The paper investigates how to evaluate bounded model updates for ICU prediction models using the MIMIC‑IV database. After auditing the data for recording artifacts and cleaning it, the authors compare a bounded update that retrains only treatment‑context and fusion parameters with full fine‑tuning across two model families and two targets, finding that bounded updates are non‑inferior or superior in most cases but depend on target, model family, and learning‑rate tuning. The study also shows that audits should precede update evaluation and that the update boundary should be chosen per target.
By Fatema Ferdous Tamanna, K. M. Merajul Arefin, Md. Abdul Masud
arXiv:2607. 21922v1 Announce Type: new Abstract: Clinical irregular multivariate time series are shaped not only by physiological dynamics but also by the measurement process that determines when and what to observe.
By Mingyi Ma, Qingxiong Tan
arXiv:2608. 03628v1 Announce Type: new Abstract: Adaptive conformal prediction can recover clinically important heartbeat classes missed by a point classifier, but delayed feedback makes its decisions sensitive to event order.
By Arash Vashagh, Yasmin Vashagh
The paper introduces instruction duplication, a simple inference‑time control that repeats the procedural instruction without retraining or decoding changes. Across seven instruction‑tuned models and 16,800 scheduled generations, duplicating the instruction improves deterministic All‑8 diagnostic‑response success from 90.22% to 93.17% and reduces failures by 30.2%. In downstream Answer Engineering scenarios, duplication further boosts success rates, demonstrating its practical impact on systems that rely on the generated trajectory.
By Victor Lavrenko (PeaceTech VC, Israel)
arXiv:2607. 10466v1 Announce Type: new Abstract: Survival models can model time-to-event outcomes using partially observed data.
By Yanqi Xu, Hui Dai, Carlos Fernandez-Granda, Krzysztof J. Geras, Yiqiu Shen
The paper investigates the reliability of language‑model judges used as measurement instruments on shared endpoints. Through two preregistered audits of 52,988 requests, the authors found that repeat rankings and byte‑identical replays fell far short of required thresholds, revealing significant instability. They identify three mechanisms—label‑to‑meaning bias, candidate gaps below the noise floor, and input permutation noise—that explain the gap, and propose a snapshot‑identity ladder, design rules, and a reporting checklist to mitigate such failures.
arXiv:2608. 16003v1 Announce Type: new Abstract: Automated checking pipelines increasingly place one language model as the checker and another (or the same one) as the fixer.
By Parsa Mazaheri, Kasra Mazaheri
arXiv:2608. 00943v1 Announce Type: cross Abstract: Automated sleep staging assigns discrete stage labels to successive time epochs throughout an overnight recording; conventionally each window spans at least 30 seconds, reflecting the minimum temporal resolution of the clinical scoring standard.
By Shuntian Zheng, Jiawei Wang, Cong Fu, Huan Yu, Chen Chen, Yu Guan, Sai Gu
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.
By Di Wen, Kailun Yang, Jimmy Weissert, Luc Maria Scherrer, Cedric Z\"ollner, Ruiping Liu, Yufan Chen, Jiale Wei, Junwei Zheng, Kunyu Peng
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.
By Yibo Ma, Qianqian Zhang, Peng Liu, Tiancheng Zhao
arXiv:2609.08015v1 Announce Type: new
Abstract: Long-running AI agents may read state, reason, wait for tools or human approval, and perform an external action much later. The state that justified th...
By Yongjian Lyu, Yang Ren, Ruofei Lai, Wenting Liu
Instruction duplication is a simple, inference‑time control that repeats the procedural instruction in a language‑model output without retraining or decoding changes. In experiments across seven instruction‑tuned models on 300 medical multiple‑choice questions, duplicating the instruction increased the proportion of deterministic All‑8 diagnostic‑responses from 90.22 % to 93.17 % and reduced failures by 30.2 %. The technique also improved pre‑provisional TF‑IDF recall and, in downstream Answer Engineering scenarios, significantly raised success rates for specific endpoints.