collaborators

12 papers

cs.AI2026

AttriMem: Attribution-Guided Process Feedback for Agent Memory Construction

Qinfeng Li, Yuntai Bao, Xinyan Yu +8

Effective memory is crucial for LLM agents, yet constructing it effectively remains challenging. A memory-construction policy decides what information to extract, store, update, co…

cs.LG2026

Mitigating Manifold Departure: Uncertainty-Aware Subspace Rectification for Trustworthy MLLM Decoding

Yingxuan Zhuang, Jingxiao Yang, Miao Pan +7

MLLMs frequently hallucinate objects inconsistent with visual inputs. This issue is typically attributed to the over-reliance on language priors, which can override the visual cont…

cs.CV2026

VITAL: Visual-Semantic Dual Supervision for Enhanced and Interpretable Latent Reasoning in Medical MLLMs

Qiaoru Li, Shaotian Liang, Jintao Chen +4

Latent reasoning enables reasoning over continuous hidden states rather than explicit tokens, avoiding the language bottleneck and inference overhead of chain-of-thought for medica…

cs.CR2026

PragLocker: Protecting Agent Intellectual Property in Untrusted Deployments via Non-Portable Prompts

Qinfeng Li, Yuntai Bao, Jianghui Hu +5

LLM agents rely on prompts to implement task-specific capabilities based on foundation LLMs, making agent prompts valuable intellectual property. However, in untrusted deployments,…

cs.AI2026

GFT: From Imitation to Reward Fine-Tuning with Unbiased Group Advantages and Dynamic Coefficient Rectification

Wangjie Gan, Miao Pan, Linbo Xi +4

Large language models are typically post-trained using supervised fine-tuning (SFT) and reinforcement learning (RL), yet effectively unifying efficient knowledge injection with rob…

cs.SD2026

AST: Adaptive, Seamless, and Training-Free Precise Speech Editing

Sihan Lv, Yechen Jin, Zhen Li +5

Text-based speech editing aims to modify specific segments while preserving speaker identity and acoustic context. Current approaches generally involve either expensive task-specif…