6 citations · 6 across the 35 of their papers we have counts for
40 papers
StartLux-Decision: AutoThink Decision Models with Reinforcement Learning for Calibrated Deliberative Decisions
Xialie Zhuang, Zunhai Su, Jia Pan +3
We introduce StartLux-Decision, decision models from 0.8B to 27B parameters that combine efficient single-pass prediction with adaptive, confidence-guided reasoning. The Instant mo…
Before the Arrest: Benchmarking LLMs on Criminal Profiling from Incomplete Evidence
Yutong Yao, Yanjie Cao, Guanhua Chen +5
Large Language Models (LLMs) are increasingly applied to legal and criminal justice tasks, yet existing work focuses almost exclusively on post-arrest scenarios where the suspect's…
JustMem: Just-Enough Memory Access for Long-Term Conversations
Guanhua Chen, Yanting Wang, Wenjing Zhi +1
Efficient long-term conversational memory requires retrieving sufficient evidence without indiscriminately expanding the context presented to the language model. This is challengin…
ROAM: Robust Organization of Atomic Memories for Agents through Semantic Relations
Jianjie Zheng, Peng Lai, Sijie Cheng +3
Long-term language-model agents rely on external memory across interactions. Atomic memories are particularly useful: their fine-grained semantic boundaries enable precise retrieva…
NutriBench-Kitchen: Benchmarking Embodied AI for Nutrition Management
Yulin Wei, Xiangchen Wang, Jianhui Pan +5
An embodied kitchen assistant must do more than recognize food in isolated frames. It must track ingredient states over time and integrate visual observations with recipe and nutri…
UniRRM: Unified Reasoning Reward Models Across Languages and Evaluation Paradigms
Peng Lai, Yichao Du, Junchao Wu +6
Reinforcement learning (RL) excels on tasks with verifiable rewards, but in open-ended tasks, the reliability of reward models remains a key challenge. Existing solutions either de…
AlignDiff: Exploiting Model-Intrinsic Information for Better Preference Data Selection
Peng Lai, He Zhu, Zhiwen Ruan +6
Aligning large language models with human preferences remains a challenge, primarily due to the critical role of preference data quality in effective alignment. Existing datasets a…
Making Clinical Language Models Auditable: Concept-Guided Fine-Tuning for Robust Prediction
Jin Mu, Guanhua Chen
Clinical language models can achieve strong in-hospital accuracy yet fail under deployment shifts because they exploit note-specific artifacts (e.g., templates, separators, boilerp…
Fisher-R1: Training LLM Agents for Reliable Hypothesis Testing
Jiacheng Miao, Jin Mu, Guanhua Chen +1
Reliable hypothesis testing is the foundation of many empirical scientific claims. Large language model (LLM) agents are increasingly used to automate this process, as they can ins…
"Allow" to Achieve, Over-Privileged Inadvertently: The Unintended Cost of Task-Completion-Driven Pop-up Decisions in Mobile GUI Agents
Dongsheng Chen, Yuxuan Li, Guanhua Chen +5
Mobile GUI agents routinely encounter system permission dialogs during task execution, yet their ability to grant only permissions that are necessary for the delegated task remains…
APPO: Agentic Procedural Policy Optimization
Xucong Wang, Ziyu Ma, Yong Wang +5
Recent advances in agentic Reinforcement Learning (RL) have substantially improved the multi-turn tool-use capabilities of large language model agents. However, most existing metho…
Beyond Static Rules: Automated Discovery of Latent Vulnerabilities in Text-to-SQL
Hanqing Wang, Yongdong Chi, Jian Yang +4
While Large Language Models (LLMs) have achieved remarkable success in Text-to-SQL tasks, their deployment in real-world environments is hindered by latent reliability issues. Iden…
StatABench: Dataset and Framework for Evaluating Statistical Analysis Capabilities of LLMs
Youxin Zhu, Yixuan Ding, Peng Lai +3
Statistical analysis is a broad, complex field requiring both domain knowledge and tool proficiency. While prior work has evaluated large language models (LLMs) in this domain, exi…
Timage: A Generative Text-in-Image Paradigm for Fine-Tuning Vision-Language Models
Yifeng Wu, Huimin Huang, Ruiluo Wu +5
Multimodal Large Language Models (MLLMs) often lose track of the right image regions during fine-grained spatial reasoning, because a textual query rarely carries any explicit geom…
Bridging the Agent-World Gap: Text World Models for LLM-based Agents
Yixia Li, Hongru Wang, Peng Lai +13
Large language model (LLM)-based agents are increasingly used in interactive textual environments, from web navigation and code editing to tool use and long-horizon dialogue. Yet m…
CAPruner: Conceptual-Adjacent Scene Graph Pruner for Enhancing 3D Spatial Reasoning of Large Language Models
Shengli Zhou, Xiangchen Wang, Guanhua Chen +1
Large language models (LLMs) have recently been applied to 3D vision-language (3D-VL) tasks, which require spatial reasoning to identify target objects relative to anchors. Scene g…
Semantically Structured Mixture-of-Experts for Compositional Robotic Manipulation
Chengyu Deng, Guanqi Chen, Yizhou Chen +4
Diffusion-based policies have established a new standard for precise robotic manipulation but face a critical scalability bottleneck: high-performance models are computationally ex…
CaptchaMind: Training CAPTCHA Solvers via Reinforcement Learning with Explicit Reasoning Supervision
Pengcheng Wang, Haoxiang Liu, Yang Dai +5
CAPTCHAs are widely deployed as human verification mechanisms and frequently block intelligent agents from completing end-to-end automation in real-world web environments. Solving…
Trust No Tool: Evaluating and Defending LLM Agents under Untrusted Tool Feedback
Lecheng Yan, Ruizhe Li, Xicheng Han +5
Tool-using LLM agents increasingly rely on external tools to make consequential decisions, yet most existing agent-security benchmarks and defenses implicitly assume that tool feed…
From Scenes to Elements: Multi-Granularity Evidence Retrieval for Verifiable Multimodal RAG
Guanhua Chen, Chuyue Huang, Yutong Yao +4
Multimodal Retrieval-Augmented Generation (RAG) systems retrieve evidence at coarse granularities (entire images or scenes), creating a mismatch with fine-grained user queries and…
Chain-of-Procedure: Hierarchical Visual-Language Reasoning for Procedural QA
Guanhua Chen, Yutong Yao, Shenghe Sun +5
Recent advances in vision-language models (VLMs) have achieved impressive results on standard image-text tasks, yet their potential for visual procedure question answering (VP-QA)…
Learning Agentic Policy from Action Guidance
Yuxiang Ji, Zengbin Wang, Yong Wang +6
Agentic reinforcement learning (RL) for Large Language Models (LLMs) critically depends on the exploration capability of the base policy, as training signals emerge only within its…
Robust LLM Unlearning Against Relearning Attacks: The Minor Components in Representations Matter
Zeguan Xiao, Xuanzhe Xu, Yun Chen +5
Large language model (LLM) unlearning aims to remove specific data influences from pre-trained model without costly retraining, addressing privacy, copyright, and safety concerns.…
CulturALL: Benchmarking Multilingual and Multicultural Competence of LLMs on Grounded Tasks
Peiqin Lin, Chenyang Lyu, Wenjiang Luo +22
Large language models (LLMs) are now deployed worldwide, inspiring a surge of benchmarks that measure their multilingual and multicultural abilities. However, these benchmarks prio…