# ChainVLA: Chaining Vision-Language-Action Queries through a Unified Execution State for Long-Horizon Manipulation

arXiv: https://arxiv.org/abs/2608.02326 · HTML: https://mlsanity.com/paper/2608.02326/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning and chain-of-thought in VLA, one of 13 directions in Vision-language-action models.
- Builds on 28 of its 36 references that are in the map.
- First posted on arXiv 3 Aug 2026.

## Placed in

- Vision-language-action models / [Reasoning and chain-of-thought in VLA](https://mlsanity.com/vla/reasoning-and-chain-of-thought-in-vla/)

## Builds on (most cited first, 28 of 28)

- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024)
- [π0.5: a Vision-Language-Action Model with Open-World Generalization](https://mlsanity.com/paper/2504.16054/) (2025)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning](https://mlsanity.com/paper/2306.03310/) (2023)
- [SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model](https://mlsanity.com/paper/2501.15830/) (2025)
- [Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks](https://mlsanity.com/paper/2311.06242/) (2023)
- [X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model](https://mlsanity.com/paper/2510.10274/) (2025)
- [Real-Time Execution of Action Chunking Flow Policies](https://mlsanity.com/paper/2506.07339/) (2025)
- [MemoryVLA: Perceptual-Cognitive Memory in Vision-Language-Action Models for Robotic Manipulation](https://mlsanity.com/paper/2508.19236/) (2025)
- [MemER: Scaling Up Memory for Robot Control via Experience Retrieval](https://mlsanity.com/paper/2510.20328/) (2025)
- [Bidirectional Decoding: Improving Action Chunking via Guided Test-Time Sampling](https://mlsanity.com/paper/2408.17355/) (2024)
- [RoboMME: Benchmarking and Understanding Memory for Robotic Generalist Policies](https://mlsanity.com/paper/2603.04639/) (2026)
- [RMBench: Memory-Dependent Robotic Manipulation Benchmark with Insights into Policy Design](https://mlsanity.com/paper/2603.01229/) (2026)
- [CronusVLA: Towards Efficient and Robust Manipulation via Multi-Frame Vision-Language-Action Modeling](https://mlsanity.com/paper/2506.19816/) (2025)
- [EventVLA: Event-Driven Visual Evidence Memory for Long-Horizon Vision-Language-Action Policies](https://mlsanity.com/paper/2606.20092/) (2026)
- [Rethinking Progression of Memory State in Robotic Manipulation: An Object-Centric Perspective](https://mlsanity.com/paper/2511.11478/) (2025)
- [Learning Native Continuation for Action Chunking Flow Policies](https://mlsanity.com/paper/2602.12978/) (2026)
- [Goal2Skill: Long-Horizon Manipulation with Adaptive Planning and Reflection](https://mlsanity.com/paper/2604.13942/) (2026)
- [Non-Markovian Long-Horizon Robot Manipulation via Keyframe Chaining](https://mlsanity.com/paper/2603.01465/) (2026)
- [HiMem-WAM: Hierarchical Memory-Gated World Action Models for Robotic Manipulation](https://mlsanity.com/paper/2606.10363/) (2026)
- [SEAM: Smooth Execution of Action-Chunked Motion for Vision-Language-Action Policies](https://mlsanity.com/paper/2607.04609/) (2026)
- [Resolving State Ambiguity in Robot Manipulation via Adaptive Working Memory Recoding](https://mlsanity.com/paper/2512.24638/) (2025)
- [RoboStream: Weaving Spatio-Temporal Reasoning with Memory in Vision-Language Models for Robotics](https://mlsanity.com/paper/2603.12939/) (2026)
- [HELM: Harness-Enhanced Long-horizon Memory for Vision-Language-Action Manipulation](https://mlsanity.com/paper/2604.18791/) (2026)
- [TempoFit: Plug-and-Play Layer-Wise Temporal KV Memory for Long-Horizon Vision-Language-Action Manipulation](https://mlsanity.com/paper/2603.07647/) (2026)
- [KEMO: Event-Driven Keyframe Memory for Long-Horizon Robot Manipulation with VLA Policies](https://mlsanity.com/paper/2606.23589/) (2026)
- [Chameleon: Control-Indexed Prospective Memory for Visuomotor Manipulation](https://mlsanity.com/paper/2603.24576/) (2026)

## Built on by (most cited first, 2 of 2)

- [SimpleMemVLA: A Simple but Effective Native-Video Memory for Vision-Language-Action Models](https://mlsanity.com/paper/2609.05533/) (2026)
- [Remember What You Did: Action-History Memory with Dual-Expert Denoising for Long-Horizon Vision-Language-Action Policies](https://mlsanity.com/paper/2609.37307/) (2026)

## Abstract (arXiv)

Humans perform long-horizon manipulation by retaining knowledge of what earlier actions have established while continuously adapting the motion underway. By contrast, action-chunked vision-language-action (VLA) policies repeatedly replan from the current input at each query. Existing methods preserve either long-term task evidence through memory or short-term motion through action reuse and ensembling, leaving the cross-query handoff incomplete. We introduce ChainVLA, a 1.2B-parameter VLA policy that chains successive queries through a joint and revisable execution state. Progress Context combines a recurrent Working State with sparse event memory to carry observation-derived task progress, while Motion Tail feeds the preceding prediction's unexecuted continuation into state construction and action generation. Together, the two components condition a decoder that regenerates each action horizon under the latest observation, allowing the carried state to guide the next prediction without fixing it. ChainVLA reaches 62.8% average success on RMBench and 98.8% across four LIBERO suites, while removing Motion Tail or Progress Context reduces RMBench success to 11.2% and 3.0%, respectively. These asymmetric ablations are consistent with motion continuity helping preserve the observation stream from which task progress is inferred.