Papers by Weinan Zhang
Progra: Progress-Aware Reinforcement Learning for Multi-Turn Function Calling (2026.findings-acl)
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Huacan Chai, Zijie Cao, Maolin Ran, Yingxuan Yang, Jianghao Lin, Xin Peng, Hairui Wang, Renjie Ding, Ziyu Wan, Muning Wen, Weiwen Liu, Weinan Zhang, Fei Huang, Ying Wen
| Challenge: | Existing methods for multi-turn function calling are limited by redundancy and lack explicit integration of progress awareness into training. |
| Approach: | They propose a framework that explicitly integrates progress awareness into LLM training for multi-turn function calling. |
| Outcome: | Empirical results show that Progra outperforms existing methods on two public benchmarks. |
NL-Debugging: Exploiting Natural Language as an Intermediate Representation for Code Debugging (2025.emnlp-main)
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Weiming Zhang, Qingyao Li, Xinyi Dai, Jizheng Chen, Kounianhua Du, Weiwen Liu, Yasheng Wang, Ruiming Tang, Yong Yu, Weinan Zhang
| Challenge: | Early debugging efforts focused on code-level analysis, which often fails when addressing complex programming errors. |
| Approach: | They propose a framework that employs natural language as an intermediate representation to improve code debugging by debuggating at a natural language level. |
| Outcome: | The proposed framework outperforms traditional debugging methods and enables a broader modification space through direct refinement guided by execution feedback. |
SelF-Eval: Self-supervised Fine-grained Dialogue Evaluation (2022.coling-1)
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| Challenge: | Existing evaluation metrics are expensive and easy to conduct but ineffective to reflect dialogue quality. |
| Approach: | They propose a self-supervised fine-grained dialogue evaluation framework which can automatically assign fine-granular scores for arbitrarily dialogue data. |
| Outcome: | The proposed framework is highly consistent with human evaluations and better than the state-of-the-art models. |
Exploring Diverse Expressions for Paraphrase Generation (D19-1)
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| Challenge: | Existing neural paraphrase generation methods focus on single paraphrases while ignoring the fact that diversity is essential for enhancing generalization capability and robustness of downstream applications. |
| Approach: | They propose a novel approach with two discriminators and multiple generators to generate a variety of different paraphrases. |
| Outcome: | The proposed model gains significant diversity and improves quality over state-of-the-art datasets. |
Stimulate the Critical Thinking of LLMs via Debiasing Discussion (2025.emnlp-main)
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| Challenge: | Existing studies show that large language models (LLMs) are often prone to stance homogeneity and human preference biases when faced with conflicting perspectives. |
| Approach: | They propose a novel two-stage training framework to address stance homogeneity bias and human preference bias by generating multi-model discussion datasets and optimizing reinforcement learning from human feedback to align with discussion correctness. |
| Outcome: | The proposed framework reduces stance homogeneity bias and human preference bias and improves generalization capabilities on non-discussion scenarios and out-of-domain datasets. |
Unraveling and Mitigating Retriever Inconsistencies in Retrieval-Augmented Large Language Models (2024.findings-acl)
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| Challenge: | Retrieval-Augmented Large Language Models (RALMs) do not consistently outperform the original retrieval-free Language Model (LM). |
| Approach: | They propose a trainable framework that can adaptively retrieve from different knowledge sources and effectively decrease unpredictable reader errors. |
| Outcome: | The proposed framework significantly improves performance over the RALM with a single retriever by significantly reducing inconsistent behaviors. |
DebateCoder: Towards Collective Intelligence of LLMs via Test Case Driven LLM Debate for Code Generation (2025.acl-long)
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Jizheng Chen, Kounianhua Du, Xinyi Dai, Weiming Zhang, Xihuai Wang, Yasheng Wang, Ruiming Tang, Weinan Zhang, Yong Yu
| Challenge: | Existing debate-based approaches to code generation are limited due to several reasons: 1) Reliance on different instances of the same LLM for debate, 2) under-utilization of test cases, and 3) reliance on third-party moderators for result consolidation and decision-making. |
| Approach: | They propose to use test cases to analyze code and identify bugs while opposing models generate test cases for each other to challenge each other's code during the debate process. |
| Outcome: | The proposed model collects intelligence of LLMs via test case-driven debate for code generation. |
PAEG: Phrase-level Adversarial Example Generation for Neural Machine Translation (2022.coling-1)
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| Challenge: | Existing methods for adversarial example generation are word-level or character-level, which ignore the ubiquitous phrase structure. |
| Approach: | They propose a phrase-level adversarial example generation framework to enhance the robustness of the translation model by adopting a sentence-level substitution strategy. |
| Outcome: | The proposed method improves translation performance and robustness to noise on three benchmarks. |
Leveraging Dual Process Theory in Language Agent Framework for Real-time Simultaneous Human-AI Collaboration (2025.acl-long)
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Shao Zhang, Xihuai Wang, Wenhao Zhang, Chaoran Li, Junru Song, Tingyu Li, Lin Qiu, Xuezhi Cao, Xunliang Cai, Wen Yao, Weinan Zhang, Xinbing Wang, Ying Wen
| Challenge: | Large language models (LLMs) excel in turn-by-turn human-AI collaboration but struggle with simultaneous tasks requiring real-time interaction. |
| Approach: | They propose a language agent framework that integrates *System 1* and *System 2* for efficient real-time simultaneous human-AI collaboration. |
| Outcome: | The proposed framework improves on existing LLM-based agents and human collaborators by integrating Theory of Mind and asynchronous reflection to infer human intentions and perform reasoning-based autonomous decisions. |
Zero Pronoun Resolution with Attention-based Neural Network (C18-1)
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| Challenge: | Recent neural network methods for zero pronoun resolution use contextual information to encode the zero pronomins since they contain no actual content. |
| Approach: | They propose a self-attention mechanism for encoding zero pronouns that focus on some informative parts of the associated texts and produce an efficient way of encode them. |
| Outcome: | The proposed model significantly surpasses existing Chinese zero pronoun resolution baseline systems. |
CLIP Models are Few-Shot Learners: Empirical Studies on VQA and Visual Entailment (2022.acl-long)
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| Challenge: | Previously, CLIP was only regarded as a powerful visual encoder. |
| Approach: | They propose a parameter-efficient fine-tuning strategy to boost CLIP's few-shot performance on a visual entailment task without introducing any additional pre-training procedure. |
| Outcome: | The proposed strategy achieves competitive zero/few-shot results on visual question answering and visual entailment tasks without introducing any additional pre-training procedure. |
Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space (2020.findings-emnlp)
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| Challenge: | Existing uncertainty sampling methods are time-consuming and can't be executed frequently. |
| Approach: | They propose adversarial uncertainty sampling in discrete space to find informative unlabeled text samples for annotation using adversarials. |
| Outcome: | The proposed approach outperforms baselines on effectiveness on five datasets. |
Harnessing the Power of Large Language Models for Empathetic Response Generation: Empirical Investigations and Improvements (2023.findings-emnlp)
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| Challenge: | Empathetic dialogue is an essential part of building harmonious social relationships and contributes to the development of a helpful AI. |
| Approach: | They propose three methods to improve the performance of large language models (LLMs) they propose semantically similar in-context learning, two-stage interactive generation and combination with the knowledge base. |
| Outcome: | The proposed methods achieve state-of-the-art in automatic and human evaluations and the possibility of GPT-4 simulating human evaluators. |
Label-Aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition (N18-1)
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Zhenghui Wang, Yanru Qu, Liheng Chen, Jian Shen, Weinan Zhang, Shaodian Zhang, Yimei Gao, Gen Gu, Ken Chen, Yong Yu
| Challenge: | NER is a fundamental problem for medical text mining because of the difference of specialties and cost of human annotation. |
| Approach: | They propose a label-aware double transfer learning framework for medical NER from electronic medical records. |
| Outcome: | The proposed framework improves accuracy over strong baselines on 12 cross-specialty NER tasks. |
HammerBench: Fine-Grained Function-Calling Evaluation in Real Mobile Assistant Scenarios (2025.findings-acl)
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Jun Wang, Jiamu Zhou, Xihuai Wang, Xiaoyun Mo, Haoyu Zhang, Qiqiang Lin, Jincheng Jincheng, Muning Wen, Weinan Zhang, Qiuying Peng, Jun Wang
| Challenge: | Evaluating the performance of LLMs in multi-turn interactions presents significant challenges due to the complexity and variability of user behavior. |
| Approach: | They propose a benchmark framework for assessing LLMs’ function-calling capabilities in multi-turn dialogues. |
| Outcome: | The proposed framework is based on a dataset derived from popular mobile apps and anonymized user logs. |
ToolPRM: Fine-Grained Inference Scaling of Structured Outputs for Function Calling (2026.acl-long)
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Jianghao Lin, Yuanyuan Shi, Xin Peng, Renjie Ding, Hairui Wang, Yuxuan Peng, Bizhe Bai, Weixi Song, Fengshuo Bai, Huacan Chai, Weinan Zhang, Fei Huang, Ying Wen
| Challenge: | Existing research on inference scaling focuses on unstructured output generation tasks, such as mathematical problems. |
| Approach: | They propose an inference-scaling framework that combines fine-grained beam search with ToolPRM, a process reward model scoring each intra-call decision. |
| Outcome: | The proposed framework outperforms outcome and coarse-grained reward models in predictive accuracy and yields consistent test-time gains on multiple function-calling benchmarks. |
Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales Dialogue (2023.findings-emnlp)
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Yuanxing Liu, Weinan Zhang, Yifan Chen, Yuchi Zhang, Haopeng Bai, Fan Feng, Hengbin Cui, Yongbin Li, Wanxiang Che
| Challenge: | E-commerce pre-sales dialogues elicit user needs and preferences for items . large language models lack domain-specific knowledge for accurate recommendations . |
| Approach: | They propose two collaboration strategies to integrate CRS and large language models in pre-sales dialogues. |
| Outcome: | The proposed methods can be very effective in some cases, the authors say . |
Boost, Disentangle, and Customize: A Robust System2-to-System1 Pipeline for Code Generation (2025.findings-acl)
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Kounianhua Du, Hanjing Wang, Jianxing Liu, Jizheng Chen, Xinyi Dai, Yasheng Wang, Ruiming Tang, Yong Yu, Jun Wang, Weinan Zhang
| Challenge: | Existing systems 2 methods for code generation are difficult to implement due to the complex hidden reasoning process and heterogeneous data distribution. |
| Approach: | They propose a framework that Boosts reasoning exploration via multi-agent collaboration and Disentangles heterogeneous data into specialized experts. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on APPS and CodeContest benchmarks and achieves 73.8% accuracy on hard problems. |
Attribution-Based Analysis and Optimization of Modular Agentic Workflows (2026.findings-acl)
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Yingxuan Yang, Bo Huang, Siyuan Qi, Chao Feng, Haoyi Hu, Yuxuan Zhu, Jinbo Hu, Haoran Zhao, Ziyi He, Xiao Liu, ZongYu Wang, Muning Wen, Lin Qiu, Xuezhi Cao, Xunliang Cai, Yong Yu, Weinan Zhang
| Challenge: | Large Language Models (LLMs) have driven the rise of agentic workflows . yet, how can we attribute performance gains to individual upgrades and their interactions? |
| Approach: | They propose a game-theoretic framework that models component upgrades as players and evaluates component coalitions to compute Shapley values. |
| Outcome: | The proposed framework provides interaction-aware attribution and recommendation for model allocation under a fixed workflow structure. |
Nested Named Entity Recognition with Span-level Graphs (2022.acl-long)
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| Challenge: | Named entity recognition is one of the major subtasks of information extraction for extracting categorized named entities from unstructured text. |
| Approach: | They propose to use retrieval-based span-level graphs to connect spans and entities in the training data based on n-gram features to integrate information of similar neighbor entities into the span representation. |
| Outcome: | The proposed method achieves general improvements on all three benchmarks and special superiority on low frequency entities. |
FAER: Benchmarking VLMs for Failure-Aware Embodied Reasoning (2026.findings-acl)
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| Challenge: | Visual-language models (VLMs) are the core component of embodied agents in perceiving the environment and making decisions. |
| Approach: | They propose a failure-aware benchmark to evaluate the performance of visual language models (VLMs) in long-horizon tasks. |
| Outcome: | The proposed benchmark evaluates the performance of 16 widely utilized VLMs and 4 LLMs for FAER tasks. |
Agent-Dice: Disentangling Knowledge Updates via Geometric Consensus for Agent Continual Learning (2026.findings-acl)
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| Challenge: | Large Language Model (LLM)-based agents extend the utility of LLMs by interacting with dynamic environments. |
| Approach: | They propose a parameter fusion framework based on directional consensus evaluation that disentangles knowledge updates through a two-stage process. |
| Outcome: | The proposed framework disentangles knowledge updates through a two-stage process with minimal computational overhead and parameter updates. |
Prove Your Point!: Bringing Proof-Enhancement Principles to Argumentative Essay Generation (2024.emnlp-main)
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| Challenge: | Existing arguments generation methods often overlook connections between opinions . Existing methods struggle with providing compelling proof . |
| Approach: | They propose a two-stage framework for argumentative essay generation with a focus on logical enhancement. |
| Outcome: | The proposed framework generates argumentative essays with better logical validity and persuasiveness than baseline models. |
RethinkMCTS: Refining Erroneous Thoughts in Monte Carlo Tree Search for Code Generation (2025.emnlp-main)
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Qingyao Li, Wei Xia, Xinyi Dai, Kounianhua Du, Weiwen Liu, Yasheng Wang, Ruiming Tang, Yong Yu, Weinan Zhang
| Challenge: | Existing tree search methods neglect the underlying reasoning process, resulting in poor search quality. |
| Approach: | They propose a framework that systematically explores and refines the reasoning process for code generation by using a tree search engine and a reflection mechanism. |
| Outcome: | The proposed framework outperforms existing methods in the code generation domain. |
Context-Sensitive Generation of Open-Domain Conversational Responses (C18-1)
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| Challenge: | Existing studies on single-turn conversation generation focus on coherence and context-sensitive generation of open-domain conversational responses. |
| Approach: | They propose static and dynamic attention based approaches for context-sensitive generation of open-domain conversational responses. |
| Outcome: | The proposed model outperforms all baselines on automatic and human evaluation on two public datasets. |
CodePRM: Execution Feedback-enhanced Process Reward Model for Code Generation (2025.findings-acl)
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| Challenge: | Recent advances in code generation focus on optimizing the thought process, but lack effective process supervision, making it difficult to optimize the thoughts. |
| Approach: | They propose a method that leverages the code execution feedback to build a code PRM by collecting a large dataset of thought traces and then training it to take both the reasoning process and code execution as input. |
| Outcome: | The proposed approach outperforms baselines and strong LLMs in the inference stage. |
Learning Logic Rules for Document-Level Relation Extraction (2021.emnlp-main)
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| Challenge: | Existing models for document-level relation extraction relied on implicitly powerful representations, which makes the model less transparent. |
| Approach: | They propose a probabilistic model for document-level relation extraction by learning logic rules. |
| Outcome: | The proposed model outperforms baseline models in relation performance and logical consistency. |
Dynamically Fused Graph Network for Multi-hop Reasoning (P19-1)
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| Challenge: | Text-based question answering (TBQA) has been studied extensively in recent years. |
| Approach: | They propose a Dynamically Fused Graph Network to answer questions requiring multiple scattered evidence and reasoning over them. |
| Outcome: | The proposed method achieves competitive results on a public TBQA dataset and produces interpretable reasoning chains. |
Glancing Transformer for Non-Autoregressive Neural Machine Translation (2021.acl-long)
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| Challenge: | Existing non-autoregressive neural machine translation methods are either inferior to Transformer or require multiple decoding passes, leading to reduced speedup. |
| Approach: | They propose a Glancing Language Model (GLM) for single-pass parallel generation models and Glancing Transformer (GLAT) with only single- pass decoding, GLAT is able to generate high-quality translation with 8-15 speedup. |
| Outcome: | The proposed model outperforms all previous non-autoregressive methods on multiple language directions and is nearly comparable to Transformer. |
PaSa: An LLM Agent for Comprehensive Academic Paper Search (2025.acl-long)
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| Challenge: | We introduce PaSa, an advanced Paper Search agent powered by large language models . despite being trained on synthetic data, PaSA outperforms existing baselines on RealScholarQuery . |
| Approach: | They introduce PaSa, an advanced Paper Search agent powered by large language models . they optimize PaSA using a synthetic dataset, AutoScholarQuery, which includes 35k fine-grained queries . |
| Outcome: | The paper analyzes the performance of a paper search agent using a synthetic dataset . it significantly outperforms existing benchmarks on RealScholarQuery . |
Retrieval-Augmented Process Reward Model for Generalizable Mathematical Reasoning (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have advanced mathematical reasoning, but they still struggle with out-of-distribution (OOD) issues. |
| Approach: | They propose a framework to evaluate the logical validity of reasoning steps . they retrieves semantically similar questions and steps for PRM as a warmup . |
| Outcome: | The proposed framework outperforms baseline models on multiple real-world datasets. |
CoreCodeBench: Decoupling Code Intelligence via Fine-Grained Repository-Level Tasks (2026.acl-long)
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Lingyue Fu, Hao Guan, Bolun Zhang, Haowei Yuan, Yaoming Zhu, Lin Qiu, ZongYu Wang, Xuezhi Cao, Xunliang Cai, Weiwen Liu, Weinan Zhang, Yong Yu
| Challenge: | Existing large language models for software engineering rely on coarse-grained pass rates obscuring specific cognitive bottlenecks. |
| Approach: | They propose a repository-level benchmark that dissects coding capabilities through atomized tasks. |
| Outcome: | The proposed framework achieves a 78.55% validity yield, surpassing the 31.7% retention rate of SWE-bench-Verified. |
A Survey of Large Language Model-Based Search Agents (2026.acl-long)
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Yunjia Xi, Jianghao Lin, Yongzhao Xiao, Zheli Zhou, Rong Shan, Te Gao, Jiachen Zhu, Weiwen Liu, Yong Yu, Weinan Zhang
| Challenge: | Large Language Models (LLMs) have revolutionized web search, but their integration is static and cannot handle complex contexts. |
| Approach: | They analyze existing research and analyze existing work from the perspectives of architecture, optimization, application, and evaluation. |
| Outcome: | The proposed models can comprehend user intentions and context and execute multi-turn retrieval with dynamic planning, extending search capabilities far beyond the web. |
A Comprehensive Survey of Process Reward Models: Data Generation, Model Construction, and Usage (2026.acl-long)
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Congmin Zheng, Jiachen Zhu, Zhuoying Ou, Yuxiang Chen, Kangning Zhang, Rong Shan, Zeyu Zheng, Mengyue Yang, Jianghao Lin, Yong Yu, Weinan Zhang
| Challenge: | Large Language Models (LLMs) have advanced reasoning ability, yet conventional alignment remains dominated by outcome reward models that judge only final answers. |
| Approach: | They summarize applications across math, code, text, multimodal reasoning, robotics, and agents . goal is to clarify design spaces, reveal open challenges, and guide future research toward fine-grained, robust reasoning alignment. |
| Outcome: | The proposed model enables finer credit assignment, richer diagnostics, and improved robustness. |
ColorBrowserAgent: Complex Long-Horizon Browser Agent with Adaptive Knowledge Evolution (2026.acl-industry)
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Jihong Wang, Jiamu Zhou, Weiming Zhang, Teng Wang, Weiwen Liu, Zhuosheng Zhang, Xingyu Lou, Weinan Zhang, Huarong Deng, Jun Wang
| Challenge: | Xue et al., 2025): deploying autonomous web agents in production remains difficult due to site heterogeneity and long-horizon instability. |
| Approach: | They propose a knowledge-evolving agent that can be used to automate web workflows . they use human-in-the-loop knowledge adaptation and knowledge-aligned progressive summarization . |
| Outcome: | Experiments on WebArena, WebChoreAren and industrial deployment show it outperforms baselines. |
Nullspace Disentanglement for Red Teaming Language Models (2025.emnlp-main)
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| Challenge: | Existing work mainly leverages crowd workers to construct test cases. |
| Approach: | They propose a black-box approach that exploits the unique properties of the nullspace to disentangle and regulate the crucial success information within test cases. |
| Outcome: | The proposed approach outperforms baseline methods regarding the attack success rate and excels in aspects of diversity and fluency. |
TeachMaster: Generative Teaching via Code (2026.acl-industry)
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Yuheng Wang, Runde Yang, Lin Wu, Jie Zhang, Jingru Fan, Tianle Zhou, Ruoyu Fu, Huatao Li, Ruijie Shi, Siheng Chen, Weinan E, Chen Qian
| Challenge: | Existing methods for creating video content are limited by high costs and slow update cycles. |
| Approach: | They propose a paradigm shifting educators from manual creators to high-level directors who focus on pedagogical intents while agents handle execution. |
| Outcome: | The proposed framework reduces production costs to 0.3% of traditional course videos and provides a robust solution for scalable education. |
Exploring and Distilling Multi-Dimensional Clues for Interpretable Social Bot Detection (2026.acl-long)
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| Challenge: | Existing research on social bot detection results directly without corresponding supportive explanations, making it difficult to assess the extent to which such predictions are trustworthy. |
| Approach: | They propose a four-dimensional clue framework that uses outcome-reward reinforcement learning to train inspectors to generate faithful, grounded clues from user information, semantic features, interactive situation, and behavioral pattern. |
| Outcome: | The proposed framework outperforms baselines in detection performance and significantly improves the performance of large language models. |
ACE-Router: Generalizing History-Aware Routing from MCP Tools to the Agent Web (2026.acl-long)
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Zhiyuan Yao, Zishan Xu, Yifu Guo, Zhiguang Han, Cheng Yang, Shuo Zhang, Weinan Zhang, Xingshan Zeng, Weiwen Liu
| Challenge: | Existing routers that use hardcoded tools are limited by scalability and generality bottlenecks. |
| Approach: | They propose a pipeline for training history-aware routers to empower precise navigation in large-scale ecosystems. |
| Outcome: | The proposed pipeline can train routers with dynamic context understanding to create the plug-and-play Light Routing Agent. |
LoopTool: Closing the Data–Training Loop for Robust LLM Tool Calls (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) are powerful tools for multi-step tasks, but static data pipelines hinder tool learning and cause noisy labels to persist. |
| Approach: | They propose a fully automated, model-aware data evolution framework that tightly integrates data synthesis and model training. |
| Outcome: | Experiments show that LoopTool-8B significantly surpasses its 32B data generator and achieves new state-of-the-art results on the BFCL-v3 and ACEBench benchmarks for its scale. |