AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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M-Diffusion Planner is proposed with a dual-decoupling design: separating intent understanding from trajectory planning, and general planning capability learning from strategy alignment, to mitigate degradation of pretrained planning capabilities.
Existing approaches to multi-view spatial reasoning operate largely on sparse input views. Vision-language models (VLMs) are thus restricted to understand a scene and infer spatial relations within these fixed views, leading to fragile cross-view alignment and geometry-to-language bottleneck. To address these issues, w...
World models aim to learn representations of real-world environments and predict their future evolution. Recent object-centric world models have made expressive progress by representing visual scenes as sets of object-level latent states, but object-object relations are often captured only implicitly, which limits expl...
JRDB-AVR is introduced, a benchmark derived from existing real-world JRDB robotics data through a structured question-generation engine that turns this gap between answer accuracy and evidence accuracy in current baselines into an explicit evaluation.
When LLMs exhibit uneven performance across planning tasks, these gaps are often attributed to task difficulty. We argue that this explanation is incomplete, as task-level variation may reflect distinct latent planning competencies rather than differences along a single ability spectrum. We study this question on ACPBe...
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