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GameHorizon Suite: Multi-Horizon Data and Evaluation in Gameplay

Provides a unified data and evaluation suite for measuring gameplay capabilities across short, medium, and long temporal horizons. Includes an automated annotator, a 5,000-hour AAA gameplay dataset with temporally aligned frames, actions and multi-horizon instructions, and a reproducible offline + stepwise online benchmark.

HappyWorld-Bench

Evaluates whether generative world models maintain consistent, controllable, and physically plausible simulated environments under exploration, interaction, and intervention. Introduces a six-level W1–W6 capability taxonomy across three tracks (video, spatial, embodied) with human A/B Arena and automated metrics to measure behavioral correctness.

GAE: Learning a Geometry-Native Latent Space for 3D-Consistent World Generation

Compresses a geometry-foundation model’s multi-level features into a compact latent that decodes jointly to RGB, depth, cameras and point maps — enabling a conditional flow to generate 3D-consistent video and novel views with measurably improved coherence.

Training Object Permanence in World Models

Provides WROP: a 1.5M-sample synthetic video corpus and a 300-question exam for training and evaluating object permanence and solidity in video world models. Includes 150 Blender task generators, a human Elo benchmark across 14 models, and a fine-tuned 16B continuation model (PWM-WROP).

Agent-Editing World Model: Rethinking World Modeling for LLM Agents

Edits an LLM agent's internal task state and action continuations to remove unsupported assumptions and noisy plans, improving downstream decisions instead of reconstructing high-entropy tool outputs. Combines Action Judge, State Revision, and EditAct and shows consistent gains across benchmarks and agent backbones.

The Past Frames the Future: Memory for Autoregressive Video Generation

Surveys memory mechanisms for autoregressive video generation, framing memory as persistent historical information that influences future generation. Organizes work by Forms, Functions, Operations, Learning, and Evaluation, and synthesizes challenges for long-horizon consistency and memory-aware learning.

Imagined Data

Provides imagined interaction segments generated by world models for RoboTwin2.0 tasks, stored as fixed-length HDF5 chunks (21 observation frames, 20 actions, rewards and episode flags). Useful for training and evaluating world-model-based policies; currently limited to the RoboTwin2.0 subdataset.

InternW0-$Δ$: A World Action Model Bridging Predictive Dynamics and Actions with 20K+ Hours of Open Data

Learns joint predictive visual dynamics and action generation for generalist robot manipulation, translating future-relevant visual representations into actions. Integrates a Mixture-of-Transformers coupling a video expert and action expert, a frozen vision-language model for semantics, 4D distillation, and Causal Imprint; pretrained on a 20K+ hour heterogeneous corpus.

FrameMorrow: Future-guided Frame Selection with Prospective Tokens for Long-Horizon Video Generation

Predicts compact 'prospective tokens' that summarize upcoming information needs and uses them to select a small set of past frames for conditioning long-horizon video generation, improving long-range consistency, visual quality, and action alignment while remaining plug-and-play across diverse generators.

From Traces to Agentic Worlds: Agentic Language World Models for Interactive Environment Simulation

Converts historical interaction traces into a reusable, queryable “worldbook” and runs a language-based world model agent (Trace2Env) as the environment for LLM agents — enabling stateful, grounded simulation with improved next-observation fidelity and long-horizon consistency.

Long-WAM: Scaling the Context of World-Action Models

Scales visual history for real-time robot control by combining autoregressive video pretraining with a streaming, asynchronous predict-then-act pipeline; shows longer context improves long-horizon manipulation and runs full inference in 107.4 ms per action chunk on an RTX 5090.

AgentGarten: Code Worlds for Evolving Agents

Creates programmable, real-time interactive code-based environments by separating deterministic simulator state from a shared neural video renderer so agents perceive, interact, and iteratively evolve via distilled playbooks; introduces Adversarial Forcing to distill a geometry-conditioned renderer for responsive visual feedback.