foundation model

Amap, part of Alibaba Group, has released λ0, which it describes as the first whole-body foundation model based on open-world human behavior. According to Amap, it learns movement, posture and two-handed manipulation from human data alone, cuts collection time per demonstration from about 100 seconds to 29, and raises real-robot task success 22.5% over the strongest baseline.

NVIDIA released Kumo Relational, a pre-trained foundation model that predicts outcomes such as churn, demand, and fraud directly on relational databases in a single forward pass. NVIDIA claims it beats supervised relational deep learning and expert-engineered gradient-boosted trees across 41 tasks on 15 databases, and surpasses AutoGluon ensembles by 8% MRR on SAP SALT.

Mistral AI has released Timestral-209M, the largest of four time series foundation models (5M, 28M, 123M, 209M parameters). According to the model card, it leads GIFT-Eval, TIME and fev-bench in zero-shot forecasting, with a context of up to 32,768 time steps and 21-quantile probabilistic output.

Google Research has published PyTorch weights for TimesFM 3.0, a 0.3-billion-parameter foundation model built for time-series forecasting. The model uses a Stacked Mixing Transformer architecture and is released under a non-commercial license.

OpenRouter has released Owl Alpha, a foundation model specifically designed for agentic workloads with native tool use support and a 1,048,756 token context window. The model is currently free for both input and output tokens and is compatible with Claude Code, OpenClaw, and other productivity tools.