InfluenceField: A Differentiable Field with Interventionally Identifiable Causal Structure for Multimodal World Modeling

• URL: http://arxiv.org/abs/2609.07874v1
• Date: Mon, 07 Sep 2026 18:36:20 GMT
• Title: InfluenceField: A Differentiable Field with Interventionally Identifiable Causal Structure for Multimodal World Modeling
• Abstract summary: InfluenceField is an intervention-aware latent field inserted between the visual encoder and language decoder.<n>It lifts patch features into a continuous spatial representation, propagates directed influence over multiple steps, and predicts local intervention effects.<n>On CausalVQA, InfluenceField improves overall accuracy over its backbone by 13.1 percentage points.
• Score: 8.383772213181853
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal large language models often capture visual-linguistic correlations but struggle to predict how local visual interventions propagate and affect downstream answers. We introduce InfluenceField, an intervention-aware latent field inserted between the visual encoder and language decoder. It lifts patch features into a continuous spatial representation, propagates directed influence over multiple steps, and predicts local intervention effects through a shared transition operator. Training jointly optimizes language modeling, cross-environment invariance, counterfactual rollout supervision, and structural regularization. For a nonlinear finite-basis population model, we show that target-aligned interventional supervision, together with a one-step separation condition on the transition, restricts admissible representations to within-location reparameterizations, so that the directed dependency graph of the full transition is recovered exactly. A linear specialization gives an exact partial-coverage characterization and a finite-loss stability bound, and the field analysis derives the spatial profile of coefficient interventions together with a shared-channel calibration result. On CausalVQA, InfluenceField improves overall accuracy over its backbone by 13.1 percentage points, with the largest gains on the planning and hypothetical categories. Capacity-matched baselines and structural controls attribute the gains in robustness and factual-counterfactual consistency to the causal objectives rather than to added capacity.

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