3 citations · 11 across the 32 of their papers we have counts for
7 papers · 1 filter
LadderEdit: Edit-Level Residual Compression for Memory-Efficient Lifelong Editing of LLMs
Xiaobing Yu, Peijie Qiu, Jin Yang +5
Lifelong editing of LLMs requires storing thousands of edits after acquisition. A widely used family of approaches attaches one LoRA adapter per edit, which preserves behavior but…
TEFM: Token-Efficient Faithful Modeling for Structured Data
Zhichao Hou, Lingdao Sha, Xueyu Mao +3
In this paper, we solve two fundamental obstacles in applying LLMs to critical domains: token efficiency and faithfulness. To address both constraints jointly, we present TEFM (Tok…
Your Agent is More Brittle Than You Think: Uncovering Indirect Injection Vulnerabilities in Agentic LLMs
Wenhui Zhu, Xuanzhao Dong, Xiwen Chen +7
The rapid deployment of open-source frameworks has significantly advanced the development of modern multi-agent systems. However, expanded action spaces, including uncontrolled pri…
AriadneMem: Threading the Maze of Lifelong Memory for LLM Agents
Wenhui Zhu, Xiwen Chen, Zhipeng Wang +11
Long-horizon LLM agents require memory systems that remain accurate under fixed context budgets. However, existing systems struggle with two persistent challenges in long-term dial…
Toward Effective Reinforcement Learning Fine-Tuning for Medical VQA in Vision-Language Models
Wenhui Zhu, Xuanzhao Dong, Xin Li +6
Recently, reinforcement learning (RL)-based tuning has shifted the trajectory of Multimodal Large Language Models (MLLMs), particularly following the introduction of Group Relative…
DRA-GRPO: Your GRPO Needs to Know Diverse Reasoning Paths for Mathematical Reasoning
Xiwen Chen, Wenhui Zhu, Peijie Qiu +7
Post-training LLMs with Reinforcement Learning, specifically Group Relative Policy Optimization (GRPO), has emerged as a paradigm for enhancing mathematical reasoning. However, sta…
Talk Before You Retrieve: Agent-Led Discussions for Better RAG in Medical QA
Xuanzhao Dong, Wenhui Zhu, Hao Wang +5
Medical question answering (QA) is a reasoning-intensive task that remains challenging for large language models (LLMs) due to hallucinations and outdated domain knowledge. Retriev…