mlsanityData to 14 Oct 2026

Conformal Orbit-Valid Trust Horizons for Equivariant World Models

arXiv 2606.24946 ↗First posted 23 Jun 2026cs.LG cs.ROcited by 1

On the map: World models → Latent predictive world models

Abstract (arXiv)

Learned world models are useful only over horizons on which their rollout error remains controlled. We study trust-horizon certification for latent world models with known group symmetries. Given a one-step latent residual and a finite-time expansion estimate, we form a raw horizon curve and calibrate it with a split-conformal multiplicative factor. On the reproducible audit set, the conformal factor is γα=1.0: the raw certificate is already conservative under the audit protocol. Across 50 stable audits, we observe zero anti-conservative violations, corresponding to an exact-binomial 95% upper bound of 5.8% on the violation rate. Our main structural result is that exact equivariance transports a calibrated trust-horizon curve over the group orbit: when the environment dynamics, encoder, predictor, action transform, and latent metric satisfy the stated equivariance/invariance conditions, rollout errors and trust horizons are orbit-constant. Empirically, the implemented models exhibit small orbit-transport residuals, with median 1.1% and maximum 4.1% over 14 orbit audits. The certificate is also non-vacuous (median certified-to-measured horizon ratio 0.67). A certificate-level calibration-cost study shows two complementary regimes. On a symmetric 2D substrate, equivariant, plain, and augmented models are all orbit-valid from a single calibration sector -- no separation, because the substrate already makes non-equivariant baselines approximately orbit-robust. A 3D yaw audit shows the other regime: the equivariant model obtains a one-sector safe and non-vacuous orbit-valid certificate, while healthy non-equivariant baselines pay violation, slack, sharpness, or additional-sector cost. The certificate is a conservative, distributional audit rather than a global reachability guarantee, and certificate-guided subgoal spacing is not confirmed in the current 3D CEM-MPC behavior layer.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Latent predictive world models, one of 11 directions in World models. Builds on 18 of its 24 references that are in the map. First posted on arXiv 23 Jun 2026.

Builds on

18 of 24 references.

Closest ancestors · 2

Same direction, newest first

  1. 2026FF-JEPA: Long-Horizon Planning in World Models with Latent Planners8 Jun
  2. 2026Hierarchical Planning with Latent World Models3 Apr

Other references · 16

  1. 2023Mastering Diverse Domains through World Modelscited by 1,253
  2. 2016Group Equivariant Convolutional Networkscited by 1,165
  3. 2021E(n) Equivariant Graph Neural Networkscited by 965
  4. 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 792
  5. 2019Conformalized Quantile Regressioncited by 629
  6. 2021Adaptive Conformal Inference Under Distribution Shiftcited by 375
  7. 2019Control Barrier Functions: Theory and Applicationscited by 372
  8. 2021Vector Neurons: A General Framework for SO(3)-Equivariant Networkscited by 305
  9. 2017Hamilton-Jacobi Reachability: A Brief Overview and Recent Advancescited by 201
  10. 2022e3nn: Euclidean Neural Networkscited by 174
  11. 2021Provably Strict Generalisation Benefit for Equivariant Modelscited by 91
  12. 2022SO(2)-Equivariant Reinforcement Learningcited by 84
4 more
  1. 2024EquiBot: SIM(3)-Equivariant Diffusion Policy for Generalizable and Data Efficient Learningcited by 75
  2. 2024Does equivariance matter at scale?cited by 52
  3. 2024Equivariant Reinforcement Learning under Partial Observabilitycited by 18
  4. 2026Symmetry, Defects, and Diffusion in Continuous-memory Recurrent Networkscited by 2

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Citing papers per year: 2026: 1.

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  1. 2026Certified World Models: Predictability Across Configuration, Horizon, and Resolution
  2. 2026When Do Conservation Laws Survive Learned Representations? Certified Horizons for Latent World Models
  3. 2026World Models in Pieces: Structural Certification for General Agents
  4. 2026World Models with Predictable Long-Horizon Marginals
  5. 2026Dual-Frontier: When Can an Agent Trust Its World Model?
  6. 2026Certified World Models as Sensing Clocks: Drift-Aware Deadlines for Active Perception
  7. 2026Exact equivariance, kept through training, buys zero-shot generalisation across the symmetry group
  8. 2026Low-Rank Dynamics-Effective Latent Carriers for Counterfactual Rollout in Learned World Models