13 papers
VibeMemBench: Evaluating Memory Systems for Coding Agents on Real Repository Coding Tasks
Liyang Fan, Yingcheng Shi, Yongbin Li +7
Coding agents operate on real repository coding tasks, and persistent memory systems promise to reuse experience across tasks. Yet existing evaluations do not show whether those sy…
HNR-DAC: Hard-Negative Reranking and Distribution-Aligned Classification for Scientific Claim Verification
Zhenchao Wang, Xin Chen, Luoxi Zhang +2
Scientific claim verification over a cited paper requires predicting the claim--paper relation and identifying the paragraphs that justify that prediction. This setting poses two l…
SrDetection: A Self-Referential Framework for Data Leakage Detection in Code Large Language Models
Shuaimin Li, Liyang Fan, Zeyang Li +9
Evaluating code large language models (Code LLMs) requires reliable detection of data leakage, where benchmark performance is artificially inflated by exposure to benchmark data du…
EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning
Guhong Chen, Yingcheng Shi, Yongbin Li +6
Autonomous LLM training is often framed as recipe search, which leaves the training harness largely static. This limitation sharpens in agentic RL, where shifting bottlenecks and s…
Beyond Quantity: Trajectory Diversity Scaling for Code Agents
Guhong Chen, Chenghao Sun, Cheng Fu +16
As code large language models (LLMs) evolve into tool-interactive agents via the Model Context Protocol (MCP), their generalization is increasingly limited by low-quality synthetic…
Probing the Difficulty Perception Mechanism of Large Language Models
Sunbowen Lee, Qingyu Yin, Chak Tou Leong +5
Large language models (LLMs) are increasingly deployed on complex reasoning tasks, yet little is known about their ability to internally evaluate problem difficulty, which is an es…
Quantification of Large Language Model Distillation
Sunbowen Lee, Junting Zhou, Chang Ao +11
Model distillation is a fundamental technique in building large language models (LLMs), transferring knowledge from a teacher model to a student model. However, distillation can le…
xJailbreak: Representation Space Guided Reinforcement Learning for Interpretable LLM Jailbreaking
Sunbowen Lee, Shiwen Ni, Chi Wei +7
Safety alignment mechanism are essential for preventing large language models (LLMs) from generating harmful information or unethical content. However, cleverly crafted prompts can…
Pre-training, Fine-tuning and Re-ranking: A Three-Stage Framework for Legal Question Answering
Shiwen Ni, Hao Cheng, Min Yang
Legal question answering (QA) has attracted increasing attention from people seeking legal advice, which aims to retrieve the most applicable answers from a large-scale database of…
Small Language Model as Data Prospector for Large Language Model
Shiwen Ni, Haihong Wu, Di Yang +3
The quality of instruction data directly affects the performance of fine-tuned Large Language Models (LLMs). Previously, \cite{li2023one} proposed \texttt{NUGGETS}, which identifie…
AutoPatent: A Multi-Agent Framework for Automatic Patent Generation
Qiyao Wang, Shiwen Ni, Huaren Liu +8
As the capabilities of Large Language Models (LLMs) continue to advance, the field of patent processing has garnered increased attention within the natural language processing comm…
Educational-Psychological Dialogue Robot Based on Multi-Agent Collaboration
Shiwen Ni, Min Yang
Intelligent dialogue systems are increasingly used in modern education and psychological counseling fields, but most existing systems are limited to a single domain, cannot deal wi…
VisPoison: An Effective Backdoor Attack Framework for Tabular Data Visualization Models
Shuaimin Li, Chen Jason Zhang, Xuanang Chen +7
Text-to-visualization (text-to-vis) models for tabular data have become essential tools in the era of big data, enabling users to generate visualizations and make data-driven decis…