	They propose to use a benchmark to learn syllogistic reasoning on a set of templates and to use them to generate and understand slogisms.
Outcome:	The proposed benchmark covers a complete taxonomy of syllogism reasoning patterns, and contains both automatically and manually constructed samples.

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Jiajie Jin, Yuyao Zhang, Yimeng Xu, Hongjin Qian, Yutao Zhu, Zhicheng Dou

INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning (2024.acl-long)

Challenge:	FinSight is the first multi-agent framework for automating end-to-end professional, multimodal financial reports.
Approach:	They propose a code agent with variable memory architecture that unifies data, tools, and agents into a programmable variable space.
Outcome:	The proposed framework outperforms leading deep research systems in factual accuracy, analytical depth, and presentation quality.

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Yutao Zhu, Peitian Zhang, Chenghao Zhang, Yifei Chen, Binyu Xie, Zheng Liu, Ji-Rong Wen, Zhicheng Dou

BIDER: Bridging Knowledge Inconsistency for Efficient Retrieval-Augmented LLMs via Key Supporting Evidence (2024.findings-acl)

Challenge:	Large language models (LLMs) have demonstrated impressive capabilities in various natural language processing tasks, but their application to information retrieval tasks is still challenging due to the infrequent occurrence of many IR-specific concepts in natural language.
Approach:	They propose to use instruction tuning to enhance LLMs' proficiency in IR tasks by combining a dataset with manually written templates to analyze the effects of instruction design, template diversity, few-shot demonstrations, and the volume of instructions.
Outcome:	The proposed model can be used to perform query understanding, document understanding, and query-document relationship understanding tasks.

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Jiajie Jin, Yutao Zhu, Yujia Zhou, Zhicheng Dou

Green & Sustainable NLP

Challenge:	Large language models (LLMs) have demonstrated efficacy in knowledge-intensive tasks such as open-domain QA, but inconsistencies between retrieval knowledge and the necessary knowledge for LLMs, leading to a decline in LLM’s answer quality.
Approach:	They propose a retrieval-augmented large language model that refines retrieval documents into Key Supporting Evidence (KSE) through knowledge synthesis, supervised fine-tuning, and preference alignment.
Outcome:	The proposed model outperforms existing methods in five datasets and boosts answer quality by 7% while reducing input content length by 80%.

Joint Semantic and Strategy Matching for Persuasive Dialogue (2023.findings-emnlp)

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Chuhao Jin, Yutao Zhu, Lingzhen Kong, Shijie Li, Xiao Zhang, Ruihua Song, Xu Chen, Huan Chen, Yuchong Sun, Yu Chen, Jun Xu

Dialogue Systems & Conversational Agents

Challenge:	Persuasive dialogue models rely on utterance semantic matching and a key aspect has been ignored . compared with utterrance semantics, conversation strategies are high-level concepts, which can be informative and provide complementary information to achieve effective persuation.
Approach:	They propose to model conversation semantics and strategies to match them using a BERT-like module and an auto-regressive predictor.
Outcome:	The proposed model improves state-of-the-art by 5% on a small and 37% on 'large' datasets.

ReasonRank: Empowering Passage Ranking with Strong Reasoning Ability (2026.acl-long)

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Wenhan Liu, Xinyu Ma, Weiwei Sun, Yutao Zhu, Yuchen Li, Dawei Yin, Zhicheng Dou

Hierarchical Document Refinement for Long-context Retrieval-augmented Generation (2025.acl-long)

Challenge:	Existing rerankers perform poorly in complex ranking scenarios due to the scarcity of reasoning-intensive training data.
Approach:	They propose an automated reasoning-intensive training framework which generates high-quality training labels from training queries and passages.
Outcome:	The proposed model outperforms baselines significantly and achieves much lower latency than the pointwise reranker.

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Jiajie Jin, Xiaoxi Li, Guanting Dong, Yuyao Zhang, Yutao Zhu, Yongkang Wu, Zhonghua Li, Ye Qi, Zhicheng Dou

Enhancing LLM Text Detection with Retrieved Contexts and Logits Distribution Consistency (2025.emnlp-main)

Challenge:	Real-world RAG applications often encounter long-context input scenarios where redundant information and noise results in higher inference costs and reduced performance.
Approach:	They propose an efficient plug-and-play refiner that leverages the structural characteristics of long documents.
Outcome:	Experiments on seven QA datasets show that LongRefiner achieves competitive performance in various scenarios while using 10x fewer computational costs and latency compared to baseline.

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Zhaoheng Huang, Yutao Zhu, Ji-Rong Wen, Zhicheng Dou

Search-o1: Agentic Search-Enhanced Large Reasoning Models (2025.emnlp-main)

Challenge:	Existing methods for detecting large language models (LLMs) generate fluent text, but they only use a few tokens due to the short length or insufficient information in some texts.
Approach:	They propose a method that leverages external text corpora to evaluate the difference in logit distribution of input text under retrieved human-written and LLM-rewritten contexts.
Outcome:	The proposed method achieves state-of-the-art performance in AUROC on five public datasets with three widely-used source LLMs.

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Xiaoxi Li, Guanting Dong, Jiajie Jin, Yuyao Zhang, Yujia Zhou, Yutao Zhu, Peitian Zhang, Zhicheng Dou

Agentic-R: Learning to Retrieve for Agentic Search (2026.findings-acl)

Challenge:	Large reasoning models (LRMs) have demonstrated impressive long stepwise reasoning capabilities through large-scale reinforcement learning.
Approach:	They propose a framework that enhances large reasoning models with an agentic retrieval-augmented generation mechanism and a Reason-in-Documents module for refining retrieved documents.
Outcome:	The proposed framework enhances LRMs with an agentic retrieval-augmented generation mechanism and Reason-in-Documents module for refining retrieved documents.

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Wenhan Liu, Xinyu Ma, Yutao Zhu, Yuchen Li, Daiting Shi, Dawei Yin, Zhicheng Dou

YuLan-Mini: Pushing the Limits of Open Data-efficient Language Model (2025.acl-long)

Challenge:	Existing retrievers for single-turn retrieval-augmented generation (RAG) rely on similarity-based retrievers, but similar passages are not always useful for final answer generation.
Approach:	They propose a retrieval-augmented-generation retriever that integrates reasoning with retrieval . they use local query-passage relevance and global answer correctness to measure passage utility .
Outcome:	The proposed retriever outperforms existing retrievers on QA benchmarks on seven single-hop and multi-hop searches.

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Hu Yiwen, Huatong Song, Jie Chen, Jia Deng, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Zican Dong, Yang Lu, Xu Miao, Xin Zhao, Ji-Rong Wen

Language Models

Challenge:	prevailing pre-training approaches for large language models involve several complexities.
Approach:	They propose a low-cost training recipe and a robust optimization approach to mitigate training instability . they also propose synthesis, curriculum, and data selection pipelines to integrate data .
Outcome:	The proposed model achieves top-tier performance among models with similar parameter scale . it is comparable to industry-leading models that require significantly more data .

Sliding Windows Are Not the End: Exploring Full Ranking with Long-Context Large Language Models (2025.acl-long)

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Wenhan Liu, Xinyu Ma, Yutao Zhu, Ziliang Zhao, Shuaiqiang Wang, Dawei Yin, Zhicheng Dou

Web Sitemap Knowledge Can Enhance Autonomous Browsing (2026.findings-acl)

Challenge:	Existing methods for listwise passage ranking use sliding window approach, which is inefficient as it requires repetitive and serialized processing.
Approach:	They propose a listwise label construction approach and importance-aware learning objective for full ranking.
Outcome:	The proposed method outperforms existing methods in listwise ranking tasks.

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Yuyao Zhang, Hongyu Lu, Jiajie Jin, Hongjin Qian, Shiyu Li, Zhao Yang, Yutao Zhu, Ji-Rong Wen, Zhicheng Dou

Responsible & Trustworthy NLP

Challenge:	Existing web agents suffer from limited robustness, efficiency and task success due to lack of structural understanding of websites and lack of browsing priors in pre-trained models.
Approach:	They propose an agent-oriented sitemap protocol that integrates structured website knowledge into web agents.
Outcome:	The proposed agent-oriented sitemap improves robustness, efficiency and effectiveness without extra training.

RAG-Critic: Leveraging Automated Critic-Guided Agentic Workflow for Retrieval Augmented Generation (2025.acl-long)

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Guanting Dong, Jiajie Jin, Xiaoxi Li, Yutao Zhu, Zhicheng Dou, Ji-Rong Wen

Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs (2024.acl-long)

Challenge:	Recent advances in large language models (LLMs) have demonstrated remarkable performance across a wide range of downstream tasks.
Approach:	They propose a framework that leverages a critic-guided agentic workflow to improve RAG capabilities autonomously.
Outcome:	The proposed framework improves RAG capabilities autonomously by leveraging a critic-guided agentic workflow.

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Jiejun Tan, Zhicheng Dou, Yutao Zhu, Peidong Guo, Kun Fang, Ji-Rong Wen

Single LLM, Multiple Roles: A Unified Retrieval-Augmented Generation Framework Using Role-Specific Token Optimization (2025.emnlp-main)

Challenge:	Existing methods to determine the knowledge an LLM already possesses and the knowledge that requires the help of a search engine are expensive and require excessive computational costs.
Approach:	They propose a slim proxy model that detects missing knowledge in LLMs with a proxy model and use it to perform retrieval for the missing knowledge.
Outcome:	The proposed approach detects missing knowledge in LLMs with a slim proxy model and takes its answers as heuristic answers.

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Yutao Zhu, Jiajie Jin, Hongjin Qian, Zheng Liu, Zhicheng Dou, Ji-Rong Wen

RLSeek: Evidence-Grounded Reasoning for RAG Hallucination Detection (2026.acl-long)

Challenge:	Existing studies have optimized retrieval-augmented generation (RAG) across sub-tasks, but integrating these optimizations into a unified framework remains challenging.
Approach:	They propose a unified retrieval-augmented generation framework that optimizes role-specific tokens for multi-task processing.
Outcome:	The proposed framework achieves efficient multi-task processing through role-specific token optimization.

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Zhaoheng Huang, Dacheng Wen, Yutao Zhu, Xiaoying Lian, Yushi Liang, Kai Hao, Nan Li, Liangjie Zhang, Qi Zhang, Ji-Rong Wen, Zhicheng Dou, Fangzhao Wu

ConvGQR: Generative Query Reformulation for Conversational Search (2023.acl-long)

Challenge:	Recent work addresses this problem by training span-level hallucination detectors using reinforcement learning and chain-of-thought reasoning.
Approach:	They propose a framework that explicitly enforces active evidence seeking during CoT reasoning by requiring quotation of relevant source segments at each verification step.
Outcome:	The proposed framework improves hallucination span detection performance with limited reasoning overhead and improved robustness in out-of-domain settings.

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Fengran Mo, Kelong Mao, Yutao Zhu, Yihong Wu, Kaiyu Huang, Jian-Yun Nie

Natural Language Interfaces

Challenge:	Existing methods to determine a good search query from the whole conversation context are expensive and often lead to sub-optimal results.
Approach:	They propose a framework to reformulate conversational queries based on generative pre-trained language models (PLMs) they propose generative knowledge infusion mechanism to optimize query reformulation and retrieval.
Outcome:	Extensive experiments on four conversational search datasets demonstrate the effectiveness of ConvGQR.

Towards Effective and Efficient Continual Pre-training of Large Language Models (2025.acl-long)

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Jie Chen, Zhipeng Chen, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Yingqian Min, Xin Zhao, Zhicheng Dou, Jiaxin Mao, Yankai Lin, Ruihua Song, Jun Xu, Xu Chen, Rui Yan, Zhewei Wei, Di Hu, Wenbing Huang, Ji-Rong Wen

Language Models

Challenge:	Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks.
Approach:	They propose a Continual pre-training method that can greatly improve Chinese language ability and scientific reasoning ability of LLMs.
Outcome:	The proposed method can greatly improve Chinese language ability and scientific reasoning ability of LLMs.

Coarse-to-Fine: Hierarchical Multi-task Learning for Natural Language Understanding (2022.coling-1)

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Zhaoye Fei, Yu Tian, Yongkang Wu, Xinyu Zhang, Yutao Zhu, Zheng Liu, Jiawen Wu, Dejiang Kong, Ruofei Lai, Zhao Cao, Zhicheng Dou, Xipeng Qiu

Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generation (2022.naacl-main)

Challenge:	Existing methods to learn downstream tasks by stitches skill block lack rationality and interpretation.
Approach:	They propose a hierarchical framework with a coarse-to-fine paradigm for generalized text representations from the large-scale corpus.
Outcome:	The proposed model learns basic language properties from all tasks and boosts performance on relevant tasks.

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Hanxun Zhong, Zhicheng Dou, Yutao Zhu, Hongjin Qian, Ji-Rong Wen

Dialogue Systems & Conversational Agents

Challenge:	Existing personalized dialogue systems extract user profiles from dialogue history to guide personalized response generation.
Approach:	They propose to refine the user dialogue history on a large scale to obtain more persona information from the dialogue history and leverage other similar users' data to enhance personalization.
Outcome:	The proposed model can handle more dialogue history and obtain more abundant and accurate persona information.

LLM-Generated Text May Harm Your Retrieval! A Robust Detection Strategy for Retrieval-Augmented Generation (2026.acl-long)

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Zhaoheng Huang, Yutao Zhu, Ji-Rong Wen, Zhicheng Dou

Responsible & Trustworthy NLP

Challenge:	Retrieval-augmented generation (RAG) improves accuracy and timeliness of large language models, but external corpora may become contaminated with LLM-generated texts.
Approach:	They propose a method that integrates external knowledge retrieved from external sources into RAG to filter out LLM-generated texts from retrieved results.
Outcome:	The proposed method mitigates performance degradation and improves stability of RAG systems.

MCP: Self-supervised Pre-training for Personalized Chatbots with Multi-level Contrastive Sampling (2022.findings-emnlp)

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Zhaoheng Huang, Zhicheng Dou, Yutao Zhu, Zhengyi Ma

Dialogue Systems & Conversational Agents

Challenge:	Existing studies focus on generating implicit user profiles from the user’s dialogue history, thus it suffers from data sparsity and performance degradation.
Approach:	They propose a self-supervised learning framework MCP for capturing better representations from users’ dialogue history for personalized chatbots.
Outcome:	The proposed model improves on two real-world datasets.

Little Giants: Synthesizing High-Quality Embedding Data at Scale (2025.naacl-long)

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Haonan Chen, Liang Wang, Nan Yang, Yutao Zhu, Ziliang Zhao, Furu Wei, Zhicheng Dou

Progressive Multimodal Reasoning via Active Retrieval (2025.acl-long)

Challenge:	Synthetic data generation is an increasingly popular way of training models without the need for large, manually labeled datasets.
Approach:	They propose a framework that aligns open-source small models to efficiently generate large-scale embedding data.
Outcome:	The proposed framework outperforms state-of-the-art embedding models by using only 1/10 of the GPT API calls.

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Guanting Dong, Chenghao Zhang, Mengjie Deng, Yutao Zhu, Zhicheng Dou, Ji-Rong Wen

Memory Matters More: Event-Centric Memory as a Logic Map for Agent Searching and Reasoning (2026.findings-acl)

Challenge:	Existing approaches to improve multimodal large language models' reasoning performance are limited.
Approach:	They propose a framework to progressively improve multimodal reasoning capabilities . they propose active retrieval and Monte Carlo tree search to improve MLLMs' reasoning .
Outcome:	The proposed framework improves multimodal reasoning capabilities in multimodal large language models.

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Yuyang Hu, Jiongnan Liu, Jiejun Tan, Yutao Zhu, Zhicheng Dou

RichRAG: Crafting Rich Responses for Multi-faceted Queries in Retrieval-Augmented Generation (2025.coling-main)

Challenge:	Existing methods for storing and retrieving memory are limited by shallow semantic retrieval.
Approach:	They propose a memory mechanism that organizes and retrieves past experiences to support decision-making.
Outcome:	Experiments on LoCoMo and NarrativeQA show that CompassMem improves retrieval and reasoning performance across multiple backbone models.

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Shuting Wang, Xin Yu, Mang Wang, Weipeng Chen, Yutao Zhu, Zhicheng Dou

ATIR: Towards Audio-Text Interleaved Contextual Retrieval (2026.acl-long)

Challenge:	Existing studies focus on question scenarios with clear user intents and concise answers, but it is prevalent that users issue broad, open-ended queries with diverse sub-intents.
Approach:	They propose a framework that includes a sub-aspect explorer and a multi-faceted retriever to build a candidate pool of diverse external documents related to these sub-intents.
Outcome:	The proposed framework provides comprehensive and satisfying responses to users on two publicly available datasets.

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Tong Zhao, Chenghao Zhang, Yutao Zhu, Zhicheng Dou

LLMs + Persona-Plug = Personalized LLMs (2025.acl-long)

Challenge:	Recent multimodal information retrieval research has focused on images, largely overlooking audio.
Approach:	They propose an audio-text interleaved contextual retrieval task where queries can alternate between audio and text modalities.
Outcome:	The proposed model significantly improves over baselines.

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Jiongnan Liu, Yutao Zhu, Shuting Wang, Xiaochi Wei, Erxue Min, Yu Lu, Shuaiqiang Wang, Dawei Yin, Zhicheng Dou

mmE5: Improving Multimodal Multilingual Embeddings via High-quality Synthetic Data (2025.findings-acl)

Challenge:	Large language models (LLMs) have demonstrated extraordinary capabilities in natural language understanding, generation, and reasoning.
Approach:	They propose a plug-and-play LLM model that embeds a user-specific embedding for each individual by modeling her historical contexts through a lightweight plug-in user embedder module.
Outcome:	Experiments on various tasks in the language model personalization (LaMP) benchmark show that the proposed model significantly outperforms existing personalized LLM approaches.

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Haonan Chen, Liang Wang, Nan Yang, Yutao Zhu, Ziliang Zhao, Furu Wei, Zhicheng Dou