Think Only When Needed: Prompt-Authority Control for Selective Slow-Path Intervention in Vision-Language-Action Manipulation
Organizations: SIGS, Tsinghua University, China · Wuhan University of Technology, China · Shanghai Jiao Tong University, China · University of Michigan - Ann Arbor, USA ·
Abstract
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 LIBERO-Plus evaluation with 10{,}030 episodes per method, success rises from 69.5% to 73.1% ( 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 (). Prompt authority is enforceable for a frozen controller; oracle-free admission calibration is the next deployment target.
Figures & tables
| Method | Setting | Camera | Robot | Language | Light | Background | Noise | Layout | Weighted Mean |
|---|---|---|---|---|---|---|---|---|---|
| OpenVLA | Direct | 0.8 | 3.5 | 23.0 | 8.1 | 34.8 | 15.2 | 28.5 | 15.6 |
| NORA | Direct | 2.2 | 37.0 | 65.1 | 45.7 | 58.6 | 12.8 | 62.1 | 39.0 |
| WorldVLA | Direct | 0.1 | 27.9 | 41.6 | 43.7 | 17.1 | 10.9 | 38.0 | 25.0 |
| UniVLA | Direct | 1.8 | 46.2 | 69.6 | 69.0 | 81.0 | 21.2 | 31.9 | 42.9 |
| Flow policy | 13.8 | 6.0 | 58.8 | 85.0 | 81.4 | 79.0 | 68.9 | 53.6 | |
| -FAST | Autoregressive | 65.1 | 21.6 | 61.0 | 73.2 | 73.2 | 74.4 | 68.8 | 61.6 |
| Variant | Task-prior | Capsule | Rerank | Fail-closed | SR (%) |
|---|---|---|---|---|---|
| Base Policy | – | – | – | – | 89.48 |
| Strategy Capsule (Top-1) | – | ✓ | – | – | 90.64 |
| Compatibility-Reranked Capsule | – | ✓ | ✓ | – | 91.60 |
| Top-2 Fail-Closed Cascade | – | ✓ | ✓ | ✓ | 91.32 |
| Complete Interface | ✓ | ✓ | ✓ | ✓ | 91.32 |
Vision-Language-Action Models
Vision-language-action models turn visual observations and natural-language instructions into actions for robots and other embodied agents.
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