#Model releases

Why it matters: The original article lists DM0.5's specific scores on multiple leaderboards and its inference latency reduction data, which can be used to assess the practical effectiveness of its pretraining approach.

Why it matters: The original breaks the upgrade into three specific changes: contextual memory, reasoning approach, and world model. Readers can use these to understand how the shift from spatial understanding to spatiotemporal understanding is realized.

Why it matters: The source gives three quantitative metrics and the architecture design, which readers can use to assess how feasible the world model is as a policy testbed.

Why it matters: The original article provides comparison figures for ICL and VLA at the same data scale and a deployment timeline, allowing readers to assess the feasibility of the in-context learning approach for manipulation tasks.

Why it matters: The article presents a zero-code-change adaptation path and a quantization compression scheme, which readers can use to assess how existing domestic compute can support ultra-large models.

Why it matters: The original text provides data scales spanning four orders of magnitude and three scaling law conclusions, which can help readers understand how human video pretraining transfers to robot data.

Why it matters: The original article provides latency and success-rate comparisons with traditional CEM search, which readers can use to judge the benefit boundaries of search-free world model control.

Why it matters: The model brings touch into large-scale pretraining along with a self-developed pseudo-labeling system, letting readers understand the path by which tactile data goes from a lab signal to a scalable pretraining resource.

Why it matters: The tweet provides comparison numbers with other VLA approaches on the same benchmark, letting readers judge how much headroom a plug-in structure that does not change weights offers.

Why it matters: The official disclosure provides detailed quantitative data on pretraining scale and generalization gaps, allowing readers to understand how a small amount of demonstration data transfers to unseen environments.

Why it matters: The original text explains the architectural composition of V³-0 and its human-in-the-loop real-robot reinforcement learning loop, which lets readers understand where the mobile whole-body manipulation capability comes from.

Why it matters: The original text presents a complete chain from human-hand data processing and real-robot correction to model architecture and experimental figures, allowing readers to assess its reproducibility value.

Why it matters: It provides a same-benchmark success-rate comparison with π0.5 and single-GPU real-time inference metrics, helping to gauge where this kind of world-action model stands in terms of performance.

Why it matters: The material provides comparison scores on RBench and the scope of the open-source release, helping readers judge the starting point and usability of this embodied video model.

Why it matters: The public weights and GM-100 results let readers directly compare its gap with π0.5 and GR00T N1.7 on long-horizon mobile manipulation.

Why it matters: The original text explains the training paradigm shift from predicting the next output to predicting the next state, as well as the approach of a frozen backbone plus lightweight readout modules, which is transferable to readers following the world model route.

Why it matters: The original text provides benchmark rankings, the change in depth error on the hardest indoor scenes, and an open-weights link, helping readers judge the improvement over the previous generation and how reusable it is.