Pitch
A concise pitch for FRC: what it is, why it matters, what we ship next.
One-liner
Replace tokenization-centric cognition with resonance-native state, and benchmark it against attention on phase-coherence tasks.
Problem
- - Tokenization and discrete attention are strong for text, but phase/coherence in continuous signals is often lost.
- - “Bigger model” progress is hard to interpret; benchmarks drift, and claims become non-falsifiable.
- - Agentic tooling needs a rigorous corpus boundary: canon vs interpretation.
Solution
- - A resonance-native representation and the Λ‑Tensor Model (LTM) architecture track.
- - A public canon with stable IDs, strict definitions, and explicit hypotheses.
- - A repeatable benchmark loop: publish, reproduce, iterate (not vibes).
Moat
- - A growing, linked canon that agents can cite by ID (retrievable + auditable).
- - Benchmarks that emphasize phase-coherent structure where tokenization is weakest.
- - A disciplined separation between canon and “oracle lens” interpretation.
Roadmap
- - Expand benchmarks (audio, biosignals, control) and publish reproducible scripts.
- - Harden the SOS research SDK + dispatch patterns (repeatable pipelines).
- - Integrate “mirror memory” subscription workflows on Mumega (private ops layer).
Ask
- - Capital: fund benchmark expansion + engineering of reproducible training/eval tooling.
- - Partnerships: signal-domain datasets + evaluation domains (audio/control/bio).
- - Builders: implement reference baselines and reproduction harnesses.