# Planning Takes More Than Token Prediction: Causal Plan for Benchmarking and Building Physically Grounded Embodied Reasoners

arXiv: https://arxiv.org/abs/2606.01810 · HTML: https://mlsanity.com/paper/2606.01810/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 1 Jun 2026.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)

## Builds on (most cited first, 11 of 11)

- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency](https://mlsanity.com/paper/2508.18265/) (2025)
- [Kimi K2.5: Visual Agentic Intelligence](https://mlsanity.com/paper/2602.02276/) (2026)
- [AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems](https://mlsanity.com/paper/2503.06669/) (2025)
- [Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning](https://mlsanity.com/paper/2503.15558/) (2025)
- [RoboBrain 2.0 Technical Report](https://mlsanity.com/paper/2507.02029/) (2025)
- [RoboBrain 2.5: Depth in Sight, Time in Mind](https://mlsanity.com/paper/2601.14352/) (2026)
- [MiMo-Embodied: X-Embodied Foundation Model Technical Report](https://mlsanity.com/paper/2511.16518/) (2025)
- [DSBench: A Comprehensive Benchmark for Evaluating External and In-Cabin Risks](https://mlsanity.com/paper/2511.14592/) (2025)

## Built on by (most cited first, 3 of 3)

- [Hy-Embodied-VLM-1.0: Efficient Physical-World Agents](https://mlsanity.com/paper/2607.12894/) (2026)
- [LayerRoute: Action-Conditioned Mixture-of-Layers Routing for Vision-Language-Action Policies](https://mlsanity.com/paper/2609.06079/) (2026)
- [Acting in Meters: Learning Metric Interactions for Precise Robotic Manipulation](https://mlsanity.com/paper/2609.18243/) (2026)

## Abstract (arXiv)

Current benchmarks for embodied vision-language planning inadvertently favor linguistic next-token prediction over physically grounded next-state reasoning. This rewards models that mimic statistical language priors rather than track true causal dependencies, reducing complex physical planning to shallow sequence modeling. Hence, achieving genuine physical autonomy requires a fundamental shift from linguistically grounded token prediction toward physically grounded causal reasoning. To this end, we introduce Causal-Plan-Bench, a high-fidelity diagnostic suite spanning four causal dimensions, curated via multi-stage verification. To endow models with this capability, a four-stage annotation pipeline extracts structured interaction records from egocentric videos to construct Causal-Plan-1M, a dense million-scale corpus of explicit causal reasoning traces. Extensive evaluation reveals a striking gap: leading models struggle to demonstrate genuine physical agency -- even GPT-6-astra scores only 43.04. In contrast, our tailored training recipe enables Causal Planner to internalize the complex physical logic required for accurate next-state estimation. Built upon Qwen3-VL-8B, Causal Planner raises its backbone's score from 33.23 to 45.28, a 36.3% relative gain, and improves on three external benchmarks without benchmark-specific adaptation. We further observe an empirical Causal-Supervision Scaling Trend. Paired no-vision controls also reveal substantial visual dependence, while cross-judge comparisons and human scoring assess the reliability of automated evaluation. More importantly, we initiate the first effort to turn agents from superficial token predictors into physically grounded causal reasoners, bridging language modeling and world modeling.