# Test-Time Scaling for World Action Models via Zero-Shot Geometric Evaluation

arXiv: https://arxiv.org/abs/2607.17454 · HTML: https://mlsanity.com/paper/2607.17454/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and robot world models, one of 11 directions in World models.
- Builds on 19 of its 29 references that are in the map.
- First posted on arXiv 20 Jul 2026.

## Placed in

- World models / [Embodied and robot world models](https://mlsanity.com/wm/embodied-and-robot-world-models/)
- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)

## Builds on (most cited first, 19 of 19)

- [RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation](https://mlsanity.com/paper/2506.18088/) (2025)
- [World Action Models are Zero-shot Policies](https://mlsanity.com/paper/2602.15922/) (2026)
- [RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots](https://mlsanity.com/paper/2406.02523/) (2024)
- [Fast-WAM: Do World Action Models Need Test-time Future Imagination?](https://mlsanity.com/paper/2603.16666/) (2026)
- [Causal World Modeling for Robot Control](https://mlsanity.com/paper/2601.21998/) (2026)
- [Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning](https://mlsanity.com/paper/2601.16163/) (2026)
- [Motus: A Unified Latent Action World Model](https://mlsanity.com/paper/2512.13030/) (2025)
- [Unified 4D World Action Modeling from Video Priors with Asynchronous Denoising](https://mlsanity.com/paper/2604.26694/) (2026)
- [Motubrain: An Advanced World Action Model for Robot Control](https://mlsanity.com/paper/2604.27792/) (2026)
- [AHA-WAM:Asynchronous Horizon-Adaptive World-Action Modeling with Observation-Guided Context Routing](https://mlsanity.com/paper/2606.09811/) (2026)
- [τ0-WM: A Unified Video-Action World Model for Robotic Manipulation](https://mlsanity.com/paper/2606.01027/) (2026)
- [Light-WAM: Efficient World Action Models with State-Fusion Action Decoding](https://mlsanity.com/paper/2606.08242/) (2026)
- [When to Trust Imagination: Adaptive Action Execution for World Action Models](https://mlsanity.com/paper/2605.06222/) (2026)
- [RoVer: Robot Reward Model as Test-Time Verifier for Vision-Language-Action Model](https://mlsanity.com/paper/2510.10975/) (2025)
- [World Action Verifier: Self-Improving World Models via Forward-Inverse Asymmetry](https://mlsanity.com/paper/2604.01985/) (2026)
- [Efficient-WAM: A 1B-Parameter World-Action Model with Low-Cost Future Imagination](https://mlsanity.com/paper/2606.10040/) (2026)
- [Is the Future Compatible? Diagnosing Dynamic Consistency in World Action Models](https://mlsanity.com/paper/2605.07514/) (2026)
- [C3ache: Accelerating World Action Models with Cross Inference Chunk Cache](https://mlsanity.com/paper/2606.08962/) (2026)
- [SANTS: A State-Adaptive Scheduler for World Action Models](https://mlsanity.com/paper/2605.27947/) (2026)

## Built on by (most cited first, 6 of 6)

- [Teach and Grow: An Agent-Centered Architecture for General Robot Learning](https://mlsanity.com/paper/2608.17209/) (2026)
- [Paired Exact-Reset Evaluation of a Prediction-Derived Medium-to-Full World-Model Cascade](https://mlsanity.com/paper/2608.14650/) (2026)
- [Beyond Visual Quality: A Study of Test-Time Planning with World Action Models](https://mlsanity.com/paper/2609.24745/) (2026)
- [Think Fast, Plan Selectively: Adaptive Deliberation for Efficient Data-Driven MPC](https://mlsanity.com/paper/2609.32591/) (2026)
- [ReSync: Re-Aligning the Two Clocks of Asynchronous World-Action Models](https://mlsanity.com/paper/2609.33944/) (2026)
- [Spotter: Let the Embodied Model Lead, and the VLM Reflect for It](https://mlsanity.com/paper/2609.36808/) (2026)

## Abstract (arXiv)

Test-time scaling improves foundation-model inference by spending additional computation, but robot control requires deciding whether extra compute is useful before executing an action. World Action Models (WAMs) make this decision natural: each rollout exposes both an action chunk and predicted future observations. We propose methodgated, a training-free selective test-time scaling framework for WAMs. We first instantiate method, a fixed-budget Best-of-N selector that ranks sampled rollouts by cross-view depth reprojection consistency of their predicted futures, computed with a frozen geometry foundation model. methodgated adds a lightweight action--future consistency gate that invokes method only when the initial rollout appears internally inconsistent. Across five benchmark--backbone settings on RoboCasa, LIBERO Long, and RoboTwin~2.0, fixed-budget method improves N=8 task success in every setting, e.g., raising the RoboCasa group average from 66.3% to 68.4% with Cosmos Policy and from 80.8% to 82.5% with X-WAM. With gating enabled, methodgated recovers on average 74.8% of the always-on success gain while triggering additional sampling on only 26.2% of decision points. Offline diagnostics show that cross-view reprojection is a strong task-label-free selector, and we identify false low-score selections as a failure mode that helps explain why performance can saturate or degrade as N increases.