2 citations · 2 across the 4 of their papers we have counts for
6 papers
EvoBelief: Falsifiable World Models for Self-Improving LLM Agents
Janet Wang, Jiang Guo, Yi Xiang +6
Self-evolving LLM agents typically improve how they act through accumulated experience, reflection, or skills. Yet even procedurally capable agents can fail when their beliefs abou…
LEGO-Anything: Coding Agents for 3D Scene Reconstruction
Xirui Li, Peng Shi, Mingwen Dong +7
A 3D scene reconstructed from a single image is most useful when represented not as a rendering or a fixed 3D output, but as an explicit scene program whose execution yields a scen…
Poison Once, Exploit Forever: Environment-Injected Memory Poisoning Attacks on Web Agents
Wei Zou, Mingwen Dong, Miguel Romero Calvo +7
Memory makes LLM-based web agents personalized, powerful, yet exploitable. By storing past interactions to personalize future tasks, agents inadvertently create a persistent attack…
R-WoM: Retrieval-augmented World Model For Computer-use Agents
Kai Mei, Jiang Guo, Shuaichen Chang +4
Large Language Models (LLMs) can serve as world models to enhance agent decision-making in digital environments by simulating future states and predicting action outcomes, potentia…
PRACTIQ: A Practical Conversational Text-to-SQL dataset with Ambiguous and Unanswerable Queries
Mingwen Dong, Nischal Ashok Kumar, Yiqun Hu +9
Previous text-to-SQL datasets and systems have primarily focused on user questions with clear intentions that can be answered. However, real user questions can often be ambiguous w…
Propagation and Pitfalls: Reasoning-based Assessment of Knowledge Editing through Counterfactual Tasks
Wenyue Hua, Jiang Guo, Mingwen Dong +3
Current approaches of knowledge editing struggle to effectively propagate updates to interconnected facts. In this work, we delve into the barriers that hinder the appropriate prop…