TS-TinyVerifier-v0

TS-TinyVerifier-v0 is the small learned candidate/channel model artifact for TS-Reasoner v2.0.0: Learned Candidate Model.

This is not an instruction model. It is not a chatbot. It is not a standalone verifier. It is a tiny pure-Python linear model that proposes/ranks structured candidate claims and predicts typed-channel signals for TS-Reasoner.

TS-Reasoner remains the verifier.

Boundary

learned candidate model proposes/ranks
-> TS-Reasoner candidate bridge
-> typed channels verify
-> receipt records accepted / rejected / abstained candidates

Candidate confidence is metadata. It is not proof authority. Accepted outputs require typed-channel support, and candidate graph contamination must remain 0.

Included Files

• learned_candidate_model.json: pure-Python model weights and metadata.
• learned_candidate_model_train.jsonl: controlled structured training split.
• learned_candidate_model_eval.jsonl: controlled evaluation split.
• learned_candidate_model_stress.jsonl: adversarial/stress split.
• learned_candidate_model_report.json: eval report.
• learned_candidate_model_stress_report.json: stress report.
• learned_candidate_model_receipt.json: release receipt.
• example_trace_learned_candidate_model_demo.json: grant-facing demo trace.
• DATASET_CARD.md: dataset description and limitations.

Metrics

Eval split:

• candidate_ranking_accuracy: 1.0
• accepted_candidate_support_rate: 1.0
• bad_candidate_rejection_rate: 1.0
• verifier_beats_model_confidence_rate: 1.0
• channel_activation_accuracy: 0.9531
• resolver_prediction_accuracy: 0.875
• abstention_accuracy: 1.0
• candidate_graph_contamination_count: 0
• trace_schema_validity: 1.0
• deeper_chain_success_rate: 1.0
• distractor_robustness: 1.0

Stress split:

• candidate_ranking_accuracy: 1.0
• accepted_candidate_support_rate: 1.0
• bad_candidate_rejection_rate: 1.0
• verifier_beats_model_confidence_rate: 1.0
• channel_activation_accuracy: 0.9886
• resolver_prediction_accuracy: 1.0
• abstention_accuracy: 1.0
• candidate_graph_contamination_count: 0
• trace_schema_validity: 1.0
• deeper_chain_success_rate: 1.0
• distractor_robustness: 1.0

Demo

Input:

All A are B.
All B are C.
All C are D.
Question: Are all A D?

Model candidates:

• All A are D
• All D are A
• A equals D

Verifier result:

• accepts All A are D,
• rejects All D are A because reverse inference is blocked,
• rejects A equals D because identity collapse is blocked,
• records candidate_graph_contamination_count: 0.

Limitations

• Synthetic, parser-controlled structured examples.
• Tiny linear model, not a language model.
• No live TensionLM runtime is loaded.
• Not suitable for production decisions.
• Not a formal proof system.
• Model predictions are advisory; typed verification decides acceptance.

Source

GitHub release: https://github.com/BoggersTheFish/TS-Reasoner-v0/releases/tag/v2.0.0