19 citations · 19 across the 5 of their papers we have counts for
6 papers · 1 filter
Don't Repeat Yourself: Self-Supervised Fine-Tuning for Coverage
Eric Fithian, Kirill Skobelev, X. Y. Han
In verifiable domains such as math and coding, finding one correct solution among many attempts can matter more than the pass rate of each attempt. Post-training can concentrate la…
Fine-Tuning Fixes Mode Collapse and Over-Dispersion in LLMs
Kirill Skobelev, Eric Fithian, X. Y. Han
Recent work by Doshi and Hauser (2024), Bisbee et al. (2024), and Xie et al. (2026) raises concerns that outputs from large language models (LLMs) tend to be under-diverse: they re…
Rank-Efficient LoRA via Joint Tangent-Space Optimization under Isotropic Curvature
Zihan Zhu, Zhehang Du, Xuyang Chen +5
Low-Rank Adaptation (LoRA) is an effective approach for adapting large pretrained models by learning low-rank weight updates. In practice, the LoRA rank is used to control an adapt…
SurgPhase: Time efficient pituitary tumor surgery phase recognition via an interactive web platform
Yan Meng, Jack Cook, X. Y. Han +12
Accurate surgical phase recognition is essential for analyzing procedural workflows, supporting intraoperative decision-making, and enabling data-driven improvements in surgical ed…
A Comparative Study in Surgical AI: Potential and Limitations of Data, Compute, and Scaling
Kirill Skobelev, Eric Fithian, Yegor Baranovski +9
Recent Artificial Intelligence (AI) models have matched or exceeded human experts in several benchmarks of biomedical task performance, but surgical benchmarks in particular are of…
Enhancing Model Context Protocol (MCP) with Context-Aware Server Collaboration
Meenakshi Amulya Jayanti, X. Y. Han
The Model Context Protocol (MCP) (MCP Community, 2025) has emerged as a widely used framework for enabling LLM-based agents to communicate with external tools and services. The ori…