1 citations · 2 across the 9 of their papers we have counts for
11 papers
Seeing, Maintaining, and Learning: The Evolution of Multimodal Agent Memory
Minghao Guo, Qingyue Jiao, Xiujin Liu +77
Multimodal models and agents have emerged to enable AI systems to learn from and interact with the real world more like humans by integrating information across multiple modalities…
HAS-Bench: Evaluating LLM-Based Human-Agent Systems under Configurable Human Participation
Yaozu Wu, Wei-Chieh Huang, Jizhou Guo +11
Large language models increasingly operate in settings where humans are active collaborators rather than passive task providers. We introduce HAS-Framework, a graph-based framework…
Embracing Trustworthy Brain-Agent Collaboration as Paradigm Extension for Intelligent Assistive Technologies
Yankai Chen, Xinni Zhang, Yifei Zhang +6
Brain-Computer Interfaces (BCIs) offer a direct communication pathway between the human brain and external devices, holding significant promise for individuals with severe neurolog…
Deep Research with Open-Domain Evaluation and Multi-Stage Guardrails for Safety
Wei-Chieh Huang, Henry Peng Zou, Yaozu Wu +12
Deep research frameworks have shown promising capabilities in synthesizing comprehensive reports from web sources. While deep research possesses significant potential to address co…
PSG-Agent: Personality-Aware Safety Guardrail for LLM-based Agents
Yaozu Wu, Jizhou Guo, Dongyuan Li +9
Effective guardrails are essential for safely deploying LLM-based agents in critical applications. Despite recent advances, existing guardrails suffer from two fundamental limitati…
A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy
Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu +10
Recent improvements in large language models (LLMs) have led many researchers to focus on building fully autonomous AI agents. This position paper questions whether this approach i…
Scaling Laws for Many-Shot In-Context Learning with Self-Generated Annotations
Zhengyao Gu, Henry Peng Zou, Yankai Chen +3
The high cost of obtaining high-quality annotated data for in-context learning (ICL) has motivated the development of methods that use self-generated annotations in place of ground…
TabGen-ICL: Residual-Aware In-Context Example Selection for Tabular Data Generation
Liancheng Fang, Aiwei Liu, Hengrui Zhang +3
Large Language models (LLMs) have achieved encouraging results in tabular data generation. However, existing approaches require fine-tuning, which is computationally expensive. Thi…
TestNUC: Enhancing Test-Time Computing Approaches and Scaling through Neighboring Unlabeled Data Consistency
Henry Peng Zou, Zhengyao Gu, Yue Zhou +7
Test-time computing approaches, which leverage additional computational resources during inference, have been proven effective in enhancing large language model performance. This w…
ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value Extraction
Henry Peng Zou, Vinay Samuel, Yue Zhou +5
Existing datasets for attribute value extraction (AVE) predominantly focus on explicit attribute values while neglecting the implicit ones, lack product images, are often not publi…
DeCrisisMB: Debiased Semi-Supervised Learning for Crisis Tweet Classification via Memory Bank
Henry Peng Zou, Yue Zhou, Weizhi Zhang +1
During crisis events, people often use social media platforms such as Twitter to disseminate information about the situation, warnings, advice, and support. Emergency relief organi…