9 citations · 12 across the 9 of their papers we have counts for
10 papers
Belief Cascades Drive Persuasion in LLM Agent Networks
Haoyi Qiu, Genglin Liu, Pranav Narayanan Venkit +4
Multi-agent LLM systems increasingly debate answers, coordinate research, simulate users, and mediate information flows, making agent-to-agent persuasion a basic but undermeasured…
Automatic Hard Example Synthesis with Multi-Level Agentic Data Curation
Genglin Liu, Muye Zhang, Krishnamurthy Viswanathan +5
Multimodal Large Language Models (MLLMs) are increasingly deployed for nuanced content safety and moderation tasks, yet they remain vulnerable to adversarial attacks and out-of-dis…
PM-Bench: Evaluating Prospective Memory in LLM Agents
Genglin Liu, Saadia Gabriel
A significant challenge in agentic AI is prospective memory: the ability to execute an intention at a specific future cue or state while other activities are ongoing. We introduce…
WebCoach: Self-Evolving Web Agents with Cross-Session Memory Guidance
Genglin Liu, Shijie Geng, Sha Li +4
Multimodal LLM-powered agents have recently demonstrated impressive capabilities in web navigation, enabling agents to complete complex browsing tasks across diverse domains. Howev…
AI Debate Aids Assessment of Controversial Claims
Salman Rahman, Sheriff Issaka, Ashima Suvarna +11
As AI grows more powerful, it will increasingly shape how we understand the world. But with this influence comes the risk of amplifying misinformation and deepening social divides-…
X-Teaming: Multi-Turn Jailbreaks and Defenses with Adaptive Multi-Agents
Salman Rahman, Liwei Jiang, James Shiffer +7
Multi-turn interactions with language models (LMs) pose critical safety risks, as harmful intent can be strategically spread across exchanges. Yet, the vast majority of prior work…
MOSAIC: Modeling Social AI for Content Dissemination and Regulation in Multi-Agent Simulations
Genglin Liu, Vivian Le, Salman Rahman +3
We present a novel, open-source social network simulation framework, MOSAIC, where generative language agents predict user behaviors such as liking, sharing, and flagging content.…
SciCode: A Research Coding Benchmark Curated by Scientists
Minyang Tian, Luyu Gao, Shizhuo Dylan Zhang +27
Since language models (LMs) now outperform average humans on many challenging tasks, it has become increasingly difficult to develop challenging, high-quality, and realistic evalua…
Examining LLMs' Uncertainty Expression Towards Questions Outside Parametric Knowledge
Genglin Liu, Xingyao Wang, Lifan Yuan +2
Can large language models (LLMs) express their uncertainty in situations where they lack sufficient parametric knowledge to generate reasonable responses? This work aims to systema…
Paxion: Patching Action Knowledge in Video-Language Foundation Models
Zhenhailong Wang, Ansel Blume, Sha Li +5
Action knowledge involves the understanding of textual, visual, and temporal aspects of actions. We introduce the Action Dynamics Benchmark (ActionBench) containing two carefully d…