MIRTH: Mutual-Information Reasoning with Temporal Hubs for Vision-Language-Action Agents

• URL: http://arxiv.org/abs/2606.31167v1
• Date: Tue, 30 Jun 2026 05:57:13 GMT
• Title: MIRTH: Mutual-Information Reasoning with Temporal Hubs for Vision-Language-Action Agents
• Abstract summary: VLA models have emerged as a powerful paradigm for transferring semantic knowledge from web-scale data to physical robotic control.<n>MIRTH is a unified framework designed to address these challenges.<n>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
• Score: 15.061825959360192
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Embodied-Navigator: Point, Think, Memorize, and Align for Efficient Navigation [68.02899606570223]
  We propose TAMP-Nav, a unified framework for efficient embodied navigation.<n>First, we introduce a Pixel-to-3D Action Formulation (Point) that reformulates navigation into 2D visual coordinates.<n>Second, we propose an integrated Selective Reasoning and Anchor-Trajectory Memory mechanism (Think and Memorize)
  arXiv  Detail & Related papers  (2026-08-18T08:37:08Z)
• DexWorldModel: Causal Latent World Modeling towards Automated Learning of Embodied Tasks [54.32016216994156]
  We introduce the Causal Latent World Model (CLWM), which employs DINOv3 features as generative targets to disentangle interaction semantics from visual noise.<n>To overcome memory scaling, CLWM features a Dual-State Test-Time Training (TTT) Memory that guarantees a strict $mathcalO(1)$ footprint for long-horizon tasks.<n>We present EmbodiChain, an online framework that establishes the Efficiency Law by injecting an infinite flow of physics-grounded trajectories during training.
  arXiv  Detail & Related papers  (2026-04-13T03:19:36Z)
• Beyond Short-Horizon: VQ-Memory for Robust Long-Horizon Manipulation in Non-Markovian Simulation Benchmarks [96.60530830276281]
  RuleSafe is a new articulated manipulation benchmark built upon a scalable LLM-aided simulation framework.<n>VQ-Memory is a compact and structured temporal representation that uses vector-quantized variational autoencoders.
  arXiv  Detail & Related papers  (2026-03-10T11:13:54Z)
• ABot-M0: VLA Foundation Model for Robotic Manipulation with Action Manifold Learning [31.000965640377128]
  ABot-M0 is a framework that builds a systematic data curation pipeline.<n>It enables end-to-end transformation of heterogeneous raw data into unified, efficient representations.<n>ABot-M0 supports modular perception via a dual-stream mechanism.
  arXiv  Detail & Related papers  (2026-02-11T16:47:01Z)
• BiManiBench: A Hierarchical Benchmark for Evaluating Bimanual Coordination of Multimodal Large Language Models [30.38546017617527]
  We introduce BiManiBench, a benchmark evaluating Multi Large Language Models (MLLMs) across three tiers.<n>Our framework isolates unique bimanual challenges, such as arm reachability and kinematic constraints.<n>Analysis of over 30 state-of-the-art models reveals that despite high-level reasoning proficiency, MLLMs struggle with dual-arm spatial grounding and control.
  arXiv  Detail & Related papers  (2026-02-09T08:47:14Z)
• T2M Mamba: Motion Periodicity-Saliency Coupling Approach for Stable Text-Driven Motion Generation [3.6564162676635363]
  Text-to-motion generation has attracted increasing attention in fields such as avatar animation and humanoid robotic interaction.<n>Models treat motion periodicity and saliency as independent factors, overlooking their coupling and causing generation drift in long sequences.<n>We propose T2M Mamba to address these limitations by (i) proposing Periodicity-Saliency Aware Mamba.
  arXiv  Detail & Related papers  (2026-02-01T17:42:53Z)
• ColaVLA: Leveraging Cognitive Latent Reasoning for Hierarchical Parallel Trajectory Planning in Autonomous Driving [44.008287454538596]
  Vision-language models (VLMs) enrich this paradigm by introducing cross-modal priors and commonsense reasoning.<n>Current VLM-based planners face three key challenges: (i) a mismatch between discrete text reasoning and continuous control, (ii) high latency from autoregressive chain-of-thought decoding, and (iii) inefficient or non-causal planners that limit real-time deployment.<n>We propose ColaVLA, a unified vision-language-action framework that transfers reasoning from text to a unified latent space and couples it with a hierarchical, parallel trajectory decoder.
  arXiv  Detail & Related papers  (2025-12-28T14:06:37Z)
• Learning Primitive Embodied World Models: Towards Scalable Robotic Learning [50.32986780156215]
  We propose a novel paradigm for world modeling--Primitive Embodied World Models (PEWM)<n>By restricting video generation to fixed short horizons, our approach enables fine-grained alignment between linguistic concepts and visual representations of robotic actions.<n>Our framework bridges the gap between fine-grained physical interaction and high-level reasoning, paving the way toward scalable, interpretable, and general-purpose embodied intelligence.
  arXiv  Detail & Related papers  (2025-08-28T14:31:48Z)
• MEgoHand: Multimodal Egocentric Hand-Object Interaction Motion Generation [28.75149480374178]
  MEgoHand is a framework that synthesizes physically plausible hand-object interactions from egocentric RGB, text, and initial hand pose.<n>It achieves substantial reductions in wrist translation error and joint rotation error, highlighting its capacity to accurately model fine-grained hand joint structures.
  arXiv  Detail & Related papers  (2025-05-22T12:37:47Z)
• ReCoM: Realistic Co-Speech Motion Generation with Recurrent Embedded Transformer [58.49950218437718]
  We present ReCoM, an efficient framework for generating high-fidelity and generalizable human body motions synchronized with speech.<n>The core innovation lies in the Recurrent Embedded Transformer (RET), which integrates Dynamic Embedding Regularization (DER) into a Vision Transformer (ViT) core architecture.<n>To enhance model robustness, we incorporate the proposed DER strategy, which equips the model with dual capabilities of noise resistance and cross-domain generalization.
  arXiv  Detail & Related papers  (2025-03-27T16:39:40Z)
• SuperFlow++: Enhanced Spatiotemporal Consistency for Cross-Modal Data Pretraining [62.433137130087445]
  SuperFlow++ is a novel framework that integrates pretraining and downstream tasks using consecutive camera pairs.<n>We show that SuperFlow++ outperforms state-of-the-art methods across diverse tasks and driving conditions.<n>With strong generalizability and computational efficiency, SuperFlow++ establishes a new benchmark for data-efficient LiDAR-based perception in autonomous driving.
  arXiv  Detail & Related papers  (2025-03-25T17:59:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.