Semantic and Visual Evidence for Efficient Long-Video Reasoning: A Solution for the HD-EPIC VQA Challenge
arXiv:2605.29402v1 Announce Type: cross Abstract: Understanding long-form egocentric videos remains challenging for multimodal large language models (MLLMs) due to limited context length and insufficient grounding of fine-grained visual details. The recently proposed HD-EPIC benchmark highlights these limitations: even strong long-context models achieve relatively low performance across diverse video question answering tasks. In this paper, we propose a unified framework that decouples long-video reasoning into two complementary forms of evidence: semantic evidence and visual evidence. Semanti
Advances in multimodal AI models are pushing boundaries, and the HD-EPIC benchmark specifically highlights current limitations in processing long-form video, prompting research into new architectural approaches.
Efficiently understanding long-form video is a critical unsolved problem for AI, impacting applications from autonomous systems to human-computer interaction and surveillance, making this a foundational research area.
This research proposes a new framework for long-video reasoning by decoupling semantic and visual evidence, which could lead to more robust and scalable multimodal AI capable of processing extended, complex visual data.
- · AI researchers and developers
- · Video analytics companies
- · Robotics and autonomous systems
- · Generative AI platforms
- · Legacy video processing methods
- · Models reliant on short context windows
Improved performance of multimodal large language models on complex, long-duration video tasks.
Accelerated development of AI agents capable of understanding and interacting with dynamic extended environments.
New forms of automated content generation and analysis, potentially transforming media, security, and assistive technologies.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI