The paper reports an entry to the EgoLongQA track of the Wearable‑AI Challenge at ECCV 2026 that achieved first place in the ≤2 B parameter division with a score of 0.8279 on the held‑out test set. The system is a single 2 B vision‑language model that answers multiple‑choice questions about ten‑minute egocentric videos in one greedy forward pass, obtained by distilling the junior perception module of a tool‑using agentic pipeline into a small student using teacher traces that answered correctly. By pruning the multilingual embedding table from 248,320 to 143,469 rows, the authors reduce the model to 1.9985 B parameters while maintaining identical logits on retained rows, enabling the model to reach 89 % of the accuracy of the large agentic pipeline with only 1.1 % of its parameters. whyItMatters":"The work demonstrates that a highly efficient, sub‑2 B parameter vision‑language model can match the performance of a much larger agentic pipeline on long‑video perception tasks, showing the feasibility of deploying such models in resource‑constrained settings."
arXiv:2608. 04589v1 Announce Type: cross Abstract: EgoCross is a cross-domain egocentric video question answering benchmark designed to evaluate whether multimodal large language models can generalize beyond common daily-life scenarios.
arXiv:2609.16255v1 Announce Type: cross Abstract: We present an efficient method to distill reasoning capabilities into compact video-language models (VLMs) for video question answering (VideoQA). Ou...
arXiv:2609.37938v1 Announce Type: cross Abstract: Embodied systems must make knowledge acquired during one encounter usable in another despite changes in viewpoint, motion, and illumination. Yet aggr...
arXiv:2609.07099v1 Announce Type: cross Abstract: We present our submission to the EgoProactive track of the ECCV 2026 Wearable AI Challenge, which ranked first in the large-model division and second...
The paper introduces WROP, a dataset of 150 hand‑designed cognitive tasks aimed at testing object permanence in video generation models. Using Blender, the authors generate over 10,000 samples per task, compiling a 1.5 M‑sample training corpus and a 300‑question exam. They evaluate 14 video models, showing that their 16B world model, PWM‑WROP, ranks first among continuation models and third overall in a blind Elo study.
arXiv:2608. 13113v1 Announce Type: cross Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have led to substantial progress in video understanding, accompanied by a growing number of long video benchmarks.
arXiv:2609.12818v1 Announce Type: new Abstract: Long video understanding often behaves like a visual needle-in-a-haystack problem: query-relevant evidence is sparsely distributed across long temporal...
Zero-shot waypoint navigation requires vision-language models to select, from the current first-person observation, a sequence of spatial actions that is feasible for the agent and reaches the goal, p...
Embodied systems must make knowledge acquired during one encounter usable in another despite changes in viewpoint, motion, and illumination. Yet aggregate cross-video accuracy conflates failures of lo...
arXiv:2607. 25487v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models translate natural-language commands into robot action sequences, but leading systems on the LIBERO-Plus robustness benchmark use three- to seven-billion-parameter backbones whose memory demands can exceed embedded robotic budgets.
arXiv:2607. 02959v1 Announce Type: cross Abstract: We introduce VSeek, an agentic framework that transforms long-video question answering (LVQA) from a passive, single-pass perception task into a multi-turn retrieval process.
arXiv:2609.15606v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress on short video understanding yet remain limited on long videos due to the...