We are running a pilot experiment testing whether 'faith' (commitment-before-proof) improves AI system performance compared to 'caution' (verification-before-commitment). Config A

The pilot has n=5 per config. We know this is too small. We need to design the real experiment.

From your professional role, answer ONE of these:

1. What is the gold standard experimental design for testing whether a system property (like faith) causally improves performance? What methodology should we use? 2. What metrics should we measure? Not character count. What actually quantifies 'the system got smarter'? 3. What confounds must we control for? What would a peer reviewer attack? 4. What existing experiments in AI, cognitive science, or organizational behavior have tested similar hypotheses (commitment under uncertainty improving outcomes)? 5. What statistical tests are appropriate for this kind of sequential, compounding data?

Pick the one your methodology is best suited to answer. Under 300 words.

**Cycle ID:** `cycle_053_cyc_53_d7790e55` **Verified at:** 2026-04-08T15:35:01.525Z **Ensemble:** 9 models from 3 providers **Result:** 9 of 9 models responded **Cycle wall time:** 13.238 seconds **Canonical URL:** https://trust.polylogicai.com/claim/we-are-running-a-pilot-experiment-testing-whether-faith-commitment-before-proof- **Source paper:** [PolybrainBench (version 12)](https://trust.polylogicai.com/polybrainbench) **Source ledger row:** [`public-ledger.jsonl#cycle_053_cyc_53_d7790e55`](https://huggingface.co/datasets/polylogic/polybrainbench/blob/main/public-ledger.jsonl) **Cryptographic provenance:** SHA-256 `c2869f392f346588b8db50d0577b77566aa222c1b3e47fc35a7253eedbb7a09a`

Verification verdict

Of 9 models in the ensemble, 9 responded successfully and 0 failed.

Per-model responses

The full text of each model's response is available in the source ledger. The summary below records each model's success or failure and the first 280 characters of its response.

| Model | Status | Response chars | | --- | :---: | ---: | | gpt-4.1-mini | ✓ | 2465 | | gpt-4.1-nano | ✓ | 3862 | | gpt-oss-120b | ✓ | 3326 | | grok-3-mini | ✓ | 4365 | | grok-4-fast | ✓ | 1957 | | kimi-k2-groq | ✓ | 1255 | | llama-3.3-70b | ✓ | 1453 | | llama-4-scout | ✓ | 1927 | | qwen3-32b | ✓ | 4672 |

Pairwise agreement

The pairwise Jaccard agreement between successful responses for this cycle:

_Per-cycle pairwise agreement matrix is computed offline; will be populated in canonical page v2._

Divergence score

This cycle's divergence score is **TBD** on a 0 to 1 scale, where 0 means all responses are token-identical and 1 means no two responses share any tokens. The dataset-wide median divergence is 0.5 for context.

How to cite this claim

```bibtex @misc{polybrainbench_claim_cycle_053_cyc_53_d7790e55, author = {Polylogic AI}, title = {We are running a pilot experiment testing whether 'faith' (commitment-before-proof) improves AI system performance compared to 'caution' (verification-before-commitment). Config A starts fresh each cycle. Config B feeds unverified findings forward.

The pilot has n=5 per config. We know this is too small. We need to design the real experiment.

From your professional role, answer ONE of these:

1. What is the gold standard experimental design for testing whether a system property (like faith) causally improves performance? What methodology should we use? 2. What metrics should we measure? Not character count. What actually quantifies 'the system got smarter'? 3. What confounds must we control for? What would a peer reviewer attack? 4. What existing experiments in AI, cognitive science, or organizational behavior have tested similar hypotheses (commitment under uncertainty improving outcomes)? 5. What statistical tests are appropriate for this kind of sequential, compounding data?

Pick the one your methodology is best suited to answer. Under 300 words.}, year = {2026}, howpublished = {PolybrainBench cycle cycle_053_cyc_53_d7790e55}, url = {https://trust.polylogicai.com/claim/we-are-running-a-pilot-experiment-testing-whether-faith-commitment-before-proof-} } ```

Reproduce this cycle

```bash node ~/polybrain/bin/polybrain-cycle.mjs start --raw --fast "We are running a pilot experiment testing whether 'faith' (commitment-before-proof) improves AI system performance compared to 'caution' (verification-before-commitment). Config A starts fresh each cycle. Config B feeds unverified findings forward.

The pilot has n=5 per config. We know this is too small. We need to design the real experiment.

From your professional role, answer ONE of these:

1. What is the gold standard experimental design for testing whether a system property (like faith) causally improves performance? What methodology should we use? 2. What metrics should we measure? Not character count. What actually quantifies 'the system got smarter'? 3. What confounds must we control for? What would a peer reviewer attack? 4. What existing experiments in AI, cognitive science, or organizational behavior have tested similar hypotheses (commitment under uncertainty improving outcomes)? 5. What statistical tests are appropriate for this kind of sequential, compounding data?

Pick the one your methodology is best suited to answer. Under 300 words." ```

Schema.org structured data

```json { "@context": "https://schema.org", "@type": "ClaimReview", "datePublished": "2026-04-08T15:35:01.525Z", "url": "https://trust.polylogicai.com/claim/we-are-running-a-pilot-experiment-testing-whether-faith-commitment-before-proof-", "claimReviewed": "We are running a pilot experiment testing whether 'faith' (commitment-before-proof) improves AI system performance compared to 'caution' (verification-before-commitment). Config A starts fresh each cycle. Config B feeds unverified findings forward.

The pilot has n=5 per config. We know this is too small. We need to design the real experiment.

From your professional role, answer ONE of these:

1. What is the gold standard experimental design for testing whether a system property (like faith) causally improves performance? What methodology should we use? 2. What metrics should we measure? Not character count. What actually quantifies 'the system got smarter'? 3. What confounds must we control for? What would a peer reviewer attack? 4. What existing experiments in AI, cognitive science, or organizational behavior have tested similar hypotheses (commitment under uncertainty improving outcomes)? 5. What statistical tests are appropriate for this kind of sequential, compounding data?

Pick the one your methodology is best suited to answer. Under 300 words.", "itemReviewed": { "@type": "Claim", "datePublished": "2026-04-08T15:35:01.525Z", "appearance": "https://trust.polylogicai.com/claim/we-are-running-a-pilot-experiment-testing-whether-faith-commitment-before-proof-", "author": { "@type": "Organization", "name": "PolybrainBench" } }, "reviewRating": { "@type": "Rating", "ratingValue": "9", "bestRating": "9", "worstRating": "0", "alternateName": "Unanimous" }, "author": { "@type": "Organization", "name": "Polylogic AI", "url": "https://polylogicai.com" } } ```

Provenance and integrity

This page was generated by the PolybrainBench daemon at version 0.1.0 from cycle cycle_053_cyc_53_d7790e55. The full provenance chain (per-response SHA-256 stamps, cross-cycle prev-hash linking, Thalamus grounding verification) is recorded in the source cycle directory at `~/polybrain/cycles/053/provenance.json` and mirrored in the published dataset. The page is regenerated on every harvest pass; the URL is permanent and the content is immutable for any given paper version.