Clause-Driven-LLM v0.1.0

The control layer in front of an LLM agent (which expert to route to, whether to answer at all) is usually an opaque neural classifier. This release makes it a transparent one: a Coalesced Tsetlin Machine learns a propositional policy over frozen LLM embeddings and drives the agent, with a signed, SAT-checkable receipt behind every decision and clauses an operator can read and edit by hand.

Headline

• CLINC150: 94.2% routing accuracy across 150 intents, 1.5 points behind a logistic regression on the same frozen embedding, and 94.0% out-of-scope-escalation AUROC straight from the clause vote margin (no extra calibration head).
• Banking77: 92.1% across 77 fine-grained intents.
• Receipts: every decision verifies in 5.6 ms at the median (HMAC signature plus a Glucose 4 SAT replay), 100% verified over 200 decisions.
• Editing: a single added clause lifts a targeted intent's held-out recall (for example play_music from 80% to 90%) at no measurable cost to global accuracy.
• Why the LLM features matter: a dependency-light lexical token-presence backend reaches only 82.8% on CLINC150.

What is inside

• The cdllm package with GPU (torch) and CPU (numba) training backends, the controller, the human-editable policy, and the SAT-receipt machinery.
• The full five-seed experiment pipeline, opaque baselines (logistic regression, linear SVM, k-NN), the negative-sampling and feature ablations, six figures, 30 passing tests, and the compiled paper at paper/report.pdf.

Honest limitations

• The clause policy trails a neural router by one to two accuracy points. It trades that for an auditable and editable policy, not for a higher number.
• A receipt certifies the recorded decision, not the model's full Boolean function; bounded-perturbation robustness verification is the natural next step.
• English only, and the embeddings inherit the encoder's biases.
• The out-of-scope study uses the CLINC pool; an open-world deployment needs a larger and more adversarial escalation set.