World Model Advances: Multiple Institutions Release New Results, Accelerating from Generation to Action

During and around WAIC 2026, multiple institutions intensively released world model-related achievements, covering foundation models, embodied intelligence, digital content, and industrial applications, marking the accelerated transition of world models from academic concepts to industrial deployment.

Kunlun: Full-Modal Model Matrix, Proclaiming "Year of the World Model"

Kunlun unveiled a full-modal model matrix spanning the physical world, foundation layer, and digital content layer at WAIC. CEO Fang Han declared 2026 as the "Year of the World Model."

Riemann-1.0: An embodied action model adopting the "world action model" approach, unifying robot action generation and environment prediction into a single framework. It achieves a 99.0% success rate on the LIBERO benchmark, 62.6% on RoboCasa-365, and 94.3% on RoboTwin2.0. After pretraining with first-person human data, long-horizon seen-scene success rates improve by 28.15%, and unseen scenes by 16.97%.
Matrix-Game 3.5: A real-time interactive world model supporting continuous interaction and long-term spatial memory, capable of generating structurally self-consistent open worlds.
Mureka V9.5 and O3: Applying cognitive and reasoning capabilities to music creation.

Jijia Vision: General World Model Product Matrix, from Content Creation to Embodied Intelligence

Jijia Vision showcased a four-in-one system at WAIC: "World Model - Embodied Foundation Model - Native Embodiment - Generalization Scenarios":

World Generation Models: YiSu (content creation), DriveDreamer (autonomous driving simulation), GigaWorld (embodied intelligence training and validation).
World Action Models: GigaBrain (general embodied brain), GigaWorld-Policy (direct action output).
Embodiment Products: Shiguang S1 (home scenarios), Maker H01 (smart manufacturing), demonstrating long-horizon real-world operations.

Yann LeCun's Team: AdaJEPA — Test-Time Adaptive World Model

Yann LeCun's team proposed AdaJEPA, achieving the first test-time adaptation of a JEPA world model within an MPC planning loop. Core innovations:

Self-supervised fine-tuning using state transitions from the agent's own interactions, requiring no external labels.
Only a single gradient update per re-planning, fine-tuning only a few top layers of the encoder and predictor, adding 0.01–0.03 seconds of latency per step.
On PushT and PointMaze benchmarks, out-of-distribution task planning success rates improve significantly, with the largest gains when training data is scarce.

LingBot World 2.0: Open-Source Infinite Interactive World Model

LingBot-World-Infinity (14B main model + 1.3B distilled model) achieves four innovations:

MoBA Hybrid Attention + Flow Matching Pretraining: Continuously generates stable output for 1 hour without quality degradation.
Consistency Distillation + Distribution Matching Distillation: Supports real-time interaction at 720P/60fps.
Multi-Granularity Hierarchical Data Engine: Fuses real footage, game synthesis, and web videos with segmented temporal annotations.
Director-Navigator Dual-Agent Scheduling: Supports diverse interactions (melee, spellcasting, weather switching) and multiplayer online play.

DAMO Academy: RynnWorld-Teleop and RynnWorld-4D

Alibaba DAMO Academy released two works:

RynnWorld-Teleop: A digital teleoperation solution replacing real robots with generative world models. Operator gestures drive real-time video generation, automatically obtaining joint-level action labels. Real-robot experiments achieve zero-shot Sim2Real transfer; synthetic data combined with real data stably improves success rates. Code and models are open-sourced.
RynnWorld-4D: A 4D embodied world model using RGB+Depth+Optical Flow (RGB-DF) representation, simultaneously generating future RGB video, depth maps, and optical flow. It constructs the Rynn4DDataset 1.0 with 254.4 million frames. The accompanying policy head, RynnWorld-4D-Policy, achieves 9Hz equivalent closed-loop control, achieving SOTA on bimanual dexterous manipulation tasks.

Zhongshu Ruizhi: Causal World Model Deployed in 35 State-Owned Enterprises

Zhongshu Ruizhi released the "AI for Reasoning" causal intelligence system and the "Causal World Model Technical System Blue Book." Core features:

Based on the "meta-causality" concept, dynamically constructing causal graphs supporting intervention and counterfactual reasoning.
In oil and gas drilling well control scenarios, it advances warning windows by approximately 15–20 minutes, achieves root cause localization accuracy of ~94%, and filters over 40% of invalid false alarms.
Deployed in over 35 large state-owned enterprises, covering more than 800 scenarios, with cumulative safe operation exceeding 15,000 hours.