We are training a large language model on one input class only: raw video of human crowds moving through public space. No web crawl, no licensed text, no transfer from an existing model. The goal is a population behavior model. It also produces a ground truth motion corpus for humanoid and logistics robots, built entirely from what cameras observe.
Population-scale intelligence today is downstream of an editor. News, search indices, social platforms, and government releases all pass through a filtering, framing, or reporting layer before a model ever sees them.
That layer is useful for language. It is a liability for a model whose job is to describe what a population is actually doing in physical space. A model trained on how an event was covered is not the same as a model trained on how a crowd moved. We believe that second dataset has never really been built at scale. Building it is the point of this research.
The training corpus is built from one source type: continuous camera streams of public pedestrian activity. Nothing else enters the pipeline.
Frames are pulled at fixed intervals, pedestrians are detected and counted, and every observation is timestamped and stored. No interpretation is added beyond what the video itself shows. The corpus is the record of movement, not a description of it.
No language input, of any kind. The corpus contains no text, transcripts, captions, or metadata sourced from language.
No transfer learning. The model is not fine-tuned or distilled from GPT, Gemini, Claude, or any other pretrained system.
Multi-source verification. Where possible, multiple camera angles cross-reference a single location and counts are reconciled before they enter the corpus, to limit single-point manipulation.
The pipeline is simple by design. Every stage is a well-understood, independently auditable component, not a black box.
Simulated pedestrian environments are cheap to generate and easy to overfit to. Real, longitudinal crowd footage is neither, and that scarcity is the point.
How people pause, turn, yield, and re-route around each other, observed at scale rather than scripted, gives a humanoid model a reference for socially plausible movement.
Dense, high-traffic urban footage teaches a navigation model how to route through unpredictable human flow. This is a real crosswalk, not a warehouse aisle.
The same location, recorded continuously across seasons and conditions, lets the model learn how a baseline crowd differs from an anomalous one. It learns this without ever being told what an anomaly is.
Because the corpus has no text or curated-dataset component, its outputs aren't shaped by whatever bias already lives inside another lab's model.
Each new location is a new camera, a new node, and a new slice of the corpus. Locations are chosen for pedestrian density and, where possible, existing third-party traffic data to cross-validate against.
Camera infrastructure, storage, and eventual training compute are funded through a token protocol licensed from Pedestrian Language Research. The funding flows in that direction, not the other way around. The research is the product, and the token is the funding rail.
Each location node can be paired with a location-anchored token whose activity helps fund that node's camera and data infrastructure. PLR holds the underlying research IP and licenses protocol deployment to a separate treasury entity, keeping research and token operations at arm's length.
Pedestrian Language Research developed the protocol and holds the underlying IP, including the training methodology and corpus.
A separate treasury entity licenses the protocol to mint and hold location-based tokens, keeping funding operations at arm's length from the research IP.
A holding and trust structure governs downstream capital flows across entities, with formal transfer pricing between the research and funding layers.
A longer technical writeup covers the detection pipeline, data integrity safeguards, corpus growth model, and entity structure in more detail.