{"type":"ScholarlyArticle","title":"AC2-VLA: Action-Context-Aware Adaptive Computation in Vision-Language-Action Models for Efficient Robotic Manipulation","authors":[{"name":"Wenda Yu","slug":"wenda-yu","given":"Wenda","family":"Yu","affiliation":""},{"name":"Tianshi Wang","slug":"tianshi-wang","given":"Tianshi","family":"Wang","affiliation":""},{"name":"Fengling Li","slug":"fengling-li","given":"Fengling","family":"Li","affiliation":""},{"name":"Jingjing Li","slug":"jingjing-li","given":"Jingjing","family":"Li","affiliation":""},{"name":"Lei Zhu","slug":"lei-zhu","given":"Lei","family":"Zhu","affiliation":""}],"abstract":"Vision-Language-Action (VLA) models have demonstrated strong performance in robotic manipulation, yet their closed-loop deployment is hindered by the high latency and compute cost of repeatedly running large vision-language backbones at every timestep. We observe that VLA inference exhibits structured redundancies across temporal, spatial, and depth dimensions, and that most existing efficiency methods ignore action context, despite its central role in embodied tasks. To address this gap, we propose Action-Context-aware Adaptive Computation for VLA models (AC2-VLA), a unified framework that conditions computation on current visual observations, language instructions, and previous action states. Based on this action-centric context, AC2-VLA adaptively performs cognition reuse across timesteps, token pruning, and selective execution of model components within a unified mechanism. To train the adaptive policy, we introduce an action-guided self-distillation scheme that preserves the behavior of the dense VLA policy while enabling structured sparsification that transfers across tasks and settings. Extensive experiments on robotic manipulation benchmarks show that AC2-VLA achieves up to a 1.79 X speedup while reducing FLOPs to 29.4% of the dense baseline, with comparable task success.","doi":"10.24963/ijcai.2026/228","url":"https://ijcai.org/proceedings/2026/228","pdf":"https://ijcai.org/proceedings/2026/0228.pdf","markdown":"https://ijcai.org/proceedings/2026/228.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/228","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Computer Vision","track":"Main Track","number":228,"pages":{"first":2046,"last":2054},"keywords":["Robotics: Manipulation","Computer Vision: Efficiency and Optimization","Robotics: Robotics and vision"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}