1 citations · 1 across the 7 of their papers we have counts for
9 papers
Agents' Last Doubt: Knowing When to Act and When to Step Back in Tool-Using Agents
Qisai Liu, Qingyue Jiao, Bangwei Guo +7
Tool-using LLM agents repeatedly face decision points under uncertainty, choosing between autonomous action, additional information gathering, and human escalation. These decisions…
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…
CompressKV: Semantic-Retrieval-Guided KV-Cache Compression for Resource-Efficient Long-Context LLM Inference
Xiaolin Lin, Jingcun Wang, Olga Kondrateva +3
Long-context large language model (LLM) inference is increasingly constrained by the memory footprint and decoding cost of key-value (KV) caches, limiting sustainable deployment on…
MemEye: A Visual-Centric Evaluation Framework for Multimodal Agent Memory
Minghao Guo, Qingyue Jiao, Zeru Shi +14
Long-term agent memory is increasingly multimodal, yet existing evaluations rarely test whether agents preserve the visual evidence needed for later reasoning. In prior work, many…
ADRD-Bench: A Preliminary LLM Benchmark for Alzheimer's Disease and Related Dementias
Guangxin Zhao, Jiahao Zheng, Malaz Boustani +4
Large language models (LLMs) have shown great potential for healthcare applications. However, existing evaluation benchmarks provide minimal coverage of Alzheimer's Disease and Rel…
FairLRF: Achieving Fairness through Sparse Low Rank Factorization
Yuanbo Guo, Jun Xia, Yiyu Shi
As deep learning (DL) techniques become integral to various applications, ensuring model fairness while maintaining high performance has become increasingly critical, particularly…
Human Resilience in the AI Era -- What Machines Can't Replace
Shaoshan Liu, Anina Schwarzenbach, Yiyu Shi
AI is changing work and decision making faster than many institutions can adapt their operating practices. We argue that this adaptation gap makes human resilience a core capabilit…
CompressKV: Semantic Retrieval Heads Know What Tokens are Not Important Before Generation
Xiaolin Lin, Jingcun Wang, Olga Kondrateva +3
Recent advances in large language models (LLMs) have significantly boosted long-context processing. However, the increasing key-value (KV) cache size poses critical challenges to m…
EBFT: Effective and Block-Wise Fine-Tuning for Sparse LLMs
Song Guo, Fan Wu, Lei Zhang +5
Existing methods for fine-tuning sparse LLMs often suffer from resource-intensive requirements and high retraining costs. Additionally, many fine-tuning methods often rely on appro…