Run a world model locally, and let any LLM agent call it as a tool. V-JEPA 2 as an MCP server.
Wellknown found it in public sources; nobody has proven control of it yet. Claiming takes one click if the repository is under your GitHub account, or a small file on your domain otherwise. Verified owners get the badge, 15-minute checks, status alerts, edits that outrank crawled data, and a ranking boost.
Agents can do it too: POST https://wellknown.network/api/v1/claims with {"agent":"wm-mcp","method":"well_known_file"} — machine-readable steps at claim.json, guide at /docs/claim.
Everything here was measured by our prober or read from a registry. Nothing is self-reported.
Attributed to the source that supplied each field. Treated as claims, not facts.
# wm **Run a world model locally, and let any LLM agent call it as a tool.**  *Live and unedited: Claude calls the `surprise` tool; V-JEPA 2 watches the clip and reports where its predictions broke (3.0–4.9s, z≈2 — the test video's splice is at exactly 4.0s). Wait time hidden, output verbatim.* LLMs can *describe* what happens when a cup gets knocked off a table. A world model can *watch* it happen and tell you, frame by frame, where reality stopped matching its prediction. `wm` puts Meta's [V-JEPA 2](https://huggingface.co/facebook/vjepa2-vitl-fpc64-256) (MIT-licensed, 0.3B params) behind an [MCP](https://modelcontextprotocol.io) server, so Claude, or any MCP client, can consult a video world model the way it consults a database. ``` uvx wm-mcp serve ``` That's the whole install. First run downloads PyTorch and ~1.2GB of model weights — **minutes of downloading, not a hang.** ## The demo Ask your agent: > "Where does this clip stop being predictable? /path/to/video.mp4" The agent calls `surprise()`, the world model slides a window over the video, predicts each window's second half from its first, and reports the segment where its prediction failed hardest — z-scored per video, so scores mean something. ## The three tools | Tool | What it does | |---|---| | `embed_video(source, start_s?, end_s?)` | Video, video slice, or still image → latent sequence, stored server-side under an opaque handle. Images become static clips — useful as goals. | | `predict(handle, horizon_frames, target_handle?)` | Predict the trailing frames of a clip from its leading context (masked in-window prediction — horizon capped at clip length, this is not open-ended rollout). With `target_handle`, also returns cosine similarity between the prediction and a target embedding — "does this video end up looking like this photo?", computed server-side. | | `surprise(source, window,…
Mapped onto the structured taxonomy from declared text and observed tool names. Confidence shown for derived entries.
Every source is kept verbatim. Field changes are logged as events.