87 citations · 160 across the 49 of their papers we have counts for
51 papers
Governance at the Boundary: How Agent Decomposition Degrades Policy Compliance
Bowen Li, Guojun Wang
Existing agent benchmarks ask whether the agent finished the task. We ask whether it finished it within policy. We introduce Fiducia-bench, a benchmark for the governability of fin…
FocusMem: Factorizing Content, Readout, and Trust in Latent GUI Memory
Zhuoran Zhang, Bowen Li, Jingcheng Ju +5
GUI agents must remember both useful experience from earlier tasks and unfinished progress in the current interaction. Latent memory offers a compact solution by compressing multim…
Rethinking Scientific Discovery in the Agentic Era
Yining Zheng, Yuxin Wang, Jiahao Lu +27
Artificial intelligence has advanced scientific discovery, but most AI4Science systems remain fragmented tools that rely on humans to coordinate problem formulation, literature gro…
Answer Presence Drives RAG Rewriting Gains
Yuejie Li, Yueying Hua, Ke Yang +7
Retrieval-augmented QA pipelines often route retrieved passages through an LLM \emph{rewriter} before a smaller reader, lifting F1 by tens of points on multi-hop benchmarks; this g…
HEART-Bench: Do LLM Agents Exhibit Human-like Psychology?
Weihan Peng, Chenxu Zhang, Qianao Wang +7
While LLM agents have demonstrated remarkable task-oriented abilities such as planning, reasoning, and action, few works have treated them as complete human personalities where emo…
Kernel-Smith: A Unified Recipe for Evolutionary Kernel Optimization
He Du, Qiming Ge, Jiakai Hu +18
We present Kernel-Smith, a framework for high-performance GPU kernel and operator generation that combines a stable evaluation-driven evolutionary agent with an evolution-oriented…
Beyond Rating: A Comprehensive Evaluation and Benchmark for AI Reviews
Bowen Li, Haochen Ma, Yuxin Wang +5
The rapid adoption of Large Language Models (LLMs) has spurred interest in automated peer review; however, progress is currently stifled by benchmarks that treat reviewing primaril…
TREX: Automating LLM Fine-tuning via Agent-Driven Tree-based Exploration
Zerun Ma, Guoqiang Wang, Xinchen Xie +7
While Large Language Models (LLMs) have empowered AI research agents to perform isolated scientific tasks, automating complex, real-world workflows, such as LLM training, remains a…
How to Fine-Tune a Reasoning Model? A Teacher-Student Cooperation Framework to Synthesize Student-Consistent SFT Data
Zixian Huang, Kaichen Yang, Xu Huang +6
A widely adopted strategy for model enhancement is to use synthetic data generated by a stronger model for supervised fine-tuning (SFT). However, for emerging reasoning models like…
Attention-guided Evidence Grounding for Spoken Question Answering
Ke Yang, Bolin Chen, Yuejie Li +5
Spoken Question Answering (Spoken QA) presents a challenging cross-modal problem: effectively aligning acoustic queries with textual knowledge while avoiding the latency and error…
The Missing Adapter Layer for Research Computing
Bowen Li, Jiazhu Xie, Chelsea Wang +3
Higher Degree by Research (HDR) candidates increasingly depend on cloud-provisioned virtual machines and local GPU hardware for their computational experiments, yet a persistent an…
RepoLaunch: Automating Build and Management of Code Repositories across Languages and Platforms
Kenan Li, Rongzhi Li, Linghao Zhang +17
Language model (LM) agents have driven substantial progress in automated software engineering (SWE), yet building and testing software repositories at scale remains a largely manua…
Spiking Graph Predictive Coding for Reliable OOD Generalization
Jing Ren, Jiapeng Du, Bowen Li +6
Graphs provide a powerful basis for modeling Web-based relational data, with expressive GNNs to support the effective learning in dynamic web environments. However, real-world depl…
Copyright Detective: A Forensic System to Evidence LLMs Flickering Copyright Leakage Risks
Guangwei Zhang, Jianing Zhu, Cheng Qian +12
We present Copyright Detective, the first interactive forensic system for detecting, analyzing, and visualizing potential copyright risks in LLM outputs. The system treats copyrigh…
When to Trust: A Causality-Aware Calibration Framework for Accurate Knowledge Graph Retrieval-Augmented Generation
Jing Ren, Bowen Li, Ziqi Xu +3
Knowledge Graph Retrieval-Augmented Generation (KG-RAG) extends the RAG paradigm by incorporating structured knowledge from knowledge graphs, enabling Large Language Models (LLMs)…
Deep GraphRAG: A Balanced Approach to Hierarchical Retrieval and Adaptive Integration
Yuejie Li, Ke Yang, Tao Wang +3
Graph-based Retrieval-Augmented Generation (GraphRAG) frameworks face a trade-off between the comprehensiveness of global search and the efficiency of local search. Existing method…
Securing LLM-as-a-Service for Small Businesses: An Industry Case Study of a Distributed Chatbot Deployment Platform
Jiazhu Xie, Bowen Li, Heyu Fu +3
Large Language Model (LLM)-based question-answering systems offer significant potential for automating customer support and internal knowledge access in small businesses, yet their…
When to Invoke: Refining LLM Fairness with Toxicity Assessment
Jing Ren, Bowen Li, Ziqi Xu +6
Large Language Models (LLMs) are increasingly used for toxicity assessment in online moderation systems, where fairness across demographic groups is essential for equitable treatme…
Debiasing Large Language Models via Adaptive Causal Prompting with Sketch-of-Thought
Bowen Li, Ziqi Xu, Jing Ren +5
Despite notable advancements in prompting methods for Large Language Models (LLMs), such as Chain-of-Thought (CoT), existing strategies still suffer from excessive token usage and…
EvoSyn: Generalizable Evolutionary Data Synthesis for Verifiable Learning
He Du, Bowen Li, Aijun Yang +3
Reliable verifiable data has become a key driver of capability gains in modern language models, enabling stable reinforcement learning with verifiable rewards and effective distill…
Confidence as a Reward: Transforming LLMs into Reward Models
He Du, Bowen Li, Chengxing Xie +3
Reward models can significantly enhance the reasoning capabilities of large language models (LLMs), but they typically require extensive curated data and costly training. To mitiga…
Revolutionizing Reinforcement Learning Framework for Diffusion Large Language Models
Yinjie Wang, Ling Yang, Bowen Li +3
We propose TraceRL, a trajectory-aware reinforcement learning framework for diffusion language models (DLMs) that incorporates preferred inference trajectory into post-training, an…
Loong: Synthesize Long Chain-of-Thoughts at Scale through Verifiers
Xingyue Huang, Rishabh, Gregor Franke +43
Recent advances in Large Language Models (LLMs) have shown that their reasoning capabilities can be significantly improved through Reinforcement Learning with Verifiable Reward (RL…
Causal Prompting for Implicit Sentiment Analysis with Large Language Models
Jing Ren, Wenhao Zhou, Bowen Li +7
Implicit Sentiment Analysis (ISA) aims to infer sentiment that is implied rather than explicitly stated, requiring models to perform deeper reasoning over subtle contextual cues. W…