---
license: mit
tags:
  - reasoning
  - interpretability
  - candidate-ranking
  - typed-verifier
  - thinking-system
library_name: other
pipeline_tag: text-classification
---

# 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

```text
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:

```text
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