SmolWAM is a compact latent world‑action model designed for robotic manipulation on edge devices. It is derived from the 2B‑parameter Cosmos Policy by reusing its pretrained trunk and selecting 12 of 28 transformer blocks, while employing single‑frame visual latents and cached text features to produce actions and future visual latents in a single rectified‑flow step. Trained in only 3.6 GPU‑hours, SmolWAM achieves 95.5% success on 400 LIBERO episodes at 57 ms per step, using 2.3 TFLOP per 16‑action chunk and 2.4 GiB GPU memory—an 11× speed‑up, 18.5× fewer FLOPs, and 3.2× less memory than its teacher model, while still forecasting scene motion.
By Timi Owolabi, Hikmah Olawore, Frances Adelakun, Ayotomiwa Oyewumi
The paper introduces Semantic Head Specialization (SHS), a phenomenon where Vision Transformer (ViT) attention heads specialize as either object- or background-focused, most evident under full attention. It proposes the SHS-Index to quantify this specialization, demonstrating its ability to distinguish full-attention from chunk-window ViTs and its strong correlation with downstream benchmark performance. Leveraging insights into window interaction, token serialization, and local softmax allocation, the authors design Ariadne Attention, a hybrid attention mechanism that matches full-attention performance on 22 image and video tasks while reducing attention compute by 6.5×.
By Chenhong He, Lei Li, Shicheng Li, Hanglong Lv, Lingpeng Kong, Qi Liu, Tong Yang, Shuhuai Ren
arXiv:2610.31762v1 Announce Type: cross
Abstract: What visual representation should a navigation policy act on? Navigation policies built on vision foundation models largely leave representation desi...
By Jiawei Chen, Yinxi Wang, Zihan Yang, Meijun Gao, Yaowei Liu, Shuming Zhang, Zecheng Li, Xiangju Mi, Yuxuan Liu, Runzhong Huang, Tianlin Li, Xun Zhang, Zhengyang Wang, Xu Miao, Tong Yang
PhysBrain 1.5 is a unified vision‑language model that learns to understand physical environments, generate actions, and predict future states by encoding language, end‑effector motion, and dense visual targets as discrete sequences and training them with autoregressive next‑token prediction. The model is pre‑trained on human interaction videos and fine‑tuned on human demonstrations, robot trajectories, and simulated experience, achieving an average score of 72.5 across 28 embodied understanding benchmarks and outperforming other open‑source models on 14 of them. It also demonstrates the ability to produce end‑effector trajectories and predict future scenes with spatially aligned RGB, depth, and robot‑mask outputs.
By DeepCybo Team, Yu Bin, Haipeng Cao, Zheng Chang, Kai Chen, Youning Chen, Kailin Deng, Yichao Du, Xiaotong Fu, Haoyang Ge, Yunlong Guo, Chenliu Hao, Jiyan He, Xuguo He, Yakun Hou, Kai Hu, Cong Huang, Tuopusen Huang, Yu Huang, Hong Li, Peize Li, Shijie Lian, Xiaopeng Lin, Yun Lin, Haibao Liu, Haochen Liu, Qiuzhi Liu, Shengcai Liu, Zhiqiang Liu, Tao Luo, Peng Ren, Shuo Ren, Chaoyi Ruan, Zhaolong Shen, Yukun Shi, Qiyuan Su, Yuxuan Tian, Yining Wang, Changti Wu, Hao Wu, Xueyin Xu, Ruoqi Yang, Zhaoyang Yang, Hang Yuan, Zhaoyang Zeng, Hanwen Zhang, Ruimeng Zhang, Yao Zhang, Yibo Zhang, Yuxiang Zhang, Zhirui Zhang, Ziyi Zhang, Zubin Zheng, Zishen Zhuang
We introduce Predictive State Retrieval (PSR), a task in which a model observes a short video prefix and a temporal question about an object's future state, then retrieves instances from other videos or images that depict that state. Unlike action anticipation, which predicts a label, moment retrieval, which localizes an observed event within a video, or video generation, which synthesizes pixels, PSR combines anticipation with cross-instance retrieval across multiple temporal horizons.
The paper introduces 4DMemory, a benchmark for long‑horizon spatiotemporal memory that includes the LongVideo dataset—about 900 hours of video with over 5,000 manually reviewed questions—and the 4DMemoryBench. It also presents the 4DMemory Harness, a system that combines four complementary skills, more than 22 specialized tool models, and a retrieval‑augmented generation component. Evaluation of 13 leading models shows that standalone foundation models perform poorly on long‑horizon tasks, while the 4DMemory Harness boosts performance by over 50% and reveals a performance drop of more than 10% compared to previous benchmarks.
By Yanhao Jia, Yi Zhu, Yiran Zhong, Weigao Sun, Feida Zhu, Pengkun Jiao, Sihan Yang, Ming Ma, Steven Hoi, Erik Cambria