{"ID":5438645,"CreatedAt":"2026-07-01T01:17:58.482524686Z","UpdatedAt":"2026-07-03T05:54:49.125664311Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.31167","arxiv_id":"2606.31167","title":"MIRTH: Mutual-Information Reasoning with Temporal Hubs for Vision-Language-Action Agents","abstract":"VLA models have emerged as a powerful paradigm for transferring semantic knowledge from web-scale data to physical robotic control. However, current single-frame architectures suffer from intrinsic limitations: temporal myopia that discards historical dynamics, reasoning gaps between high-level instructions and low-level motor commands, and inference inefficiency due to autoregressive scalar decoding. In this work, we propose MIRTH, a unified framework designed to address these challenges. MIRTH augments a pretrained VLA backbone with three key innovations: (1) dual-scale temporal memory hubs that compress long-term scene evolution and short-term motion trends into compact embeddings; (2) latent reasoning tokens optimized via a mutual-information objective carving out a semantic plan space to align multimodal context with action trajectories; and (3) a parallel action decoding scheme that replaces autoregressive generation with vector-wise prediction to maximize control throughput. Extensive evaluations on the LIBERO simulation benchmark and a real-world LeRobot platform demonstrate that MIRTH achieves state-of-the-art performance and exhibiting emergent error recovery capabilities. The codes and collected datasets are released at http://github.com/kiva12138/mirth.","short_abstract":"VLA models have emerged as a powerful paradigm for transferring semantic knowledge from web-scale data to physical robotic control. However, current single-frame architectures suffer from intrinsic limitations: temporal myopia that discards historical dynamics, reasoning gaps between high-level instructions and low-lev...","url_abs":"https://arxiv.org/abs/2606.31167","url_pdf":"https://arxiv.org/pdf/2606.31167v1","authors":"[\"Hao Sun\",\"Yu Song\",\"Shiyu Teng\",\"Ziwei Niu\",\"Yen-Wei Chen\"]","published":"2026-06-30T05:57:13Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\"]","methods":"[]","has_code":false,"code_links":[{"ID":613769,"CreatedAt":"2026-07-01T01:17:58.482524686Z","UpdatedAt":"2026-07-01T01:17:58.482524686Z","DeletedAt":null,"paper_id":5438645,"paper_url":"https://arxiv.org/abs/2606.31167","paper_title":"MIRTH: Mutual-Information Reasoning with Temporal Hubs for Vision-Language-Action Agents","repo_url":"https://github.com/kiva12138/mirth","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}