Qantara: Bridge-Flow Training for Multi-Paradigm JEPA Control

2026-07-06

AI Summary

Existing JEPA-based world models are constrained by a single inference paradigm, limiting their flexibility at deployment. This work proposes Qantara, the first single JEPA model capable of unifying latent-space planning, behavioral cloning, and inverse dynamics inference without retraining. Central to Qantara is a bridging flow training framework that jointly optimizes Brownian bridge interpolation along the state axis and noise-to-data flow matching along the action axis, augmented with a video inverse synthesis query mechanism that focuses on squared-noise boundary regions to enhance training efficiency. Experiments demonstrate that Qantara achieves an average success rate of 91.2% on the LeWM control suite, sets a new state of the art on OGBench-Cube with a 7.7% absolute improvement in success rate, and simultaneously attains 82–83% behavioral cloning accuracy and 71–73% video inverse trajectory success on Push-T and Cube tasks.

Abstract

Joint-Embedding Predictive Architectures (JEPAs) underpin a growing family of latent world models for control from raw pixels, but every existing JEPA world model commits at training time to a single inference paradigm: either trajectory optimisation in a learned dynamics model, or direct behaviour cloning. A single checkpoint that serves both would defer this choice to inference, when deployment constraints (rollout cost, observation accessibility) determine which path wins. We present Qantara, an end-to-end JEPA whose joint training objective pairs a Brownian-bridge interpolant between consecutive clean latents on the state axis with noise-to-data flow matching on the action axis. The same checkpoint serves three inference paradigms without retraining: latent planning, behaviour-cloning action sampling, and inverse dynamics, which we query through a video-inverse composition that first predicts the next latent without action conditioning, then extracts the action. Training concentrates mass on the edges of the (action-time, state-time) noise square, where inference queries the predictor: replacing it with uniform interior sampling drops Push-T planning from 90.1 to 53.3 SR at matched compute. On the LeWM control suite, Qantara reaches a 91.2 SR three-train-seed average and sets new SOTA on OGBench-Cube (+7.7 SR over DINO-WM, +19.7 over LeWM). From the same weights, the behaviour-cloning and video-inverse paths reach 82-83 SR on Push-T and 71-73 SR on Cube. These results move JEPA world models from single-paradigm planners to multi-paradigm controllers.

Problem

• JEPA
• multi-paradigm control
• world models
• inference paradigm
• latent planning

Innovation

• JEPA
• multi-paradigm control
• Brownian bridge
• flow matching
• latent world model