Truth Beam

Verifiable video capture via optical temporal anchoring. Don't trust; verify.

New here? Start with the plain-language introduction - why a verifiable recording matters and what it is for - then come back here for the evidence.

Up front: this proves the provenance of the capture record under a committed protocol (the physical-interaction reading is the separately measured layer), not the semantic truth of the scene. A clean, recomputable result - the provenance pipeline recomputable end to end - not general deepfake detection.

Truth Beam makes the act of recording itself verifiable: it lights the scene with a code that, in the released evaluation, this rig answered correctly while every tested forgery and wrong emission failed. Mechanically, it treats the projector-scene-camera assembly as a PUF-like challenge-response channel. Each frame's illumination is a fresh challenge derived from the evolving chain state (a BLAKE3-XOF expansion over prior captures, with a fresh drand round folded in each step as unpredictable public entropy); a Rootstock (RSK) block is committed at genesis, and the chain states and final root are anchored outward to RSK for public, timestamped ordering. The captured frame answers that challenge live and is hash-sealed into the chain. The optical challenge is generated from the chain, not appended to a finished recording.

The capture is then checked by a conditional-diffusion verifier: a diffusion model, conditioned on the chain-derived emission, scores how well a frame matches the light this rig should have produced under those committed controls. It clears two distinct bars:

• Emission discrimination. Two designs, kept separate. A ranking test scores each of 120 frames against the true emission plus fifty wrong same-session ones (±10 neighbours excluded); the true emission ranks first of 51 every time (top-1 = 100%). A separate held-out AUROC (n = 198 and 200) scores each correct emission against temporal-offset negatives (the emissions at -2, +2, -15, +15, and +30 frames) and reaches AUROC = 1.000.
• Trained-forger probe. Separately, a Path-A probe (Path A is the fast recompute path) on the verifier's per-frame scores tells a genuine capture from a forged one - the trained forger F-A v1, whose weights are public, so you can load it (or build a stronger attacker) and try to beat the verifier - at AUROC = 1.000: a measured floor against that one attacker, not general deepfake detection.

All of these are held-out, same-rig, two-session, single-performer results.

This single-rig result is, by design, the reference signal: a clean, recomputable baseline - the optical-provenance counterpart of the basic Reality Kernel loop - against which more complex datasets (cross-session, cross-rig) and networks are measured. More sessions, cross-session verification, and partner rigs are the next step.

In the V10 session, four large language models (Claude, Grok, and two OpenAI models) directed a live improvisation; every directive is committed into the chain and anchored on-chain. Read the directives, including the instruction to mouth ten unguessable words ("...dinghy opposite gecko...").

curl -fsSL https://data.truthbeam.com/release/truthbeam_verify.tar.gz | tar xz
cd truthbeam_verify
bash verify_all.sh

Where each layer stands

Truth Beam is the verifiable core of PolieBotics, the wider research programme. It is one demonstrated instance of a more general object, the Reality Kernel: a Markov kernel, here applied to a projector-camera system. The parent apparatus was first filed in 2023 (published as WO 2025/046153 A2); the generalised formalism is set out in Filing 1 (apparatus, 2026), the governance layer in Filing 2 (2026), and the self-witnessing layer in Filing 3 (filed 3 July 2026). The exploration layer at PoliePals accompanies it: an imaginal universe, the proposed Filing-2 governance, and the open fringe, all kept separate and never offered as evidence for the technology.

Scope: a same-rig, single-performer, two-session corpus evaluated against one trained forger (F-A v1). A clean, measured, end-to-end result, not a claim of general deepfake detection. The honest-rig assumption is about the initial data collection that builds the training corpus, not a free pass in the held-out evaluation, which the verifier still has to pass on unseen frames. That is the released scope, not a fundamental ceiling: the design goal, set out in the filings, is that a verifier trained across many rigs can in principle check an untrusted rig (the whole point), a capability enabled in the filings rather than demonstrated here.

This is an LLM-mediated dataset: written to be parsed and re-presented by a large language model, not only read by a human. Point your own LLM at this page to explain, check, or recompute it. Treat the prose as a machine-authored summary; verify it against the linked sources.