63 citations · 111 across the 22 of their papers we have counts for
22 papers
MIRIX: Multi-Agent Memory System for LLM-Based Agents
Yu Wang, Xi Chen
Although memory capabilities of AI agents are gaining increasing attention, existing solutions remain fundamentally limited. Most rely on flat, narrowly scoped memory components, c…
Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions
Yuanzhe Hu, Yu Wang, Julian McAuley
Recent benchmarks for Large Language Model (LLM) agents primarily focus on evaluating reasoning, planning, and execution capabilities, while another critical component-memory, enco…
RM-R1: Reward Modeling as Reasoning
Xiusi Chen, Gaotang Li, Ziqi Wang +9
Reward modeling is essential for aligning large language models with human preferences through reinforcement learning. To provide accurate reward signals, a reward model (RM) shoul…
A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment
Kun Wang, Guibin Zhang, Zhenhong Zhou +100
The remarkable success of Large Language Models (LLMs) has illuminated a promising pathway toward achieving Artificial General Intelligence for both academic and industrial communi…
M+: Extending MemoryLLM with Scalable Long-Term Memory
Yu Wang, Dmitry Krotov, Yuanzhe Hu +6
Equipping large language models (LLMs) with latent-space memory has attracted increasing attention as they can extend the context window of existing language models. However, retai…
Self-Updatable Large Language Models by Integrating Context into Model Parameters
Yu Wang, Xinshuang Liu, Xiusi Chen +3
Despite significant advancements in large language models (LLMs), the rapid and frequent integration of small-scale experiences, such as interactions with surrounding objects, rema…
Towards LifeSpan Cognitive Systems
Yu Wang, Chi Han, Tongtong Wu +9
Building a human-like system that continuously interacts with complex environments -- whether simulated digital worlds or human society -- presents several key challenges. Central…
CoMMIT: Coordinated Multimodal Instruction Tuning
Xintong Li, Junda Wu, Tong Yu +6
Instruction tuning in multimodal large language models (MLLMs) generally involves cooperative learning between a backbone LLM and a feature encoder of non-text input modalities. Th…
Large Scale Knowledge Washing
Yu Wang, Ruihan Wu, Zexue He +2
Large language models show impressive abilities in memorizing world knowledge, which leads to concerns regarding memorization of private information, toxic or sensitive knowledge,…
LVCHAT: Facilitating Long Video Comprehension
Yu Wang, Zeyuan Zhang, Julian McAuley +1
Enabling large language models (LLMs) to read videos is vital for multimodal LLMs. Existing works show promise on short videos whereas long video (longer than e.g.~1 minute) compre…
Unified Generation, Reconstruction, and Representation: Generalized Diffusion with Adaptive Latent Encoding-Decoding
Guangyi Liu, Yu Wang, Zeyu Feng +9
The vast applications of deep generative models are anchored in three core capabilities -- generating new instances, reconstructing inputs, and learning compact representations --…
MEMORYLLM: Towards Self-Updatable Large Language Models
Yu Wang, Yifan Gao, Xiusi Chen +9
Existing Large Language Models (LLMs) usually remain static after deployment, which might make it hard to inject new knowledge into the model. We aim to build models containing a c…
Deciphering Compatibility Relationships with Textual Descriptions via Extraction and Explanation
Yu Wang, Zexue He, Zhankui He +2
Understanding and accurately explaining compatibility relationships between fashion items is a challenging problem in the burgeoning domain of AI-driven outfit recommendations. Pre…
MedEval: A Multi-Level, Multi-Task, and Multi-Domain Medical Benchmark for Language Model Evaluation
Zexue He, Yu Wang, An Yan +5
Curated datasets for healthcare are often limited due to the need of human annotations from experts. In this paper, we present MedEval, a multi-level, multi-task, and multi-domain…
Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models
An Yan, Yu Wang, Yiwu Zhong +8
Medical image classification is a critical problem for healthcare, with the potential to alleviate the workload of doctors and facilitate diagnoses of patients. However, two challe…
Learning Concise and Descriptive Attributes for Visual Recognition
An Yan, Yu Wang, Yiwu Zhong +6
Recent advances in foundation models present new opportunities for interpretable visual recognition -- one can first query Large Language Models (LLMs) to obtain a set of attribute…
Label Denoising through Cross-Model Agreement
Yu Wang, Xin Xin, Zaiqiao Meng +2
Learning from corrupted labels is very common in real-world machine-learning applications. Memorizing such noisy labels could affect the learning of the model, leading to sub-optim…
Controlling Bias Exposure for Fair Interpretable Predictions
Zexue He, Yu Wang, Julian McAuley +1
Recent work on reducing bias in NLP models usually focuses on protecting or isolating information related to a sensitive attribute (like gender or race). However, when sensitive in…
Differentiable Invariant Causal Discovery
Yu Wang, An Zhang, Xiang Wang +3
Learning causal structure from observational data is a fundamental challenge in machine learning. However, the majority of commonly used differentiable causal discovery methods are…
Improving Out-of-Distribution Robustness via Selective Augmentation
Huaxiu Yao, Yu Wang, Sai Li +4
Machine learning algorithms typically assume that training and test examples are drawn from the same distribution. However, distribution shift is a common problem in real-world app…
Meta-learning with an Adaptive Task Scheduler
Huaxiu Yao, Yu Wang, Ying Wei +4
To benefit the learning of a new task, meta-learning has been proposed to transfer a well-generalized meta-model learned from various meta-training tasks. Existing meta-learning al…
Learning Robust Recommenders through Cross-Model Agreement
Yu Wang, Xin Xin, Zaiqiao Meng +3
Learning from implicit feedback is one of the most common cases in the application of recommender systems. Generally speaking, interacted examples are considered as positive while…