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SurgStreamer is a system that proactively monitors surgical video streams by separating procedure‑specific perception—mapping a 1‑FPS video to a structured state of tools, actions, and phases—from procedure‑agnostic reasoning that evaluates formal monitoring conditions over this state and an event memory. In a sealed test set of 12 laparoscopic cholecystectomies, SurgStreamer achieved a macro event‑F1 of 0.598 over full procedures and 0.677 on 75‑second windows, outperforming streaming vision‑language models. Re‑training only the perception component improved held‑out performance from 0.239 to 0.470, and a controller trained on cholecystectomy data performed comparably to an in‑domain controller on pituitary surgery, highlighting perception under distribution shift as the main limitation.
PROVIA is a system for online mistake detection in egocentric videos that tracks procedure state by maintaining a factual state and a learned summary of performed steps, including mistakes. It uses Bayesian state merging to create an automaton from correct demonstrations and applies a sequential test to convert per‑frame mistake probabilities into alarms. Evaluated on datasets such as CaptainCook4D, IndustReal, HoloAssist, and IMPACT‑ego, PROVIA outperforms baseline methods while operating at 58–70 fps.
TRACE (Temporal Audit and Condition-aware Evaluation) is a new benchmark and evaluation framework for streaming video understanding that explicitly records when evidence becomes valid, how visual history is maintained, and how responses are triggered. It combines temporally audited visual tasks, evidence timing, instruction-dependent trigger annotations, a unified causal Core–Adapter protocol, and multidimensional reporting of answer quality, timeliness, response-selection behavior, workload, completion, and reliability. Using 1,240 records from 517 videos, TRACE evaluated eight publicly available models in eight configurations, revealing that similar QA accuracy can hide significant differences in completion, answer validity, generation workload, and proactive performance metrics such as response delay, false alarms, and missed target windows.
arXiv:2603.29962v4 Announce Type: replace Abstract: Surgical procedures are inherently complex and risky, requiring extensive expertise and constant focus to navigate evolving intraoperative scenes....
arXiv:2610.34926v1 Announce Type: new Abstract: An action seen once and the same action seen three times receive the same action-recognition label, so that supervision does not require a model to cou...
arXiv:2608. 14015v1 Announce Type: cross Abstract: Understanding tens-of-minutes surgical videos requires long-horizon temporal reasoning, answering what happens before, after, or across stages of a procedure by grounding the question in visual evidence spread across time.