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…
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…
On Synthetic Data Strategies for Domain-Specific Generative Retrieval
Haoyang Wen, Jiang Guo, Yi Zhang +2
This paper investigates synthetic data generation strategies in developing generative retrieval models for domain-specific corpora, thereby addressing the scalability challenges in…
You Only Read Once (YORO): Learning to Internalize Database Knowledge for Text-to-SQL
Hideo Kobayashi, Wuwei Lan, Peng Shi +5
While significant progress has been made on the text-to-SQL task, recent solutions repeatedly encode the same database schema for every question, resulting in unnecessary high infe…
Towards a Holistic Evaluation of LLMs on Factual Knowledge Recall
Jiaqing Yuan, Lin Pan, Chung-Wei Hang +5
Large language models (LLMs) have shown remarkable performance on a variety of NLP tasks, and are being rapidly adopted in a wide range of use cases. It is therefore of vital impor…