Procedural activities are fundamentally driven by object state transitions, yet existing instructional video benchmarks remain action-centric and cannot evaluate whether models reason about how objects evolve toward task completion. In this work, we introduce ProcObject-10K, the first benchmark that jointly evaluates object-centric reasoning and temporal evidence grounding in instructional videos, across both egocentric and exocentric views. It comprises 10,522 open-ended VideoQA pairs grounded in 1,799 video clips, spanning 137 tasks across 9 domains and five reasoning types covering preconditions, state evolution, counterfactuals, mistakes, and readiness. Benchmarking 13 leading MLLMs reveals a substantial answering-grounding gap: models produce plausible answers while failing to localize the supporting evidence (mIoU < 45%), exposing their reliance on linguistic priors rather than fine-grained object dynamics. As a step toward closing this gap, we further provide an object-centric supervised fine-tuning baseline with pseudo object-level supervision and spatial-temporal constraints. Models fine-tuned on ProcObject-10K not only improve on the benchmark itself, but also transfer effectively to other grounded VideoQA and embodied planning tasks. The dataset, annotations, and evaluation toolkit will be publicly released to support future research on object-centric procedural understanding.
Carnegie Mellon University（卡内基梅隆大学）；Microsoft Research Asia（微软亚洲研究院）；Beijing Institute of Technology（北京理工大学）；University of Southern California（南加利福尼亚大学）
RECIPE: Procedural Planning via Grounding in Instructional Video
Khoury College of Computer Sciences, Northeastern University, Boston（东北大学北斯托顿学院计算机科学学院）；Department of Mathematics and Computer Science, University of Catania, Italy（卡塔尼亚大学数学与计算机科学系）
Why Not Use Your Textbook? Knowledge-Enhanced Procedure Planning of Instructional Videos
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；NEC Laboratories（NEC实验室）；University of Auckland（奥克兰大学）；Weizmann Institute of Science（魏茨曼科学研究所）
Neuro Symbolic Knowledge Reasoning for Procedural Video Question Answering
Centre for Frontier AI Research, A*STAR, Singapore（前沿人工智能研究所以（A*STAR））；Institute of High-Performance Computing, A*STAR, Singapore（高性能计算研究所（A*STAR））；College of Computing and Data Science, NTU, Singapore（计算与数据科学学院（NTU））
Exo2EgoDVC: Dense Video Captioning of Egocentric Procedural Activities Using Web Instructional Videos
The University of Tokyo（东京大学）；OMRON SINIC X Corp.（欧姆龙SINIC X公司）；National Institute of Advanced Industrial Science and Technology (AIST)（日本产业技术综合研究所）；LY Corporation（LY公司）
Overview of the NLPCC 2026 Shared Task 1: Difficulty-Aware Multilingual and Multimodal Medical Instructional Video Understanding Evaluation
School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与工程学院）；Department of Computing, The Hong Kong Polytechnic University（香港理工大学计算学系）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
