activity
20242026
collaborators

7 papers

cs.AI2026

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…

cs.LG2026

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…

cs.CL2026

PA3: Policy-Aware Agent Alignment through Chain-of-Thought

Shubhashis Roy Dipta, Daniel Bis, Kun Zhou +4

Conversational assistants powered by large language models (LLMs) excel at tool-use tasks but struggle with adhering to complex, business-specific rules. While models can reason ov…

cs.CL2026

Beyond Perfect APIs: A Comprehensive Evaluation of LLM Agents Under Real-World API Complexity

Doyoung Kim, Zhiwei Ren, Jie Hao +11

We introduce WildAGTEval, a benchmark designed to evaluate large language model (LLM) agents' function-calling capabilities under realistic API complexity. Unlike prior work that a…

cs.AI2025

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…

cs.CL2025

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…

cs.CL2024

MEND: Meta dEmonstratioN Distillation for Efficient and Effective In-Context Learning

Yichuan Li, Xiyao Ma, Sixing Lu +3

Large Language models (LLMs) have demonstrated impressive in-context learning (ICL) capabilities, where a LLM makes predictions for a given test input together with a few input-out…