3 papers
cs.HC2025
Explainable AI for Automated User-specific Feedback in Surgical Skill Acquisition
Catalina Gomez, Lalithkumar Seenivasan, Xinrui Zou +9
Traditional surgical skill acquisition relies heavily on expert feedback, yet direct access is limited by faculty availability and variability in subjective assessments. While trai…
cs.RO2025
Beyond Rigid AI: Towards Natural Human-Machine Symbiosis for Interoperative Surgical Assistance
Lalithkumar Seenivasan, Jiru Xu, Roger D. Soberanis Mukul +6
Emerging surgical data science and robotics solutions, especially those designed to provide assistance in situ, require natural human-machine interfaces to fully unlock their poten…
cs.LG2025
Position: Foundation Models Need Digital Twin Representations
Yiqing Shen, Hao Ding, Lalithkumar Seenivasan +2
Current foundation models (FMs) rely on token representations that directly fragment continuous real-world multimodal data into discrete tokens. They limit FMs to learning real-wor…