Papers by Runze Wang
ReviewRL: Towards Automated Scientific Review with RL (2025.emnlp-main)
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Sihang Zeng, Kai Tian, Kaiyan Zhang, Yuru Wang, Junqi Gao, Runze Liu, Sa Yang, Jingxuan Li, Xinwei Long, Jiaheng Ma, Biqing Qi, Bowen Zhou
| Challenge: | Existing automated review systems struggle with factual accuracy, rating consistency, and analytical depth. |
| Approach: | They propose a framework for generating comprehensive and factually grounded scientific paper reviews using supervised fine-tuning and reinforcement learning. |
| Outcome: | The proposed framework outperforms existing methods on ICLR 2025 papers. |
Learning Geometry-Aware Representations for New Intent Discovery (2024.acl-long)
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| Challenge: | Existing methods for intent classification fail to distinguish new intents due to intertwined centers . a novel framework that learns geometry-aware representations to maximally separate all intents is proposed . |
| Approach: | They propose a new intent discovery framework that learns geometry-aware representations to maximally separate all intents. |
| Outcome: | The proposed framework achieves a new state-of-the-art performance on three benchmarking datasets. |
CrowdAgent: Multi-Agent Managed Multi-Source Annotation System (2025.emnlp-demos)
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Maosheng Qin, Renyu Zhu, Mingxuan Xia, null Chenchenkai, Zhen Zhu, Minmin Lin, Junbo Zhao, Lu Xu, Changjie Fan, Runze Wu, Haobo Wang
| Challenge: | Recent approaches to annotate data focus on labeling, but lack holistic process control . a novel system that integrates task assignment, data annotation, and quality/cost management is needed . |
| Approach: | They propose a multi-agent system that integrates task assignment, data annotation, and quality/cost management. |
| Outcome: | The proposed system automates human management by using a collaborative multi-agent system. |
Joint Intent Detection and Entity Linking on Spatial Domain Queries (2020.findings-emnlp)
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| Challenge: | Spatial domain queries have unique properties making them more challenging for language understanding than common conversational queries. |
| Approach: | They propose a language understanding framework for spatial domain queries that jointly learns the intent detection and entity linking tasks on a voice assistant service. |
| Outcome: | The proposed framework outperforms baseline methods with a significant margin. |
Prompt Candidates, then Distill: A Teacher-Student Framework for LLM-driven Data Annotation (2025.acl-long)
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| Challenge: | Existing methods for data annotation use an aggressive approach prompting LLMs to determine a single gold label for each unlabeled sample. |
| Approach: | They propose a teacher-student framework that distills candidate annotations with a Small Language Model (SLM) they propose to use LLMs to generate and distill candidate annotation with slms to ensure unique labels are provided for downstream tasks. |
| Outcome: | The proposed method outperforms existing methods due to uncertainty in LLMs and is noisetolerant. |
PGPO: Enhancing Agent Reasoning via Pseudocode-style Planning Guided Preference Optimization (2025.findings-acl)
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| Challenge: | Existing LLM agents generate verbose and inefficient natural language plans to guide reasoning, which restricts agents’ ability to generalize across similar tasks. |
| Approach: | They propose a pseudocode-style planning guide optimization method that captures the structural logic of reasoning and uses two planning-oriented rewards to enhance agent learning. |
| Outcome: | The proposed method outperforms existing LLM agents on representative agent benchmarks and outperformed the current leading baselines. |
Towards Transferable Personality Representation Learning based on Triplet Comparisons and Its Applications (2025.emnlp-main)
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Kai Tang, Rui Wang, Renyu Zhu, Minmin Lin, Xiao Ding, Tangjie Lv, Changjie Fan, Runze Wu, Haobo Wang
| Challenge: | Existing methods for personality analysis treat corpus as a single unit for classification, but this approach presents several challenges. |
| Approach: | They propose a task paradigm for text-based personality representation learning that uses a triplet personality trend comparison dataset to learn single-sentence personality embeddings with desirable metric properties. |
| Outcome: | The proposed model significantly boosts performance across various applications, including personality detection, personality retrieval, and emotion translation prediction. |
CIKT: A Collaborative and Iterative Knowledge Tracing Framework with Large Language Models (2025.emnlp-main)
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| Challenge: | Knowledge Tracing (KT) aims to model a student’s learning state over time and predict their future performance. |
| Approach: | They propose a framework that harnesses Large Language Models to enhance both prediction accuracy and explainability by a synergistic optimization loop. |
| Outcome: | The proposed framework improves both prediction accuracy and explainability by using a synergistic optimization loop. |
McQueen: a Benchmark for Multimodal Conversational Query Rewrite (2022.emnlp-main)
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| Challenge: | Recent studies have focused on conversational-related tasks that involve drawing information from more than one modality. |
| Approach: | They propose a task of multimodal conversational query rewrite which performs query . they collect a large-scale visual conversation dataset and benchmark it against other tasks . |
| Outcome: | The proposed task performs on a large-scale visual conversation dataset . it eliminates coreference and ellipsis in the original query without changing its semantic information. |
Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization (2026.findings-acl)
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| Challenge: | Existing methods for designing and optimizing multi-agent systems are static and do not learn from experience. |
| Approach: | They propose a framework that enables a multi-agent system to learn to evolve . they use "textual gradients" to pinpoint failures and suggest granular modifications . |
| Outcome: | a new framework enables a multi-agent system to learn to evolve . it learns from historical optimization experiences to improve its performance . |
Graph of Trace: Visualizing Execution Traces of Scientific Agents (2026.acl-demo)
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| Challenge: | Scientific AI agents can perform complex research tasks, but these unfolded workflows are difficult for humans to inspect and review, limiting interpretable, controllable and effective human–AI collaboration. |
| Approach: | They propose a monitoring and visualization framework that records fine-grained execution events and organizes them into a directed graph that makes agent workflows explicit as they proceed. |
| Outcome: | The proposed framework records intermediate steps (e.g. tool calls and code executions) and renders them as real-time updated visual traces that expose workflow structure. |
Enabling Agents to Communicate Entirely in Latent Space (2026.acl-long)
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Zhuoyun Du, Runze Wang, Huiyu Bai, Zouying Cao, Xiaoyong Zhu, Yu Cheng, Bo Zheng, Wei Chen, Haochao Ying
| Challenge: | Natural language is the de facto communication medium for LLM-based agents, but it presents a fundamental constraint . natural language downsampling limits the depth and nuance of information that can be transmitted . et al.: inter-agent latent space communication is a promising paradigm for solving complex tasks . |
| Approach: | They propose a paradigm that leverages the last hidden states of an LLM as a representation of its thought for direct communication. |
| Outcome: | The proposed paradigm outperforms fine-tuned chain-of-thought prompting and single-agent baselines even across heterogeneous models. |
IRT-Router: Effective and Interpretable Multi-LLM Routing via Item Response Theory (2025.acl-long)
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| Challenge: | Large language models have demonstrated exceptional performance across a wide range of tasks . however, selecting the optimal LLM to respond to a user query often necessitates a delicate balance between performance and cost. |
| Approach: | They propose a multi-LLM routing framework that efficiently routes user queries to the most suitable LLM. |
| Outcome: | The proposed framework outperforms baseline methods in terms of effectiveness and interpretability. |
FreeAL: Towards Human-Free Active Learning in the Era of Large Language Models (2023.emnlp-main)
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| Challenge: | Modern machine learning models require a huge collection of precisely labeled data, which can be labor-intensive and time-consuming. |
| Approach: | They propose a collaborative learning framework that interactively distills and filters the task-specific knowledge from LLMs. |
| Outcome: | The proposed framework improves zero-shot performance on eight benchmark datasets without human supervision. |
CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite (2024.lrec-main)
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| Challenge: | Recent advances in conversational IR systems have seen a resurgent interest in conversation . generative query rewrite generates reconstructed query based on the conversation history . |
| Approach: | They propose to use unlabeled data to make further improvements using contrastive co-training paradigm. |
| Outcome: | The proposed model is robust to noise and language style shift under few-shot and zero-shot scenarios. |