The paper introduces a new benchmark of 345 annotated tennis ball bounces and racket hits across eight video clips featuring diverse viewpoints, including broadcast, fixed elevated, phone, and court-level angles. It evaluates several event detection methods—supervised spotters, zero-shot vision-language models, and a parameter-free arbitration layer called RallyArb—showing that supervised models perform well on broadcast footage but poorly on non-broadcast views, while RallyArb maintains high bounce detection across all viewpoints but struggles with precise hit timing. The dataset, evaluation tools, and frozen predictions are publicly released to support further research.
By David Soeiro-Vuong, Andre-Louis Rochet
arXiv:2608. 06361v1 Announce Type: new Abstract: Real-world video benchmarks provide broad coverage, but their fixed clips entangle event count, rate, duration, and visual complexity, making failure modes hard to isolate.
By Sarvesh Baskar, Zikui Cai, Shayan Shabihi, Anirudh Satheesh, Muhammad R. Islam, Udari Madhushani Sehwag, Tom Goldstein, Furong Huang
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:2609.15100v1 Announce Type: cross
Abstract: An image tool can change its underlying generator while retaining its public name, making version attribution from online posts ambiguous. We study t...
By Dennis Ng, Xingyu Shen, Ankit Raj, Kidus Zewde, Tommy Duong, Yuchen Zhou, Yuxin Zhang, Neo Tiangratanakul, Simiao Ren
The paper introduces an event‑anchored evaluation protocol for video world models, using 62 free‑fall recordings and 124 clips with detailed release and impact annotations. It finds that while some models (Runway, Veo) can generate release and impact events with high accuracy, they often start them late, and others (Cosmos‑Predict‑2.5, MAGI‑1) rarely produce measurable consequences. A human study shows that people’s predictions align with recorded futures but also reveal ambiguity in plausible continuations, highlighting that physical foresight requires initiating, timing, and realizing motion correctly.
By Estela Monserrat Arriaga Santana (National Autonomous University of Mexico), Julian Rosas Scull (National Autonomous University of Mexico), Eh\'ecatl Sacamch'en N\'u\~nez Rico (National Autonomous University of Mexico), Hugo Jair Escalante (University of Texas at El Paso)
arXiv:2609.09528v1 Announce Type: new
Abstract: Video large language models (Video-LLMs) are increasingly used as the perceptual front end of world models, a role that assumes they can read motion: h...
By Dhairya Bhatia, Bishoy Galoaa, Oliver Fritsche, Shahid Kamal, Muhammad Obaidullah Abdul Salam, Umer Saleem, Om Rastogi, Frania Felix Chettiar, Nesli Erdogmus, Sarah Ostadabbas
The paper presents a system that combines coded police crash records with officer narratives to estimate uncoded crash factors in Texas from 2017‑2025. It uses an in‑context tabular foundation model (Kumo Tabular) to process coded data, a calibrated System One model (Jev) to read sampled narratives, and human judgments to recalibrate probabilities, producing validated population estimates for factors such as hydroplaning, medical episodes, fatigue, animals, and phone use. The approach demonstrates that narrative analysis uncovers more injury crashes than coded fields and that a single additional round of human review can significantly tighten estimate precision.
"whyItMatters":"The system provides safety offices with more accurate counts, a discordance map, a validated re‑read list, and a reading budget, enabling better resource allocation for road‑safety interventions."
By Amir Rafe, Subasish Das
arXiv:2607. 20476v1 Announce Type: new Abstract: We present an empirical benchmark evaluating how five large language models assess multisensor physical hazard data.
By Faizan Iqbal
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.
By Emanuel Dopater, Daniel Gavlas, Luk\'a\v{s} Jochymek, Kannan Paul, Miloslav Szczypka, David Voj\'a\v{c}ek, Josef \v{Z}\'a\v{c}ek, Jan Plucar, Radek Svoboda, Ivan Zelinka
The paper identifies a flaw in current text-to-video safety filters that rely on keyword prompts and per-frame checks, showing that these systems miss harms that arise solely from the ordering of frames. Experiments reveal that a significant portion of generated videos contain harmful content that is undetected because each individual frame appears safe, and prompt engineering does not mitigate this issue. The authors propose an order-aware detector that can distinguish harmful videos from benign shuffles, achieving an AUC of 0.74 compared to chance for per-frame checking.
By Yuxin Cao, Fusen Guo, Yuezhong Wu, Huadong Mo, Wei Song
arXiv:2606. 02443v1 Announce Type: cross Abstract: Between the first visible sign of danger and the moment an accident occurs, there is often a window where intervention remains possible.
By Yusong Zhao, Yuejin Xie, Youliang Yuan, Junjie Hu, Jitian Guo, Yujiu Yang, Pinjia He
arXiv:2606. 25097v1 Announce Type: new Abstract: Speculative decoding accelerates inference by letting a draft model propose tokens for a target model to verify, raising a concrete safety question: at temperature zero, can draft-side behavior leak into safety-scored outputs?
By Sahil Kadadekar