IMWM: Intuition Models Complement World Models for Latent Planning

• URL: http://arxiv.org/abs/2606.01626v1
• Date: Mon, 01 Jun 2026 03:23:25 GMT
• Title: IMWM: Intuition Models Complement World Models for Latent Planning
• Abstract summary: We show that a finite-budget sample-based planner still fails on some tasks, indicating that the bottleneck can lie in search rather than in world-model accuracy.<n>Motivated by this gap, we propose IMWM, which pairs the world model with an intuition model trained from demonstrations to recognize promising actions.
• Score: 21.033541866844683
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Planning with a learned latent world model is a promising route to control from raw pixels, but a strong world model alone is not enough. We show this experimentally: even with a perfect world model (operationalized by replacing the learned forward predictor with an idealized rollout of the true environment dynamics), a finite-budget sample-based planner still fails on some tasks, indicating that the bottleneck can lie in search rather than in world-model accuracy. Motivated by this gap, we propose IMWM (Intuition Model + World Model), which pairs the world model with an intuition model trained from demonstrations to recognize promising actions. The two models collaborate through three lightweight components: (i) Retrieval Initialization, which initializes the planner's action proposal from a retrieved demonstration; (ii) Hybrid Cost, which combines the intuition score with the world-model rollout cost; and (iii) a Reliability Gate, which adjusts how much the planner trusts intuition in each setting. Across four pixel-based goal-reaching tasks (Two-Room, Reacher, Push-T, and OGBench-Cube), IMWM has higher mean success than the world-model-only planner on all four, with the largest gains on Two-Room (99.2%, +11.5 percentage points) and OGBench-Cube (94.7%, +28.5 percentage points).

Related papers

• Self-Evolving World Models for LLM Agent Planning [72.99782619992361]
  We introduce WorldEvolver, a self-evolving world model framework that revises its deployment-time context while keeping the downstream agent and all model parameters frozen.<n>WorldEvolver achieves the highest prediction accuracy across three backbones and leads other world model baselines on downstream agent success rate.
  arXiv  Detail & Related papers  (2026-06-29T17:58:43Z)
• MotuBrain: An Advanced World Action Model for Robot Control [23.733029557644354]
  We present MotuBrain, a unified World Action Model that jointly models video and action under a UniDiffuser formulation.<n>A single model supports policy learning, world modeling, video generation, inverse dynamics, and joint video-action prediction.<n>Building on Motus, MotuBrain further introduces unified multiview modeling, an independent text stream for stronger language-action coupling.<n>Our inference stack combines step reduction, compilation, FP8 quantization, DiT caching, V2A-style action-only inference, and real-time chunked closed-loop execution.
  arXiv  Detail & Related papers  (2026-04-30T12:34:44Z)
• HY-Embodied-0.5: Embodied Foundation Models for Real-World Agents [71.09493646932046]
  We introduce HY-Embodied-0.5, a family of foundation models specifically designed for real-world embodied agents.<n>The HY-Embodied-0.5 suite comprises two primary variants: an efficient model with 2B activated parameters designed for edge deployment, and a powerful model with 32B activated parameters targeted for complex reasoning.<n>Our MoT-2B model outperforms similarly sized state-of-the-art models on 16 benchmarks, while the 32B variant achieves performance comparable to frontier models such as Gemini 3.0 Pro.
  arXiv  Detail & Related papers  (2026-04-08T17:59:48Z)
• World Action Verifier: Self-Improving World Models via Forward-Inverse Asymmetry [82.93104394404781]
  General-purpose world models promise scalable policy evaluation, optimization, and planning.<n>We propose World Action Verifier (WAV), a framework that enables world models to identify their own prediction errors and self-improve.
  arXiv  Detail & Related papers  (2026-04-02T12:48:36Z)
• WorldCompass: Reinforcement Learning for Long-Horizon World Models [81.03997753254023]
  This work presents World, a novel Reinforcement Learning (RL) framework for interactive video-based world models.<n>We introduce three core innovations tailored to the autoregressive video generation paradigm.<n>We show that World significantly improves interaction accuracy and visual fidelity across various scenarios.
  arXiv  Detail & Related papers  (2026-02-09T18:59:47Z)
• Co-Evolving Latent Action World Models [57.48921576959243]
  Adapting pre-trained video models into controllable world models via latent actions is a promising step towards creating generalist world models.<n>We propose CoLA-World, which for the first time successfully realizes this synergistic paradigm.<n>This unlocks a co-evolution cycle: the world model acts as a knowledgeable tutor, providing gradients to shape a high-quality LAM.
  arXiv  Detail & Related papers  (2025-10-30T12:28:40Z)
• From Forecasting to Planning: Policy World Model for Collaborative State-Action Prediction [57.56072009935036]
  We introduce a new driving paradigm named Policy World Model (PWM)<n>PWM integrates world modeling and trajectory planning within a unified architecture.<n>Our method matches or exceeds state-of-the-art approaches that rely on multi-view and multi-modal inputs.
  arXiv  Detail & Related papers  (2025-10-22T14:57:51Z)
• Bootstrapping World Models from Dynamics Models in Multimodal Foundation Models [37.774994737939394]
  We use dynamics models to bootstrap world models using synthetic data and inference time verification.<n>Our best model achieves a performance competitive with state-of-the-art image editing models, improving on them by a margin of $15%$ on real-world subsets according to GPT4o-as-judge.
  arXiv  Detail & Related papers  (2025-06-06T11:50:18Z)
• WorldPrediction: A Benchmark for High-level World Modeling and Long-horizon Procedural Planning [52.36434784963598]
  We introduce WorldPrediction, a video-based benchmark for evaluating world modeling and procedural planning capabilities of different AI models.<n>We show that current frontier models barely achieve 57% accuracy on WorldPrediction-WM and 38% on WorldPrediction-PP whereas humans are able to solve both tasks perfectly.
  arXiv  Detail & Related papers  (2025-06-04T18:22:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.