arXiv AI

Toward Fast Human Reaction Modeling from High-Frequency Digital Interaction Data

The paper explores whether rapid, salience-driven behavior can be detected in high‑frequency Counter‑Strike 2 telemetry by reconstructing players’ perceptual states with a millisecond‑precision visibility engine. Using a 60–120 ms reaction window, the authors derive fight‑or‑flight labels and train two telemetry‑native models—EMBER (gradient boosting) and ORBIT (custom transformer)—which outperform a majority‑class baseline and a large language model while requiring far less inference time and cost. Adding a short post‑trigger window further improves accuracy to 85.15%, suggesting a potential probe of the shift from rapid to deliberative control, though the study remains hypothesis‑generating rather than mechanistic.

Hugging Face Trending Papers

Jun 3

Plan, Watch, Recover: A Benchmark and Architectures for Proactive Procedural Assistance

We envision a proactive multi-modal assistant system which gives users real-time step-by-step guidance on a procedural task, autonomously deciding \textit{when} to interrupt, and \textit{how} to coach. However, progress is limited by the absence of large-scale, cross-domain benchmarks that reflect realistic conditions, particularly the common case in which users deviate from the expected step sequence.

llms multimodal benchmarks

arXiv Machine Learning

Sep 1

Behavioral Latency as Weak Event-Time Supervision for EEG Reaction-Time Decoding

arXiv:2608.29428v1 Announce Type: new Abstract: Single-trial EEG analyses are often organized around events and latencies, yet EEG-based reaction-time (RT) prediction is posed as scalar regression on...

By Anuar Aimoldin, Ayana Mussabayeva, Yedige Mussabayev, Xue Liu, Kun Zhang

safety

arXiv AI

Jun 4

Plan, Watch, Recover: A Benchmark and Architectures for Proactive Procedural Assistance

arXiv:2606. 04970v1 Announce Type: cross Abstract: We envision a proactive multi-modal assistant system which gives users real-time step-by-step guidance on a procedural task, autonomously deciding \textit{when} to interrupt, and \textit{how} to coach.

By Kaustav Kundu, Ritvik Shrivastava, Maxim Arap, Nanshu Wang, Xianhui Zhu, Quintin Fettes, Gautam Tiwari, Parth Suresh, Th\'eo Moutakanni, Alejandro Castillejo Munoz, Allen Bolourchi, Pascale Fung, Pinar Donmez, Babak Damavandi, Anuj Kumar, Seungwhan Moon

llms multimodal benchmarks

arXiv Computer Vision

5d ago

Readout and Operating-Point Sensitivity in Event-Stream Candidate Selection

The paper evaluates how readout settings and operating points affect event-stream candidate selection in event-triggered recording. Using 520 DailyDVS‑200 clips and 47 participants, the authors compare several encoder configurations—including DINO‑CLS, patch mean, and MobileNetV3‑Small—across different accumulation durations and view counts, reporting metrics such as target capture, false triggers, and partial AUC. The study finds that lightweight baselines and specific operating points significantly influence performance, while no clear advantage is observed for the JEPA‑specific method.

By Yifan Shuai

arXiv AI

Jun 4

The Saturation Trap and the Subjectivity of Intervention Timing: Why Affect-Based Triggers and LLM Judges Fail to Time Interventions on Autonomous Agents

arXiv:2606. 04296v1 Announce Type: new Abstract: As autonomous AI agents move from conversational systems to long-horizon software execution, runtime safety layers that decide when to interrupt an agent have become essential.

By Manvendra Modgil

llms agents safety

arXiv AI

Sep 30

More Features Are Not More Evidence: Limits of Training-Free Human Activity Recognition with Jev

arXiv:2609.36154v1 Announce Type: new Abstract: General-purpose models promise sensor-based decisions without training a task-specific classifier, which could reduce the dependence of Human Activity...

By Orhan Konak

arXiv Machine Learning

Sep 3

Orthogonal Ensembles and Tested Explanations for Performer-Independent Body-Motion Emotion Recognition

The paper investigates 12‑class body‑only emotion recognition from skeleton motion using a leave‑performer‑out evaluation, where chance accuracy is 8.3% and a reproduced STGCN++ baseline scores 25.73% Macro‑F1. By ensembling eleven models with orthogonal error modes, the authors achieve 36.80% Macro‑F1, a 43% relative improvement over the baseline. They also introduce a tested explanation suite that demonstrates the ensemble’s decisions rely on motion‑grounded body‑region evidence, aligning strongly with Laban Movement Analysis attributes rather than classical kinematics, while showing diffuse temporal saliency.

By Naoto Nishida, Yoshio Ishiguro

safety

arXiv Machine Learning

Sep 15

GroundBench: A Factorized, Counterfactual Benchmark for Locating VLM Affordance Failures

arXiv:2609.13308v1 Announce Type: cross Abstract: A companion evaluation found that naming the target part in a manipulation prompt increased action accuracy by 0.32-0.63 across eight vision-language...

By Sarthak Sattigeri

llms robotics multimodal benchmarks

arXiv AI

Jul 28

Cortex: Compact Behavior Cloning for Quake with Frozen Visual Features

arXiv:2607. 22739v1 Announce Type: cross Abstract: We study how far a deliberately simple behavioral-cloning policy can progress in a visually rich first-person game before adding reinforcement learning or explicit memory.

By Dzmitry Malyshau

llms reinforcement-learning

arXiv Machine Learning

Jun 8

Uncertainty-Guided Label Rebalancing for CPS Safety Monitoring

arXiv:2603. 25670v3 Announce Type: replace Abstract: Safety monitoring is essential for Cyber-Physical Systems (CPSs).

By John Ayotunde, Qinghua Xu, Guancheng Wang, Lionel C. Briand

benchmarks safety

arXiv AI

Aug 14

Decomposition of Evidence, Contradiction, and Fragility in Perturbation Responses

arXiv:2608. 12935v1 Announce Type: new Abstract: Perturbation methods explain model decisions by measuring prediction changes under altered inputs, but response magnitude tells us only how much a model reacts, not what that reaction means.

By Lei You

arXiv AI

Oct 9

PulseBound: Future-Beat State Forecasting Under an Explicit Information Boundary

PulseBound is a photoplethysmography (PPG) representation learner that enforces an explicit stored‑window information boundary, ensuring that encoder inputs depend only on a visible prefix and a content‑independent cutoff. By combining physiologically structured future‑beat prediction with prefix‑only normalization, suffix replacement, and aligned masking, PulseBound achieves stored‑suffix invariance, preventing changes in the stored suffix from affecting forecast context or predictions. Evaluated on MIMIC and VitalDB datasets, PulseBound reduces mean absolute error by over 22% compared to last‑visible‑beat persistence and outperforms seven other models on nine frozen linear‑probe and seven full‑fine‑tuning downstream tasks.

By Chenyang Xu, Donglin Xie, Xi Xiang, Xiaoyu Li, Yufan Lu, Jiqiun Gao, Yi Zhao, Xin-Yi Li, Guangpu Zhu, Zijian Wang, Xiwen Yang, Dezhen Wang, Lin Chen, Shenda Hong, Leilei Li

fine-tuning

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