activity
20242026
most citedVisual Prompting in Multimodal Large Language Models: A Survey

4 citations · 12 across the 30 of their papers we have counts for

collaborators
Showing cs.CLShow all

6 papers · 1 filter

cs.CL2026

Filesystem-Based Memory for LLM Agents: Organization, Evolution, and Sustainability

Sizhe Zhou, Sheldon Yu, Hui Wei +8

Deployed LLM agents increasingly keep their long-term memory as a filesystem: a directory tree of markdown files that the agent itself reads, writes, and reorganizes through generi…

cs.CL2025

Pluralistic Off-policy Evaluation and Alignment

Chengkai Huang, Junda Wu, Zhouhang Xie +6

Personalized preference alignment for LLMs with diverse human preferences requires evaluation and alignment methods that capture pluralism. Most existing preference alignment datas…

cs.CL2025

Explainable Chain-of-Thought Reasoning: An Empirical Analysis on State-Aware Reasoning Dynamics

Sheldon Yu, Yuxin Xiong, Junda Wu +6

Recent advances in chain-of-thought (CoT) prompting have enabled large language models (LLMs) to perform multi-step reasoning. However, the explainability of such reasoning remains…

cs.CL2025

SAND: Boosting LLM Agents with Self-Taught Action Deliberation

Yu Xia, Yiran Shen, Junda Wu +5

Large Language Model (LLM) agents are commonly tuned with supervised finetuning on ReAct-style expert trajectories or preference optimization over pairwise rollouts. Most of these…

cs.CL2024

Knowledge-Aware Query Expansion with Large Language Models for Textual and Relational Retrieval

Yu Xia, Junda Wu, Sungchul Kim +4

Large language models (LLMs) have been used to generate query expansions augmenting original queries for improving information search. Recent studies also explore providing LLMs wi…

cs.CL2024

Diversify-verify-adapt: Efficient and Robust Retrieval-Augmented Ambiguous Question Answering

Yeonjun In, Sungchul Kim, Ryan A. Rossi +4

The retrieval augmented generation (RAG) framework addresses an ambiguity in user queries in QA systems by retrieving passages that cover all plausible interpretations and generati…