Infrared and visible image fusion integrates complementary information from both modalities to provide a more comprehensive representation of the scene. We propose ProtoFusion, a protocol-guided generative framework for infrared and visible image fusion. Instead of relying on fixed fusion operators, hand-crafted objectives, or manually specified one-shot prompts, ProtoFusion formulates fusion as protocol-constrained generation. It represents fusion intent with a structured protocol, consisting of environment state cards, grounding principles, and a style profile, which explicitly specifies which evidence should be preserved or suppressed and which properties the fused result should satisfy. To optimize this protocol, we introduce protocol-guided prompt refinement, a closed-loop learning framework that iteratively performs generation, evaluation, and revision under multimodal evidence. Once learned, the optimized protocol is fixed at inference time and directly guides fusion generation. To address residual source inconsistencies, we further incorporate structured belief construction, which organizes modality reliability, scene evidence, and cross-modal conflicts into a structured representation for conservative correction after generation. Extensive experiments on standard benchmarks show that ProtoFusion improves quantitative performance, achieves favorable results in aesthetic evaluation, and produces visually coherent fusion outputs. Project Page: {https://erfect2020.github.io/ProtoFusion/}.
Probing Spatial State Representations in Visual Foundation Models
Baiqiao Yin, Zhongang Cai, Ruisi Wang, Qiwei Liang, Lei Yang, Ziwei Liu
AI 速览：该研究通过四个层级的冻结特征探针，检验视觉基础模型在流式自我中心观测下是否具备可维持、可预测更新并可用于目标导向动作的空间状态表征。
An observer sees only part of the surrounding world, yet must keep track of what lies beyond the current view. Spatial perception therefore involves more than recognizing individual images: it calls for a persistent spatial state that is updated under self-motion and used to guide action. Do visual foundation models provide representations that support such a state? We study this question through four levels of spatial capability---spatial structure, object persistence, predictive spatial update, and actionability---using frozen-feature probes on ten representations under streaming egocentric observations of static indoor environments. The resulting capability profiles vary across tasks, network layers, and prefix lengths: strong readout of the visible scene does not ensure accurate object persistence or future-feature retrieval. Matched controls separate readout accuracy from reliance on visual history and motion. Native future prediction retains recoverable spatial structure, but a spatial reader trained on real features does not transfer to predicted features unchanged. Finally, goal-dependent candidate selection does not establish reliable continuous action utility: predicted actions respond to goal changes, yet supplying the correct goal does not reliably improve progress. Across the four levels, object persistence and actionability build on camera-pose readout, whereas feature prediction builds on visible-scene readout. Together, these findings motivate evaluating spatial representations not only by what can be decoded from them, but by whether that information is maintained beyond observation, updated predictively, and used for goal-directed action.
Predicting future physical states stands as a central challenge in embodied intelligence. Existing video world models leave 3D dynamics implicit, action-based formulations remain tied to specific robot configurations, and 3D motion prediction models use flow-only supervision, providing a limited learning signal for physical dynamics. To circumvent these limitations, we introduce \method{}, a generative world model that jointly predicts future video and query-based 3D trajectories. By parameterizing 3D motion as continuous displacement tokens aligned with the video grid, a unified Mixture-of-Transformers (MoT) backbone jointly denoises both modalities under a rectified-flow objective. This formulation couples explicit geometric dynamics with visual evolution, effectively unlocking the physical commonsense embedded within pretrained video models. To empirically scale and validate this unified architecture, we train \method{} on a heterogeneous mixture of 1.44 million object-level samples spanning simulated and real-world domains. We further introduce WorldFlowBench, comprising 1,678 evaluation samples from 11 data sources across simulated and real-world interactions, including spatial reasoning tasks. Across all three evaluation sets, WorldFlow achieves the lowest average displacement error (ADE) and final displacement error (FDE) among the evaluated learning-based methods. Notably, on the challenging SpatialCog set, WorldFlow achieves an ADE of 0.060 m, a 54.2\% reduction compared with the strongest external baseline. It also reduces ADE and FDE by 55.9\% and 68.5\%, respectively, compared with its flow-only variant. Project page: https://worldflow-rbs.github.io/.