Think Only When Needed: Prompt-Authority Control for Selective Slow-Path Intervention in Vision-Language-Action Manipulation
On the map: Vision-language-action models → Reasoning and chain-of-thought in VLA
Abstract (arXiv)
Retrieval can efficiently and effectively augment a frozen vision--language--action (VLA) policy without retraining, yet retrieved text becomes a control intervention once it enters the executed prompt. In a matched audit, raw appended text reduces mean success from 92.47% to 3.00%, while meaningful and length-matched meaningless appends both fail on all 500 states. This result identifies prompt-form collapse: changing the instruction form, rather than adding useful semantics, can dominate execution. We introduce TOWN-VLA (Think Only When Needed), a prompt-authority interface that separates candidate generation from permission to alter the policy input. A fixed compatibility rule authorizes a canonical compact instruction; otherwise, the interface restores the original Base prompt exactly. Across 900 audited routes, every route follows this contract: 525 routes recover Base with matching hashes, and all 375 authorized prompts preserve the task signature. On a matched 4×7 LIBERO-Plus evaluation with 10,030 episodes per method, success rises from 69.5% to 73.1% (+362 episodes; 95% CI 1.89--5.45 points), improving on six perturbation axes and all four suites. On a physical PiPER arm with a frozen pizerofive checkpoint, success rises from 52.7% to 78.7% over 150 trials per method (p=3.16×10-6). Prompt authority is enforceable for a frozen controller; oracle-free admission calibration is the next deployment target.
Key facts. Cited by 1 paper 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 32 of its 43 references that are in the map. First posted on arXiv 24 Aug 2026.
Builds on
32 of 43 references.
Closest ancestors · 3
Same direction, newest first
- 2025MAP-VLA: Memory-Augmented Prompting for Vision-Language-Action Model in Robotic Manipulation12 Nov
- 2025MemoryVLA: Perceptual-Cognitive Memory in Vision-Language-Action Models for Robotic Manipulation26 Aug
- 2025InstructVLA: Vision-Language-Action Instruction Tuning from Understanding to Manipulation23 Jul
Other references · 29
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 3,104
- 2023RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Controlcited by 3,080
- 2017Billion-scale similarity search with GPUscited by 2,497
- 2023PaLM-E: An Embodied Multimodal Language Modelcited by 2,071
- 2022RT-1: Robotics Transformer for Real-World Control at Scalecited by 2,061
- 2022Code as Policies: Language Model Programs for Embodied Controlcited by 1,210
- 2025Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Successcited by 883
- 2023Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formattingcited by 817
- 2024RDT-1B: a Diffusion Foundation Model for Bimanual Manipulationcited by 691
- 2025SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Roboticscited by 530
- 2025WorldVLA: Towards Autoregressive Action World Modelcited by 367
17 more
- 2019SelectiveNet: A Deep Neural Network with an Integrated Reject Optioncited by 350
- 2021Do Prompt-Based Models Really Understand the Meaning of their Prompts?cited by 326
- 2023Robots That Ask For Help: Uncertainty Alignment for Large Language Model Plannerscited by 308
- 2026VLA-JEPA: Enhancing Vision-Language-Action Model with Latent World Modelcited by 118
- 2024HiRT: Enhancing Robotic Control with Hierarchical Robot Transformerscited by 75
- 2025MemER: Scaling Up Memory for Robot Control via Experience Retrievalcited by 67
- 2026World Action Models: The Next Frontier in Embodied AIcited by 50
- 2026Learning Latent Action World Models In The Wildcited by 47
- 2026Harness VLA: Steering Frozen VLAs into Reliable Manipulation Primitives via Memory-Guided Agentscited by 47
- 2025Fast-in-Slow: A Dual-System Foundation Model Unifying Fast Manipulation within Slow Reasoningcited by 31
- 2026STRONG-VLA: Decoupled Robustness Learning for Vision-Language-Action Models under Multimodal Perturbationscited by 10
- 2025Asynchronous Fast-Slow Vision-Language-Action Policies for Whole-Body Robotic Manipulationcited by 7
- 2026Learning What to Say to Your VLA: Mostly Harmless Vision Language Action Model Steeringcited by 4
- 2026CoRE-VLA: Towards Scalable and Robust Vision-Language-Action Modeling via Conditional Routing of Expertscited by 4
- 2026World Pilot: Steering Vision-Language-Action Models with World-Action Priorscited by 3
- 2026Red-Teaming Vision-Language-Action Models via Quality Diversity Prompt Generation for Robust Robot Policiescited by 2
- 2026RouterVLA: Budgeted Commissioning and Expert Onboarding for Growing VLA Poolscited by 2
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Citing papers per year: 2026: 1.
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- 2026ChainVLA: Chaining Vision-Language-Action Queries through a Unified Execution State for Long-Horizon Manipulation
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