Contrastive Bisimulation World Models

Scaling Abstract Representations Across Domains and Modalities

AI Track Research
0.931
CBWM Abstraction Ratio (Pendulum)
1.891
Reconstruction Ratio (Pendulum)
1.810x
Best Transfer Improvement
6.211
Avg. CBWM Effective Rank

Abstraction Ratio Comparison

Lower ratio = better abstraction (irrelevant sensitivity / relevant sensitivity)

Sensitivity Analysis

Relevant vs. irrelevant sensitivity per method

Detailed Abstraction Results

DomainMethodRel. Sens.Irr. Sens.Ratio
Linear DynamicsCBWM (Ours)2.5544.3701.711
Reconstruction2.5242.8291.121
Forward-Only0.6710.5110.762
Nonlinear PendulumCBWM (Ours)2.8642.6680.931
Reconstruction2.6965.0981.891
Forward-Only0.3860.2740.710
Grid NavigationCBWM (Ours)4.6874.6270.987
Reconstruction1.6042.9541.841
Forward-Only0.5550.4050.730

Multi-Step Forward Prediction Error

Latent L2 error over prediction horizon

Cross-Domain Transfer

Adaptation curves with frozen encoder (20 gradient steps)

Transfer Results

Transfer PairInitial ErrorFinal ErrorImprovement
Linear Dyn. → Pendulum4.7012.5971.810x
Linear Dyn. → Grid4.8192.7941.725x
Pendulum → Grid3.6032.0511.757x

Latent Dimensionality Scaling

Abstraction ratio and prediction error vs. latent dim

Effective Rank

Exponential entropy of normalized singular values

Effective Rank Summary

MethodLinear DynamicsNonlinear PendulumGrid Navigation
CBWM (Ours)6.2486.2996.085
Reconstruction7.7407.7517.746
Forward-Only7.4787.2176.797

Latent Dimensionality Scaling (Linear Dynamics Domain)

DimAbstraction RatioRel. SensitivityIrr. SensitivityAvg Fwd Error
25.9700.5233.1251.395
42.1881.3382.9292.109
82.0261.9613.9732.670
161.4292.2233.1763.177
320.3662.5210.9233.034