Research · 02 · current priority
Thauten
Discrete auto-encoding of reasoning into an intermediate representation. The semiodynamic hypothesis, direct: meaning under compression has dynamics, and a model that reasons in a compressed representation is both cheaper and more robust than one that reasons in tokens.
Transformer-native language formation
Natural language is a lossy interface the model inherits from us, not the representation it would choose. Thauten rewards a model for expressing a reasoning step in the fewest bits that still reconstruct it exactly. Under that pressure the model drifts off human syntax and settles into a dense private notation — a language native to the transformer rather than to speech.
Two-stage process
First, semantic compression: map the meaning of a passage to a short code in a learned intermediate representation (IR). Second, semiodynamic extrusion: unfold that code only as far as a given compute budget allows, trading fidelity for speed on demand. A cheap query gets a coarse unfolding; a hard one gets the full expansion from the same code.
Hypercompressed reasoning
Once the IR exists, the model reasons in the compressed space without ever expanding to tokens. Each step operates on “meaning quanta” — the semantic equivalent of working directly on a zip file. Chains of thought that would run to thousands of tokens become dozens of IR operations.
Physics of meaning
In the IR a reasoning problem is a landscape and the answer is a path through it. Good reasoning looks less like retrieving a fact and more like water finding the low route — a tsunami washing over rigid static structure, flowing around hard constraints instead of colliding with them.
In the protocol
Thauten produces a compressed embedding zᵢ of every disclosed document.
Two things fall out of it: a learned latent value estimate, and a disclosure score
D that gates listing.
v₄ = f_θ(zᵢ) // latent-value estimator D ≥ D_min // VERIFY gate — cannot list below the floor