2 citations · 3 across the 8 of their papers we have counts for
9 papers
VideoHarness-SRSI: Structured Recursive Self-Improvement for Video Understanding Harness
Zihao Lin, Haoyu Dong, Haobo Xu +10
LLM system capabilities depend not only on model parameters but also on the executable harnesses that organize information acquisition, tool use, reasoning, and memory. Recursive h…
MiLDEdit: Reasoning-Based Multi-Layer Design Document Editing
Zihao Lin, Wanrong Zhu, Jiuxiang Gu +8
Real-world design documents (e.g., posters) are inherently multi-layered, combining decoration, text, and images. Editing them from natural-language instructions requires fine-grai…
Sycophancy Mitigation Through Reinforcement Learning with Uncertainty-Aware Adaptive Reasoning Trajectories
Mohammad Beigi, Ying Shen, Parshin Shojaee +5
Despite the remarkable capabilities of large language models, current training paradigms inadvertently foster \textit{sycophancy}, i.e., the tendency of a model to agree with or re…
A Survey on Mechanistic Interpretability for Multi-Modal Foundation Models
Zihao Lin, Samyadeep Basu, Mohammad Beigi +18
The rise of foundation models has transformed machine learning research, prompting efforts to uncover their inner workings and develop more efficient and reliable applications for…
Persona-SQ: A Personalized Suggested Question Generation Framework For Real-world Documents
Zihao Lin, Zichao Wang, Yuanting Pan +5
Suggested questions (SQs) provide an effective initial interface for users to engage with their documents in AI-powered reading applications. In practical reading sessions, users h…
Rethinking the Uncertainty: A Critical Review and Analysis in the Era of Large Language Models
Mohammad Beigi, Sijia Wang, Ying Shen +9
In recent years, Large Language Models (LLMs) have become fundamental to a broad spectrum of artificial intelligence applications. As the use of LLMs expands, precisely estimating…
InternalInspector : Robust Confidence Estimation in LLMs through Internal States
Mohammad Beigi, Ying Shen, Runing Yang +7
Despite their vast capabilities, Large Language Models (LLMs) often struggle with generating reliable outputs, frequently producing high-confidence inaccuracies known as hallucinat…
Navigating the Dual Facets: A Comprehensive Evaluation of Sequential Memory Editing in Large Language Models
Zihao Lin, Mohammad Beigi, Hongxuan Li +5
Memory Editing (ME) has emerged as an efficient method to modify erroneous facts or inject new facts into Large Language Models (LLMs). Two mainstream ME methods exist: parameter-m…
Efficient Federated Prompt Tuning for Black-box Large Pre-trained Models
Zihao Lin, Yan Sun, Yifan Shi +4
With the blowout development of pre-trained models (PTMs), the efficient tuning of these models for diverse downstream applications has emerged as a pivotal research concern. Altho…