PhysioBench is a unified benchmark for physiological signal question answering that consolidates annotations from 22 public datasets into 61.4 million question‑answer pairs spanning 30 tasks. Each pair is linked to a specific signal segment and traceable to its source annotation. The benchmark evaluates 21 models—including large language models, vision‑language models, time‑series language models, and physiological signal foundation models—under three settings, revealing that no model consistently excels across all modalities and tasks, and that performance is sensitive to question wording.
By Mengxuan Li, Junfa Chen, Jinze Xia, Yundan Chen, Lixin Fan, Ke Liu, Keyue Shi, Haishuai Wang
ZYT-World is a real‑time, controllable world model designed for closed‑loop autonomous‑driving simulation. It generates a full fisheye‑pinhole camera rig at native resolution, using Plucker adapters for camera geometry, ego‑motion adaptive layer normalization for motion control, and a lightweight pixel‑aligned layout to condition traffic participants. The model achieves high fidelity to a 40‑step teacher while being over 100 times faster, and includes an implicit‑memory module that preserves place‑specific evidence for long‑horizon stability.
By Boni Hu, Xiong Wei, Haoming Huang, Yong Huang, Chenbo Wang, Yi Yang, Jiancheng Wang, Ruicheng Zhu, Zhimin Yang, Guanglai Liu, Qiaowan Jin, Dongzhuo Wang, Haiwei Kuang, Jiajun Fan, Yue Wu, Jiaxin Wei, Hao Sun, Feihong Yan, Wei Bi, Kaixuan Wang, Zichao Guo, Xiaozhi Chen
HERMES is a holistic end‑to‑end multimodal driving framework that incorporates long‑tail semantic knowledge into trajectory planning for autonomous vehicles. It uses a foundation‑model‑assisted annotation pipeline to build Long‑Tail Scene Context and Long‑Tail Planning Context, capturing hazard‑centric scene information, maneuver intent, and risk‑aware guidance. A Tri‑Modal Driving Module then fuses multi‑view visual observations, historical ego‑motion, and long‑tail semantic instructions to generate intent‑ and risk‑aware trajectories, achieving consistent performance gains on a large‑scale real‑world long‑tail driving benchmark.
By Weizhe Tang, Junwei You, Jiaxi Liu, Zhaoyi Wang, Rui Gan, Zilin Huang, Feng Wei, Bin Ran
arXiv:2609.21755v1 Announce Type: new
Abstract: Emergency department (ED) decision-making relies on heterogeneous clinical information, including patient history, vital signs, laboratory results, and...
By Jinning Liang, Mingcheng Zhu, Tingting Zhu
arXiv:2609.21751v1 Announce Type: cross
Abstract: Manipulating objects requires understanding not only their motion, but also the physical properties that determine it. For articulated objects, these...
By Tim Engelbracht, Ren\'e Zurbr\"ugg, Mayank Mittal, Marco Hutter, Marc Pollefeys, Hermann Blum, Zuria Bauer
arXiv:2609.21828v1 Announce Type: cross
Abstract: Blind and low-vision users often face challenges when locating and physically acquiring objects in unfamiliar indoor environments. Existing vision-la...
By George Xi Wang, Xiangyu Li, Shaoyue Wen, Jiaqian Hu, Junan Xie, Yupeng Wang, Ziyue Shi, Qijun Chen, Maaike Bouwmeester, Yuhua Jin, Jing Qian
arXiv:2609.21967v1 Announce Type: cross
Abstract: We introduce NemotronLabs VoiceChat, an open full-duplex speech-to-speech model with native tool-calling capabilities. NemotronLabs VoiceChat combine...
By Jagadeesh Balam, Travis Bartley, Edresson Casanova, Sanjay Chauhan, Chen Chen, Zhehuai Chen, Zijia Chen, Francesco Ciannella, Slyne Deng, Mikyas Desta, Harishchandra Dubey, Slim Essid, Nourchene Ferchichi, Boris Ginsburg, Mariana Graterol Fuenmayor, Negar Habibi, Kevin Hu, Anand Joseph, Viraj Karandikar, Myungjong Kim, Viacheslav Klimkov, Seelan Lakshmi Narasimhan, Lily Lee, Jason Li, Eileen Long, Ameya Mahabaleshwarkar, Aditya Malte, Adi Margolin, Sasha Meister, Valentin Mendelev, Oluwatobi Olabiyi, Ankita Pasad, Yifan Peng, Elena Rastorgueva, Jayda Ritchie, Jason Roche, Nikhil Srihari, Yuanhang Su, Yoshi Suhara, Viet Anh Trinh, Jinhan Wang, Piotr Zelasko, Hui Wang, Puhui Meng, Chaosen Zhang, Yunsheng Liu, Shawn Wang, Wenjing Li, Zhonglei He
arXiv:2603.16086v2 Announce Type: replace-cross
Abstract: While recent Vision-Language-Action (VLA) models have begun to incorporate audio, they typically treat sound as static pre-execution prompts...
By Chang Nie, Tianchen Deng, Guangming Wang, Zhe Liu, Hesheng Wang
arXiv:2607.08837v4 Announce Type: replace-cross
Abstract: Exploration is essential to RL since a policy cannot improve by repeatedly sampling the behaviors it already prefers. Standard methods inject...
By Sunshine Jiang, John Marangola, David Zhang, Raghuram Kowdeed, Ruiyang Luo, Nitish Dashora, Richard Li, Pulkit Agrawal, Zhang-Wei Hong
arXiv:2609.21363v1 Announce Type: cross
Abstract: Multimodal large reasoning models (MLRMs) have demonstrated remarkable capabilities in complex visual understanding. However, this very power introdu...
By Yining Wang, Xi Li, Mi Zhang, Xiaohan Zhang, Xiaoyu You, Zhenxing Qian, Mi Wen
arXiv:2509.24789v5 Announce Type: replace
Abstract: The evaluation of time series forecasting models is hindered by a lack of high-quality benchmarks, leading to overestimated assessments of progress...
By Zhijian Xu, Wanxu Cai, Xilin Dai, Zhaorong Deng, Qiang Xu
arXiv:2609.20850v1 Announce Type: new
Abstract: While Multimodal Large Language Models (MLLMs) show remarkable advancements, their cross-modal capabilities introduce complex vulnerabilities that easi...
By Yueming Lyu, Yilian Shi, Haoxiang Tan, Linzhuang Zou, Qihao Wang, Guihua Yu, Jie Qin, Xin Gao, Chenyang Si, Jing Dong, Caifeng Shan
arXiv:2609.20995v1 Announce Type: cross
Abstract: Natural spoken interaction requires more than streaming ASR, language generation, and speech synthesis: a system must react to overlap without cancel...
By Bertil Braun
arXiv:2601.06848v2 Announce Type: replace
Abstract: Multimodal aspect-based sentiment analysis (MABSA) aims to identify aspect-level sentiments by jointly modeling textual and visual information, whi...
By Zhongzheng Wang, Yuanhe Tian, Hongzhi Wang, Yan Song
arXiv:2609.21351v1 Announce Type: new
Abstract: Table extraction suffers from frequent structural errors and semantic hallucinations. We propose PrismAlign, a multi-VLM framework aligning diverse vis...
By Guangyi Liu, Qianjun Huang, Boyu Hou
arXiv:2609.21402v1 Announce Type: new
Abstract: Surgical instrument segmentation (SIS) plays a critical role in robotic assistance and surgical workflow analysis. However, most existing SIS methods f...
By Zhibo Zhang, Qijie Wang, Zengqiang Yan
arXiv:2609.21379v1 Announce Type: new
Abstract: Accurate traffic forecasting requires both understanding scene dynamics and synthesizing realistic future observations. Recent diffusion-based video ge...
By Trinh Tra Giang Nguyen, Thanh Nguyen Vo, Nguyen Hoai Thuong Bui, Ha Duc Bui
arXiv:2609.21675v1 Announce Type: new
Abstract: Despite the remarkable progress in Multimodal Large Language Models (MLLMs), prevailing Chain-of-Thought (CoT) paradigms remain confined to the natural...
By Wan Xu, Yuanfan Guo, Kevin Han, LaLa Chen, Wangmeng Zuo
arXiv:2609.22083v1 Announce Type: new
Abstract: We present MintAct, a family of vision-language models that unifies UI grounding, multi-step navigation across mobile, desktop, and web, and visual too...
By Mingfei Gao, Rui Tian, Haiming Gang, Bohan Zhai, Le Zhang, Yuanzheng Gong, Di Feng, Ege \"Ozsoy, Kaixin Ma, Vishwesh Kirthivasan, O\u{g}uzhan Fatih Kar, Roman Bachmann, Anders Boesen Lindbo Larsen, Afshin Dehghan
arXiv:2504.15776v2 Announce Type: replace
Abstract: Public autonomous driving datasets underpin the training and benchmarking of perception, mapping, and localization algorithms, yet residual inaccur...
By Quentin Herau, Nathan Piasco, Moussab Bennehar, Luis Rold\~ao, Dzmitry Tsishkou, Bingbing Liu, Cyrille Migniot, Pascal Vasseur, C\'edric Demonceaux