activity
20232026
most citedAdapting LLM Agents with Universal Feedback in Communication

6 citations · 6 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2026

MemDelta: Controlled Baselines and Hidden Confounds in Agent Memory Evaluation

Kuan Wang

Agent memory systems are increasingly evaluated against RAG and full-context baselines, but reported gains often mix changes in the memory method with changes in the language model…

cs.LG2026

MemLeak: Diagnosing Information Leaks in Multimodal Agent Memory

Kuan Wang, Chao Zhang

When a multimodal AI agent is asked to forget a fact, current memory systems usually delete the text entry and report success. We find that the fact can remain recoverable from ret…

cs.RO2025

What Matters in Learning from Large-Scale Datasets for Robot Manipulation

Vaibhav Saxena, Matthew Bronars, Nadun Ranawaka Arachchige +5

Imitation learning from large multi-task demonstration datasets has emerged as a promising path for building generally-capable robots. As a result, 1000s of hours have been spent o…

cs.CL2024

RNR: Teaching Large Language Models to Follow Roles and Rules

Kuan Wang, Alexander Bukharin, Haoming Jiang +9

Instruction fine-tuning (IFT) elicits instruction following capabilities and steers the behavior of large language models (LLMs) via supervised learning. However, existing models t…

cs.CV2024

VIMI: Grounding Video Generation through Multi-modal Instruction

Yuwei Fang, Willi Menapace, Aliaksandr Siarohin +5

Existing text-to-video diffusion models rely solely on text-only encoders for their pretraining. This limitation stems from the absence of large-scale multimodal prompt video datas…

cs.CL2024

ARL2: Aligning Retrievers for Black-box Large Language Models via Self-guided Adaptive Relevance Labeling

Lingxi Zhang, Yue Yu, Kuan Wang +1

Retrieval-augmented generation enhances large language models (LLMs) by incorporating relevant information from external knowledge sources. This enables LLMs to adapt to specific d…

cs.CL2023★ 6 cited

Adapting LLM Agents with Universal Feedback in Communication

Kuan Wang, Yadong Lu, Michael Santacroce +3

Recent advances in large language models (LLMs) have demonstrated potential for LLM agents. To facilitate the training for these agents with both linguistic feedback and non-lingui…