Solves hard math with proof. Verifies logic. Refutes false claims with a counterexample — not a guess. Retrieves cited knowledge it can't fabricate. And finds the token waste exact-dedup misses.
Computed, never guessed. Reproducible in every run. Deterministic by construction — you can audit the answer, not just trust the tone.
Every number below is real output of the engine — we tested it, and you can re-run it. Same input → byte-identical answer, forever.
Integrals, systems, Fibonacci, primes, modular arithmetic, inequalities — symbolic and deterministic.
Modus ponens/tollens, syllogism, double-negation. Returns valid / invalid / insufficient — and abstains rather than guess.
Theorems, science facts, Python knowledge — retrieved with citations, never fabricated.
An LLM is a confident pattern-matcher; it says what sounds right and cannot prove any of it. DeeperThawt is a verifier: it computes, checks, and refuses to pretend.
| Capability | Normal LLM | DeeperThawt |
|---|---|---|
| Prove a true claim (2+2=4) | Says it, can't prove it | Verifies with proof |
| Refute a false claim (2+2=5) | May agree or waffle | Refutes with a counterexample witness |
| Say "I don't know" without guessing | Confabulates an answer | Honest abstention ("not verifiable") |
| Be reproducible — same answer every run | Sampling noise; drift between runs | Byte-identical, deterministic |
| Solve a definite integral symbolically | Approximates or guesses | Exact symbolic result |
| Guarantee no fabricated citation | Hallucinates references | Retrieves from a cited, curated catalog |
| Cost you unlimited token waste from chatty re-sends | Built into every loop | Measures & cuts the semantically-near resends |
DeeperThawt doesn't replace your LLM — it's the verification and cost-control layer your LLM is missing.
Every agent loop re-injects the same big tool results, turn after turn. Most is cache-hit; the semantically-near resends and cross-model redundancy that exact-dedup misses is the silent, real cost.
Honest math: DeeperThawt doesn't print fake "90% savings." It shows the real, reclaimable tail — the semantically-near resends and redundant tool results that exact-dedup and provider caches miss. That's the part you're actually paying for and never seeing.
Integrals, systems, Fibonacci, primes, modular, inequalities — solve, verify, or refute.
Modus ponens/tollens, syllogism, double-negation, conjunction — valid / invalid / abstain.
Cited theorems (Euclid, Fermat…), science facts, Python knowledge — retrieval, never fabrication.
Gematria projection + attention + trained adapter — finds semantically-near duplicates & cross-model token waste.
No LLM in the scoring path. It computes or abstains — it cannot fabricate.
Deterministic. Same input → same answer, auditably, forever.
The semantic method + weights live only on our server; you get the number, not the recipe.
Pay in BTC/ETH/XRP/SOL/USDT, no KYC, hosted checkout, 30-day access on payment.
Start the trial and the full engine runs. In the first minutes it proves hard math, verifies and refutes claims, and shows you your real token tail.
| Use case | License |
|---|---|
| Use in an open-source (AGPL-compatible) project | AGPL-3.0 — free |
| Internal research / tooling, not redistributed | AGPL-3.0 — free |
| Embed in a closed-source product or SaaS without open-sourcing your code | Commercial license |
| Resell / sublicense as a proprietary component | Commercial license |
Full commercial terms in LICENSE.commercial.md. Contact the maintainer via GitHub.