Latent Spatial Memory for Video World Models research paper by Microsoft, 2026

Microsoft · Multimodal and robotics · Jun 8, 2026 · 24 citations · 71 upvotes · unverified

Latent spatial memory for video world models stores 3D scene information directly in diffusion latent space, eliminating pixel-space reconstruction overhead and achieving faster generation with reduced memory usage.

By Weijie Wang, Haoyu Zhao, Yifan Yang and 7 more · arXiv 2606.09828 · PDF · Code

UnverifiedHugging Face's summary; not yet checked by hand.

More from Microsoft

All 57

Agents and evaluationUnverifiedNov 2, 2026WebHarbor: Docking Real Websites for Evolving GUI Agent EnvironmentsWebHarbor provides realistic, containerized website environments for training and testing computer-using web agents across complex online tasks.

Microsoft28 upvotesRead →Agents and evaluationUnverifiedOct 19, 2026Agent World Model in the Wild: Computer-Use Agent Environment SynthesisThis pipeline creates controllable, open-source environments to safely train and test AI agents on complex, long-horizon computer-use tasks.

Microsoft69 upvotesRead →Training and scalingUnverifiedOct 16, 2026Self-Evolving Coding Agents via Harness-Policy OptimizationThis framework improves coding agents by recursively alternating between optimizing the agent's policy and the environment harness it uses to interact with code.

Microsoft48 upvotesRead →Multimodal and roboticsUnverifiedOct 13, 2026AesCode: Aesthetic Code Generation with Decoupled Cross-Modal RewardsThis paper introduces a method to improve code-based visual generation by using image-based aesthetic guidance and decoupled rewards to ensure quality and correctness.

Microsoft137 upvotesRead →Training and scalingUnverifiedOct 13, 2026APT: Feedback-Driven Agentic Post-Training for Text-to-Image GenerationThis paper introduces an automated post-training framework that uses a feedback loop of evaluation and diagnosis to improve text-to-image models.

Microsoft23 upvotesRead →Multimodal and roboticsUnverifiedOct 13, 2026LensErase: A Foundation Model for Erase AnythingThis paper introduces a foundation model designed for versatile image editing and object removal using mask-based inputs and efficient positional encoding.

Microsoft52 upvotesRead →Inference and efficiencyUnverifiedOct 13, 2026Simplifying and Improving On-Policy Distillation for Few-Step DiffusionThis paper introduces a simplified regression-based method for distilling diffusion models that improves generation speed without requiring complex training techniques.

Microsoft27 upvotesRead →Applied AIUnverifiedOct 5, 2026MiniCorp: The Last Mile of the AI Agent FirmMiniCorp is an office simulator that allows researchers to study how agents run companies and generate enterprise data for training.

Microsoft39 upvotesRead →

Topic

Multimodal and roboticsApr 5, 2023Segment AnythingA promptable model and a dataset of over a billion masks that cut out any object in any image.

Meta16.8k citations6 upvotesRead →Multimodal and roboticsJan 30, 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language ModelsBLIP-2 efficiently pre-trains vision-language models using off-the-shelf frozen encoders and decoders, achieving state-of-the-art performance with fewer parameters.

Salesforce9,805 citations3 upvotesRead →Multimodal and roboticsJan 28, 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and GenerationBLIP, a Vision-Language Pre-training framework, improves performance across both understanding and generation tasks by bootstrapping captions from noisy web data, achieving state-of-the-art results on image-text retrieval, image captioning, and VQA, and showing strong generalization to video-language tasks.

Salesforce7,655 citations5 upvotesRead →Multimodal and roboticsUnverifiedOct 25, 2024GPT-4o System CardGPT-4o is an omnimodal autoregressive model trained to handle text, audio, image, and video inputs, offering high-performance outputs across these modalities, with particular strengths in vision and audio.

OpenAI5,388 citations88 upvotesRead →Multimodal and roboticsUnverifiedNov 20, 2025SAM 3: Segment Anything with ConceptsSegment Anything Model 3 achieves state-of-the-art performance in promptable concept segmentation and tracking by leveraging a unified model architecture with decoupled recognition and localization.

Meta1,466 citations138 upvotesRead →Multimodal and roboticsUnverifiedMar 8, 2024DeepSeek-VL: Towards Real-World Vision-Language UnderstandingDeepSeek-VL is an open-source vision-language model that achieves state-of-the-art performance in real-world applications by combining a hybrid vision encoder with effective pretraining strategies to preserve language model capabilities.

DeepSeek939 citations49 upvotesRead →

About this paper

Venue: arXiv.org
Citations: 24, 3 influential · Semantic Scholar
Upvotes: 71 · Hugging Face
Lab: Microsoft · on Companies · on Acquisitions · on Quarterly · on Paydays · on Releases · on TechConf

Changes

CitationsNov 9, 2026Citations rose from 22 to 24.

CitationsNov 2, 2026Influential citations rose from 1 to 3.

CitationsNov 2, 2026Citations rose from 8 to 22.

CitationsOct 5, 2026Influential citations rose from 0 to 1.

CitationsOct 5, 2026Citations rose from 5 to 8.

CitationsSep 25, 2026A first count of 0 influential citations.

CitationsSep 25, 2026A first count of 5 citations.

New paperSep 25, 2026Found by the weekly Hugging Face scan; not yet checked by hand.

What it shows

More from Microsoft

Other multimodal and robotics papers

Changes