Eximius Labs

Eximius Labs builds open-weight multimodal models that run on your own hardware: the memory and perception layer for physical AI. The technical report is on arXiv: https://arxiv.org/abs/2607.18666.

We freeze a strong base model and add new senses from the outside, so existing embeddings stay valid and gaining a modality never forces a re-index. One shared vector space spans text, image, video, audio, thermal, and motion (IMU).

Fusion Embedding, the memory layer

One vector space for cross-modal retrieval, RAG, clustering, and semantic search.

• fusion-embedding-2 — text, image, video, and audio in one space (connector plus modality-gated adapters).
• fusion-embedding-1 — the connector-only first generation.
• Ember — thermal and infrared, as a loadable sense pack.
• Tremor — body-worn and robot motion (IMU and accelerometer), read as language, with a Unitree-G1 head.
• fusion-embedding-2-k3-vision — Kimi K3's vision encoder, projected into the shared space.

Fusion Perception, the perception layer

Dense scene understanding and geometric place recognition on a frozen vision backbone, projected into the Fusion Embedding space. Code: https://github.com/Eximius-Labs/fusion-perception.

Build with it

• Cookbook: https://github.com/Eximius-Labs/cookbook
• Code: https://github.com/Eximius-Labs
• Paper: https://arxiv.org/abs/2607.18666

Everything is open weight and self-hostable. Core models are research preview; the sensor packs are CC-BY-NC-4.0.