5 papers
MemComposer: Learning to Assemble Memories for LLM Agents via Reinforcement Learning
Yixuan Xie, Xueqiang Xu, Xinyi Fan +8
Agent memory enables large language model (LLM) agents to reuse past experiences without updating their parameters. However, semantic similarity alone does not guarantee memory use…
Scaling Automatic Research Agents via World Models
Xiyuan Yang, Sheikh Sarwar, Jingru Cheng +8
Automating empirical research is a long-standing direction of AI. Recent automatic research (AutoResearch) agents bring this goal within reach, as modern LLMs show the capability t…
Analyzing and Internalizing Complex Policy Documents for LLM Agents
Jiateng Liu, Zhenhailong Wang, Xiaojiang Huang +6
Large Language Model (LLM)-based agentic systems rely on in-context policy documents encoding diverse business rules. As requirements grow, these documents expand rapidly, causing…
Multimodal Policy Internalization for Conversational Agents
Zhenhailong Wang, Jiateng Liu, Amin Fazel +6
Modern conversational agents like ChatGPT and Alexa+ rely on predefined policies specifying metadata, response styles, and tool-usage rules. As these LLM-based systems expand to su…
WINELL: Wikipedia Never-Ending Updating with LLM Agents
Revanth Gangi Reddy, Tanay Dixit, Jiaxin Qin +7
Wikipedia, a vast and continuously consulted knowledge base, faces significant challenges in maintaining up-to-date content due to its reliance on manual human editors. Inspired by…