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OpenWAM: An Open, Modular Exploration Towards Systematic World-Action Model Pretraining
OpenWAM releases an open modular stack for world-action model pretraining, plus OpenWAM-alpha trained on about 6,400 hours of egocentric and robot data.
OpenWAM is an open research stack that factorizes World-Action Model pretraining into composable infrastructure, study, and model components with unified training, inference, and evaluation. Controlled experiments distill three principles on knowledge inheritance, world-action synergy, and out-of-domain generalization gains from embodied co-training. The resulting OpenWAM-alpha, pretrained on roughly 6,400 hours of egocentric human and robot data, achieves top-tier results across eight simulation benchmarks and real-robot tests spanning single-arm, bimanual, and dexterous embodiments. The full stack, including infrastructure, evaluation protocols, pretrained models, and data recipes, is released openly.
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The /wayfinder Skill: Navigating the “Fog of War” of Planning
Matt Pocock released the /wayfinder skill, an orchestrator layer that manages planning sessions, maps, and tickets for AFK coding agents.
Latent Space interviews Matt Pocock, whose AI Skills for Real Engineers project has 220,000+ GitHub stars, about his new /wayfinder skill. The skill manages agent context during ambiguous planning by splitting work into grilling, prototype, research, and task tickets organized under a shared map, enabling overnight AFK agent runs. It uses deliberate terminology like map, ticket, and session to steer agent behavior, and was tested on projects including a personal website rearchitecture.
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DeCAL: Towards Physically-Grounded Dexterous Vision-Language-Action Models via Contact-Aware Latent Co-Imagination
DeCAL, a contact-aware dexterous vision-language-action model with visuo-tactile fusion, reports 71% average task success.
DeCAL is a physically-grounded dexterous vision-language-action (VLA) model built on a Mixture-of-Transformers architecture with specialized experts for understanding, imagination, and action generation. It introduces Adaptive Visuo-Tactile Fusion with contact-aware gating and Visuo-Tactile Latent Co-Imagination to jointly model visual and tactile dynamics. It reports state-of-the-art results with a 71% average success rate and 83.4% progress success rate, plus generalization to unseen scenarios.
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