Provides a 30K+ problem multimodal, multilingual dataset of Olympiad-level math problems with expert solutions and a math-aware retrieval benchmark—includes images, hierarchical topics, provenance from official booklets, and LLM-assisted metadata (v0, CC BY 4.0).
Curated monolingual Khasi sentence corpus (CSV) with under 1,000 sentences for language modeling, tokenization, and low-resource NLP experiments. Single-column structure (khasi_sentence) and CC BY‑NC 4.0 license — suitable for research and data-augmentation workflows, not for commercial use.
Self-hosted visual CMS that runs as a single Bun server, combining a canvas editor, content engine, media, auth, forms, plugins, and a publisher. Emits semantic HTML and compact CSS and includes a provider-agnostic AI agent that edits pages as real, editable nodes. Best for teams that want full control and simple deployments.
Provides aligned urban driving sensor streams (camera frames, LiDAR, radar and HD‑map / lanelet2 annotations) for multimodal perception, tracking and mapping research. Expert-generated labels under CC BY‑NC‑4.0 and hosted on Hugging Face.
Provides paired images and English captions for vision–language research, curated by Stanford Vision Lab and hosted on Hugging Face; useful for training and evaluating multimodal models and reproducing related research.
Provides tools and samples to build context management, enrichment, and retrieval solutions on Google Cloud Knowledge Catalog — an AI-oriented data catalog that builds a dynamic knowledge graph for structured and unstructured data, suitable for RAG and agent workflows.
Provides 10,000 articulated 3D objects in URDF for robotics and embodied-AI research. Generated by the Articraft agent and released under CC-BY-4.0, the dataset targets simulation, manipulation, kinematics evaluation, and training of embodied agents.
A 12B unified, encoder-free multimodal model that directly ingests text, images and audio and returns text; supports very long contexts (up to 256K tokens), native function-calling/thinking modes, and small-model deployment for local or on-device use.
A collection of 14,056 self-contained research-level mathematical problems extracted from papers and open-problem lists, each rewritten with taxonomy labels and open-status metadata for training or evaluating models on research-grade math reasoning.
Evaluates metric 3D spatial reasoning from single driving images via multiple-choice questions that require reconstructing scene geometry rather than relying on image-layout shortcuts. Each sample pairs a numbered-bbox image with a question, four choices, and the correct answer; images come from PlusAI and the dataset is CC BY 4.0.
Omnimodal world model that jointly processes and generates text, images, video, audio, and action trajectories for physical AI. Uses a mixture-of-transformers to combine autoregressive reasoning and diffusion-based multimodal generation; released open-source with checkpoints, datasets and benchmarks for robotics and simulation.
Evaluates multimodal LLMs on streaming egocentric video for spatial intelligence using 1,680 human-annotated questions across 348 videos; organizes tasks into four hierarchical levels (perception → tracking → simulation → allocentric mapping) and highlights allocentric mapping as the main bottleneck.