5 citations · 6 across the 7 of their papers we have counts for
7 papers
The World Won't Stay Still: Programmable Evolution for Agent Benchmarks
Guangrui Li, Yaochen Xie, Yi Liu +11
LLM-powered tool-calling agents fulfill user requests by interacting with environments, querying data, and invoking tools in a multi-turn process. Yet, most existing benchmarks eva…
WEBSERV: A Full-Stack and RL-Ready Web Environment for Training Web Agents at Scale
Yuxuan Lu, Ziyi Wang, Jing Huang +10
Reinforcement learning (RL) for web agents demands environments that are both effective for evaluation and efficient enough for large-scale on-policy training. Current web environm…
Beyond Text: Unveiling Privacy Vulnerabilities in Multi-modal Retrieval-Augmented Generation
Jiankun Zhang, Shenglai Zeng, Jie Ren +4
Multimodal Retrieval-Augmented Generation (MRAG) systems enhance LMMs by integrating external multimodal databases, but introduce unexplored privacy vulnerabilities. While text-bas…
Unlocking Efficient, Scalable, and Continual Knowledge Editing with Basis-Level Representation Fine-Tuning
Tianci Liu, Ruirui Li, Yunzhe Qi +8
Large language models (LLMs) have achieved remarkable performance on various natural language tasks. However, they are trained on static corpora and their knowledge can become outd…
Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approach
Shenglai Zeng, Pengfei He, Kai Guo +4
Large Language Models (LLMs) enhanced with external contexts, such as through retrieval-augmented generation (RAG), often face challenges in handling imperfect evidence. They tend…
Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective
Shenglai Zeng, Jiankun Zhang, Bingheng Li +8
Retrieval-Augmented Generation (RAG) systems have shown promise in enhancing the performance of Large Language Models (LLMs). However, these systems face challenges in effectively…