Recursive Belief Vision Language Action Models

• URL: http://arxiv.org/abs/2602.20659v2
• Date: Wed, 25 Feb 2026 17:38:24 GMT
• Title: Recursive Belief Vision Language Action Models
• Authors: Vaidehi Bagaria, Bijo Sebastian, Nirav Kumar Patel,
• Abstract summary: Long-horizon manipulation requires persistent, action-conditioned state representations.<n>Current vision-language models exhibit limited temporal and physical reasoning.<n>This paper introduces RB-VLA, a belief-centric architecture trained with self-supervised world-model objectives.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-language-action models must enable agents to execute long-horizon tasks under partial observability. However, most existing approaches remain observation-driven, relying on short context windows or repeated queries to vision-language models (VLMs). This leads to loss of task progress, action repetition under perceptual aliasing, and high inference latency. While semantic grounding is important, long-horizon manipulation fundamentally requires persistent, action-conditioned state representations. Current VLAs lack such representations and exhibit limited temporal and physical reasoning, making them ill-suited for multi-stage control. This paper introduces RB-VLA, a belief-centric architecture trained with self-supervised world-model objectives that maintains a compact latent state encoding task-relevant history, dynamics, and object interactions. Queried once per task, the VLM provides high-level intent, while the belief tracks task progress and enables phase-aware, causally grounded control under partial observability without storing raw observations or scaling memory with time. The belief and intent jointly condition a diffusion policy for robust closed-loop execution. RB-VLA outperforms prior VLAs on long-horizon benchmarks, achieving 52.5 percent and 37.5 percent higher success rates on multi-stage pick-and-place and stacking tasks, respectively, compared to pi_0. It also reduces inference latency by up to five times relative to baselines and eliminates memory growth across timesteps observed in existing VLAs. Ablations show the belief module is the primary driver of performance, increasing success rates from 32.5 percent without belief to 77.5 percent with belief.

Related papers

• Global Prior Meets Local Consistency: Dual-Memory Augmented Vision-Language-Action Model for Efficient Robotic Manipulation [95.89924101984566]
  We introduce OptimusVLA, a dual-memory VLA framework with Global Prior Memory (GPM) and Local Consistency Memory (LCM)<n>GPM replaces Gaussian noise with task-level priors retrieved from semantically similar trajectories.<n>LCM injects a learned consistency constraint that enforces temporal coherence and smoothness of trajectory.
  arXiv  Detail & Related papers  (2026-02-22T15:39:34Z)
• Vision-aligned Latent Reasoning for Multi-modal Large Language Model [82.26044667101011]
  Vision-aligned Latent Reasoning (VaLR) is a framework that dynamically generates vision-aligned latent tokens before each Chain of Thought reasoning step.<n>VaLR is trained to preserve visual knowledge during reasoning by aligning intermediate embeddings of MLLM with those from vision encoders.
  arXiv  Detail & Related papers  (2026-02-04T12:04:02Z)
• Efficient Long-Horizon Vision-Language-Action Models via Static-Dynamic Disentanglement [27.517125673741486]
  Vision-Language-Action (VLA) models have emerged as a promising paradigm for generalist robotic control.<n>We propose SD-VLA, a framework that disentangles visual inputs into multi-level static and dynamic tokens.<n>We introduce a new benchmark that more effectively evaluates the long-horizon temporal dependency modeling ability of VLAs.
  arXiv  Detail & Related papers  (2026-02-03T20:17:47Z)
• Vision-Language Models Unlock Task-Centric Latent Actions [75.53481518882275]
  We propose to utilize the common-sense reasoning abilities of Vision-Language Models (VLMs) to provide promptable representations.<n>We show that simply asking VLMs to ignore distractors can substantially improve latent action quality, yielding up to a six-fold increase in downstream success rates on Distracting MetaWorld.
  arXiv  Detail & Related papers  (2026-01-30T08:38:59Z)
• ReViP: Reducing False Completion in Vision-Language-Action Models with Vision-Proprioception Rebalance [50.05984919728878]
  We present ReViP, a novel VLA framework with Vision-Proprioception Rebalance to enhance visual grounding and robustness under perturbations.<n>Specifically, we use an external VLM as a task-stage observer to extract real-time task-centric visual cues from visual observations.<n>To evaluate false completion, we propose the first False-Completion Benchmark Suite built on LIBERO with controlled settings such as Object-Drop.
  arXiv  Detail & Related papers  (2026-01-23T11:31:07Z)
• Value Vision-Language-Action Planning & Search [1.631000263754549]
  Vision-Language-Action (VLA) models have emerged as powerful generalist policies for robotic manipulation.<n>We introduce Value Vision-Language-Action Planning and Search (V-VLAPS), a framework that augments Monte Carlo Tree Search with a lightweight, learnable value function.<n>We evaluate V-VLAPS on the LIBERO robotic manipulation suite, demonstrating that our value-guided search improves success rates by over 5 percentage points.
  arXiv  Detail & Related papers  (2026-01-02T19:40:34Z)
• EvoVLA: Self-Evolving Vision-Language-Action Model [11.746804244345613]
  Long-horizon robotic manipulation remains challenging for Vision-Language-Action (VLA) models.<n>We present EvoVLA, a self-supervised VLA framework that addresses this issue through three complementary components.<n>EvoVLA achieves one-and-a-half times better sample efficiency and reduces stage hallucination from 38.5 percent to 14.8 percent.
  arXiv  Detail & Related papers  (2025-11-20T09:08:33Z)
• ContextVLA: Vision-Language-Action Model with Amortized Multi-Frame Context [54.58057019521198]
  Leveraging temporal context is crucial for success in partially observable robotic tasks.<n>Prior work in behavior cloning has demonstrated inconsistent performance gains when using multi-frame observations.<n>We introduce ContextVLA, a policy model that robustly improves robotic task performance by effectively leveraging multi-frame observations.
  arXiv  Detail & Related papers  (2025-10-05T15:29:57Z)
• dVLA: Diffusion Vision-Language-Action Model with Multimodal Chain-of-Thought [66.78110237549087]
  Vision-Language-Action (VLA) models are emerging as a next-generation paradigm for robotics.<n>We introduce dVLA, a diffusion-based VLA that unifies visual perception, language reasoning, and robotic control in a single system.
  arXiv  Detail & Related papers  (2025-09-30T02:36:11Z)
• CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models [89.44024245194315]
  We introduce a method that incorporates explicit visual chain-of-thought (CoT) reasoning into vision-language-action models (VLAs)<n>We introduce CoT-VLA, a state-of-the-art 7B VLA that can understand and generate visual and action tokens.<n>Our experimental results demonstrate that CoT-VLA achieves strong performance, outperforming the state-of-the-art VLA model by 17% in real-world manipulation tasks and 6% in simulation benchmarks.
  arXiv  Detail & Related papers  (2025-03-27T22:23:04Z)

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.