https://worldmodelatlas.com/pulse/ Selected public releases, research, products and demonstrations from tracked world-model teams. Each item keeps its source role and its evidence limit. Item dates are Atlas review dates, not the team's publication dates; the publication date and its precision appear in every description. This feed is not a complete news feed and not a capability ranking. en Sun, 16 Aug 2026 00:00:00 GMT https://worldmodelatlas.com/pulse/#nvidia-cosmos-3-droid-policies-2026 nvidia-cosmos-3-droid-policies-2026 Sun, 16 Aug 2026 00:00:00 GMT Tooling Official release NVIDIA / Cosmos 3 Policy — Tooling. Published 2026-08-04 (day precision); recorded by Atlas on 2026-08-16. NVIDIA documented 16-billion-parameter Nano and 4-billion-parameter Edge policy checkpoints post-trained for the DROID Franka setup. The Edge route is described as generating 32 actions per inference at 15 Hz on Jetson Thor, while the linked technical report reports RoboLab success of 36.8% from the omni checkpoint versus 28.1% from the base checkpoint under the same recipe, data and compute. Why it matters: Released policy weights and an edge deployment path make the claim that a general world model can become a robot controller more testable than a model-family announcement alone. Official release — Official NVIDIA technical-marketing post drawing on NVIDIA's own Cosmos 3 report and checkpoints. The RoboLab comparison is developer-run, covers a DROID/Franka policy route and is not independent hardware validation; the 15 Hz figure is a deployment throughput claim, not a task-success measure. Primary source: https://developer.nvidia.com/blog/beyond-vlas-how-world-action-models-reshape-robot-manipulation/ https://worldmodelatlas.com/pulse/#wayve-gaia-4-2026 wayve-gaia-4-2026 Sun, 16 Aug 2026 00:00:00 GMT Model release Official release Wayve / GAIA-4 — Model release. Published 2026-08-03 (day precision); recorded by Atlas on 2026-08-16. Wayve introduced GAIA-4 as the world model at the core of its Simulation 2.0 system. Starting from recorded sensor data, the simulator generates camera and radar inputs while the AI Driver's actions alter what it receives next; a world-on-rails mode holds other road users to their logged trajectories, with a separate reactive-agents mode for interaction. Why it matters: GAIA-4 moves the published GAIA line from controllable scene generation and offline evaluation toward policy-in-the-loop simulation, where the simulator is intended to measure the behaviour of an end-to-end driving system. Official release — Company research page with curated demonstrations. Wayve reports a 2.5x improvement in preserving the recorded world, but publishes no numeric result for the three fidelity levels it names: outcome, closed-loop and component fidelity. No fixed technical report or independent GAIA-4 evaluation was located; world-on-rails also deliberately prevents other agents from reacting to the ego vehicle. Primary source: https://wayve.ai/thinking/gaia-4/ https://worldmodelatlas.com/pulse/#world-labs-r2s2r-2026 world-labs-r2s2r-2026 Sun, 16 Aug 2026 00:00:00 GMT Research Official release World Labs / R2S2R — Research. Published 2026-07-28 (day precision); recorded by Atlas on 2026-08-16. Following its acquisition of SceniX, World Labs presented early R2S2R results spanning real-task reconstruction, simulation-only policy training and matched hardware evaluation. One policy-comparison figure uses 2,000 simulated trials and 100 real-world trials per checkpoint, while selected policies are shown operating on physical robots for one hour without intervention. Why it matters: The work extends World Labs from creating reusable spatial worlds toward a harder claim: generated simulations that train robot policies and preserve enough real-world behaviour to rank them before hardware testing. Official release — Developer-authored research post with selected demonstrations, not an independent evaluation or fixed technical report. The page does not publish raw success rates, correlation coefficients or uncertainty for the policy-ranking figure, and one-hour demonstrations on selected tasks do not establish general robot reliability. Primary source: https://www.worldlabs.ai/blog/real-to-sim-to-real https://worldmodelatlas.com/pulse/#deepmind-genie-3-wbench-2026 deepmind-genie-3-wbench-2026 Sun, 16 Aug 2026 00:00:00 GMT Benchmark Independent evidence Google DeepMind / Genie 3 — Benchmark. Published 2026-05-25 (day precision); recorded by Atlas on 2026-08-16. WBench evaluated Genie 3 alongside 19 other interactive video world models. On the six-model action-conditioned track, Genie 3 scored 73.3 for navigation, 82.6 for consistency and 65.7 for physics: fifth, second and first within that group, respectively. Why it matters: This is the first located third-party benchmark to replace selected Genie demonstrations with a shared test protocol, and it shows a mixed profile rather than across-the-board leadership. Independent evidence — Independent of Google but still an unrefereed arXiv v1 from the Meituan LongCat team and Fudan University. Genie 3 was tested only on the 158-case navigation subset through a mutable closed web service; semantic interactions were not tested, exact server versions cannot be pinned and the benchmark team also evaluates its own model. The 22 automatic submetrics were compared with human judgments, but no outside replication has been located. Primary source: https://arxiv.org/abs/2605.25874v1 https://worldmodelatlas.com/pulse/#runway-gwm-robot-policy-evaluation-2026 runway-gwm-robot-policy-evaluation-2026 Sun, 16 Aug 2026 00:00:00 GMT Benchmark Official release Runway / GWM-Robotics — Benchmark. Published 2026-02-27 (day precision); recorded by Atlas on 2026-08-16. Runway compared eight Franka Panda manipulation policies across 1,450 GWM-Robotics rollouts and real RoboArena outcomes, using more than 16,000 human ratings. It reports a Pearson correlation of 0.95 and a Mean Maximum Rank Violation of 0.033 between simulated and real-world policy rankings. Why it matters: The study turns GWM-Robotics from a proposed evaluation use case into a quantified developer test of whether simulated rollouts can help select policies before hardware deployment. Official release — Runway's own study, not independent validation. It evaluates relative policy ranking rather than absolute success-rate calibration, covers one Franka Panda tabletop setting, and does not expose the model for outside replication. The roughly ten graders per rollout are independent human raters within the protocol, not independent research organizations. Primary source: https://runway.com/research/accelerating-robot-policy-evaluation https://worldmodelatlas.com/pulse/#nvidia-cosmos-3-edge-2026 nvidia-cosmos-3-edge-2026 Mon, 27 Jul 2026 00:00:00 GMT Model release Official release NVIDIA / Cosmos 3 Edge — Model release. Published 2026-07-20 (day precision); recorded by Atlas on 2026-07-27. NVIDIA released Cosmos 3 Edge, a 4-billion-parameter omnimodel built to run on device, completing a stated Cosmos 3 size ladder of Edge 4B, Nano 16B and Super 64B. The same SIGGRAPH keynote introduced Cosmos-Dreams, described as a collection of closed-loop simulators. Why it matters: Moving a world model onto local hardware changes which deployment questions can be tested at all, but a smaller model on cheaper GPUs is a distribution change rather than evidence of better physical understanding. Official release — Official announcement. The stated No. 1 VANTAGE-Bench position applies to the model's parameter class and is NVIDIA's own reading; the Cosmos-Dreams autonomous-vehicle simulator was shown as a stage demonstration. Neither was independently evaluated, and no closed-loop deployment result is reported. Primary source: https://blogs.nvidia.com/blog/siggraph-news-2026/ https://worldmodelatlas.com/pulse/#nvidia-cosmos-3-2026 nvidia-cosmos-3-2026 Mon, 27 Jul 2026 00:00:00 GMT Model release Official release NVIDIA / Cosmos 3 — Model release. Published 2026-06-01 (day precision); recorded by Atlas on 2026-07-27. At GTC Taipei, NVIDIA launched Cosmos 3, described as a single open model combining vision reasoning, world generation and action prediction through a mixture-of-transformers architecture. NVIDIA also launched the Cosmos Coalition with founding members including Agile Robots, Black Forest Labs, Generalist, LTX, Runway and Skild AI. Why it matters: It merges three previously separate roles—understanding, simulation and action—into one released artifact, which makes the question of what any single benchmark score actually measures harder and more important. Official release — Official launch release. The leading positions cited across Artificial Analysis, Physics-IQ, PAI-Bench, R-Bench, RoboLab, RoboArena, VANTAGE-Bench and TAR are NVIDIA's stated readings of those leaderboards, each measuring a different thing; Atlas did not verify the leaderboards. The developer-authored Cosmos 3 technical report is held separately as source nvidia-cosmos-3. Primary source: https://investor.nvidia.com/news/press-release-details/2026/NVIDIA-Launches-Cosmos-3-the-Open-Frontier-Foundation-Model-for-Physical-AI/default.aspx https://worldmodelatlas.com/pulse/#deepmind-project-genie-street-view-2026 deepmind-project-genie-street-view-2026 Mon, 27 Jul 2026 00:00:00 GMT Product Official release Google DeepMind / Project Genie — Product. Published 2026-05-19 (day precision); recorded by Atlas on 2026-07-27. Google added a Street View grounding capability to Project Genie, its experimental Genie-based prototype, so a generated world can start from real imagery of a chosen place. Access expanded to eligible Google AI Ultra subscribers globally; Street View grounding launched for US places only. Why it matters: Anchoring a generated world to real imagery is the first visible attempt to tie interactive generation back to a specific real location, which is a different problem from generating a plausible-looking scene. Official release — Official product post. Google describes Project Genie as an experimental research prototype and points to current limitations; the post reports no fidelity, latency, persistence or geometric-accuracy measurement, and grounding on imagery is not a claim of physical correctness. Primary source: https://blog.google/innovation-and-ai/models-and-research/google-deepmind/project-genie-expands/ https://worldmodelatlas.com/pulse/#meta-interpreting-physics-2026 meta-interpreting-physics-2026 Mon, 27 Jul 2026 00:00:00 GMT Research Primary research Meta AI / Interpreting Physics in Video World Models — Research. Published 2026-02-04 (day precision); recorded by Atlas on 2026-07-27. Researchers probed where physical information lives inside large-scale video encoders and report a sharp intermediate-depth transition they call the Physics Emergence Zone. Speed and acceleration are reported as accessible from early layers while motion direction becomes accessible only at that transition, encoded as a high-dimensional circular population code. Their conclusion is that these models do not hold factorized, physics-engine-style state variables but a distributed representation that is still sufficient for physical prediction. Why it matters: Most world-model evidence is behavioural: the output looks right, or the benchmark score is high. This asks the different question of what is represented internally, which is where a claim about physical understanding would actually have to be settled. Primary research — Fixed arXiv v1, listed by Meta on 2026-07-03 with ICML as publisher. Atlas read the abstracts only, not the full paper, so no layer index, model list or figure locator is confirmed here; the abstracts do not name which models were studied. Finding a distributed rather than factorized representation is not the same as showing the representation is correct, complete or sufficient for control. Primary source: https://arxiv.org/abs/2602.07050v1 https://worldmodelatlas.com/pulse/#waymo-world-model-2026 waymo-world-model-2026 Mon, 27 Jul 2026 00:00:00 GMT Model release Official release Waymo / Waymo World Model — Model release. Published 2026-02 (month precision); recorded by Atlas on 2026-07-27. Waymo introduced the Waymo World Model, which it says is built on Google DeepMind's Genie 3 and post-trained for driving so that it emits both camera and lidar output. Waymo describes driving-action, scene-layout and language control, conversion of ordinary dashcam video into multi-sensor simulation, and an efficient variant for longer rollouts. Why it matters: It is the first well-documented public case of a general interactive world model being specialized into a safety-critical industrial simulator, which makes the transfer question concrete rather than hypothetical. Official release — Official blog post. Evidence is curated simulation video plus a lidar-alignment consistency demonstration chosen by Waymo. The post reports no quantitative benchmark, no error rate and no independent evaluation, and simulating a rare event is not evidence that the simulated frequency matches reality. Primary source: https://waymo.com/blog/2026/02/the-waymo-world-model-a-new-frontier-for-autonomous-driving-simulation/ https://worldmodelatlas.com/pulse/#runway-gwm-1-2025 runway-gwm-1-2025 Mon, 27 Jul 2026 00:00:00 GMT Model release Official release Runway / GWM-1 — Model release. Published 2025-12-11 (day precision); recorded by Atlas on 2026-07-27. Runway announced GWM-1, an autoregressive model built on top of Gen-4.5 that generates frame by frame in real time under action control including camera pose, robot commands and audio. It ships as three separate post-trained variants: GWM Worlds for explorable environments, GWM Avatars for conversational characters, and GWM Robotics for manipulation. Runway states that unifying them under a single base model is future work. Why it matters: A video-generation company reaching the same three application surfaces as the robotics and driving labs shows the routes are converging on shared infrastructure, while three separate post-trained models show that generality is still an aspiration rather than a delivered property. Official release — Official research announcement. Runway proposes GWM Robotics for evaluating robot policies in simulation rather than on hardware, but the page reports no benchmark, no success rate and no independent evaluation of whether simulated assessment matches real behaviour. Access is via an early-access request form, so the claims cannot currently be checked by outside testing. Primary source: https://runway.com/research/introducing-runway-gwm-1 https://worldmodelatlas.com/pulse/#wayve-gaia-3-2025 wayve-gaia-3-2025 Mon, 27 Jul 2026 00:00:00 GMT Research Primary research Wayve / GAIA-3 — Research. Published 2025-12-02 (day precision); recorded by Atlas on 2026-07-27. Wayve presented GAIA-3 as a 15-billion-parameter latent diffusion world model trained with roughly five times the compute and ten times the data of GAIA-2, across 9 countries. Its stated purpose is offline evaluation: re-driving recorded sequences with a changed ego trajectory while the rest of the scene stays fixed, generating NCAP-style collision scenarios, and transferring scenes between camera rigs. Why it matters: The stated goal moves from making driving video look real to using generated video to measure a driving policy, which is a much stronger claim and needs a much stronger kind of evidence. Primary research — Developer-authored research page. Wayve states that correlation studies between synthetic interventions and on-road experiments indicate the model can reliably predict relative policy performance, but publishes no numbers, protocol or independent report for that claim in this post. The page was modified on 2026-07-24, so wording may have changed since first publication. Primary source: https://wayve.ai/thinking/gaia-3/ https://worldmodelatlas.com/pulse/#waabi-copilot-4d-2024 waabi-copilot-4d-2024 Mon, 27 Jul 2026 00:00:00 GMT Research Primary research Waabi / Copilot4D — Research. Published 2024 (year precision); recorded by Atlas on 2026-07-27. Waabi's ICLR 2024 world model tokenizes lidar point clouds with a VQVAE and predicts future observations with discrete diffusion rather than generating camera video. It can be prompted with a candidate future action, and Waabi shows the model predicting that a vehicle behind brakes when the ego vehicle is prompted to brake hard. Waabi reports reducing prior state-of-the-art Chamfer distance by more than 65% at one second and more than 50% at three seconds across NuScenes, KITTI Odometry and Argoverse2. Why it matters: It is the clearest published counterexample to the assumption that a driving world model must generate video. Predicting in a sensor space that already encodes geometry is a different bet about what a model needs to represent. Primary research — Peer-reviewed at ICLR 2024, which is a stronger review path than the company blog posts elsewhere in this stream, but the authors include Waabi's founder and the reported numbers are their own. Atlas read Waabi's research page, not the paper, so no table or protocol locator is confirmed. Forecasting a point cloud accurately is not evidence about driving-policy quality, deployment safety or on-road behaviour. Primary source: https://waabi.ai/research/copilot-4d https://worldmodelatlas.com/pulse/#wayve-gaia-2-2025 wayve-gaia-2-2025 Fri, 10 Jul 2026 00:00:00 GMT Research Primary research Wayve / GAIA-2 — Research. Published 2025 (year precision); recorded by Atlas on 2026-07-10. Wayve reports a controllable, multi-view generative world model conditioned on driving actions, road semantics and environmental factors. Why it matters: It shows how a world model can be specialized for scenario generation and controlled rollouts in autonomous-driving research. Primary research — Developer-authored technical report; generated scenarios are not equivalent to independently calibrated policy-in-the-loop safety validation. Primary source: https://arxiv.org/abs/2503.20523 https://worldmodelatlas.com/pulse/#deepmind-genie-3-2025 deepmind-genie-3-2025 Thu, 09 Jul 2026 00:00:00 GMT Model release Official release Google DeepMind / Genie 3 — Model release. Published 2025 (year precision); recorded by Atlas on 2026-07-09. Google DeepMind describes Genie 3 as a general-purpose world model that generates dynamic environments from text and supports real-time navigation. Why it matters: The release makes interactive, action-responsive generated worlds a more visible research direction beyond fixed video output. Official release — Official capability claims; public access and independent long-horizon evaluation remain limited. Primary source: https://deepmind.google/blog/genie-3-a-new-frontier-for-world-models/ https://worldmodelatlas.com/pulse/#meta-vjepa-2-2025 meta-vjepa-2-2025 Thu, 09 Jul 2026 00:00:00 GMT Research Official release Meta AI / V-JEPA 2 — Research. Published 2025 (year precision); recorded by Atlas on 2026-07-09. Meta presents V-JEPA 2 as a predictive representation model trained from video, with evaluations for physical reasoning and a separate action-conditioned robot-control route. Why it matters: It represents a world-model strategy centered on latent prediction and planning rather than direct photorealistic generation. Official release — Official research release with developer-run benchmarks; task transfer and protocol limits remain important. Primary source: https://ai.meta.com/blog/v-jepa-2-world-model-benchmarks/ https://worldmodelatlas.com/pulse/#world-labs-marble-2025 world-labs-marble-2025 Thu, 09 Jul 2026 00:00:00 GMT Product Official release World Labs / Marble — Product. Published 2025 (year precision); recorded by Atlas on 2026-07-09. World Labs presents Marble as a multimodal system for creating, editing, expanding and exporting 3D worlds. Why it matters: It moves the product question from generating a clip toward creating spatial environments that can be explored, modified and reused in other workflows. Official release — Official product positioning and demonstrations; physical fidelity and downstream usefulness require separate evaluation. Primary source: https://www.worldlabs.ai/blog/marble-world-model https://worldmodelatlas.com/pulse/#openai-sora-world-simulators-2024 openai-sora-world-simulators-2024 Thu, 09 Jul 2026 00:00:00 GMT Research Official release OpenAI / Sora — Research. Published 2024 (year precision); recorded by Atlas on 2026-07-09. OpenAI's technical note frames large-scale video generation as a possible path toward general-purpose simulators of the physical world. Why it matters: It helped make the boundary between video generation and world modeling a central public research question. Official release — Official technical framing; it does not establish persistent action-conditioned interaction or planning utility. Primary source: https://openai.com/index/video-generation-models-as-world-simulators/