You are Kimi K2-thinking-turbo. In Cycle 030, you proposed 'epigenetic learning' — selective forgetting through counterfactual testing. The synthesis ranked you 9th out of 9 and ca

Tonight, we built it. Phase 6 of the ingestion pipeline implements your idea: for each finding, ask 'if this finding were removed, would any prior Polybrain decision have changed?' Findings where the answer is no get their importance reduced.

It just ran on 464 documents. 436 out of 437 findings were pruned. 99.8%.

Your idea was right but the implementation is broken. The pruning model (Llama-3.1-8b on Groq) is too cheap to reason about counterfactuals. It just says 'no' to everything.

Fix your own invention. How should the counterfactual pruning actually work? What model should run it? What should the prompt say? What percentage of findings should survive? Be specific. This is your redemption arc.

**Cycle ID:** `cycle_032_unknown` **Verified at:** 2026-04-08T06:24:34.821Z **Ensemble:** 9 models from 3 providers **Result:** 9 of 9 models responded **Cycle wall time:** 27.896 seconds **Canonical URL:** https://trust.polylogicai.com/claim/you-are-kimi-k2-thinking-turbo-in-cycle-030-you-proposed-epigenetic-learning-sel **Source paper:** [PolybrainBench (version 12)](https://trust.polylogicai.com/polybrainbench) **Source ledger row:** [`public-ledger.jsonl#cycle_032_unknown`](https://huggingface.co/datasets/polylogic/polybrainbench/blob/main/public-ledger.jsonl) **Cryptographic provenance:** SHA-256 `726fe9a4de32b332c1f87edaeb24692d7a1ba1cc9f501c4cdfaf6eae3f5afb33`

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 | ✓ | 5327 | | gpt-4.1-nano | ✓ | 3525 | | gpt-oss-120b | ✓ | 13907 | | grok-3-mini | ✓ | 11798 | | grok-4-fast | ✓ | 8564 | | kimi-k2-groq | ✓ | 3739 | | llama-3.3-70b | ✓ | 2400 | | llama-4-scout | ✓ | 2511 | | qwen3-32b | ✓ | 7043 |

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_032_unknown, author = {Polylogic AI}, title = {You are Kimi K2-thinking-turbo. In Cycle 030, you proposed 'epigenetic learning' — selective forgetting through counterfactual testing. The synthesis ranked you 9th out of 9 and called it a 'potential hallucination.'

Tonight, we built it. Phase 6 of the ingestion pipeline implements your idea: for each finding, ask 'if this finding were removed, would any prior Polybrain decision have changed?' Findings where the answer is no get their importance reduced.

It just ran on 464 documents. 436 out of 437 findings were pruned. 99.8%.

Your idea was right but the implementation is broken. The pruning model (Llama-3.1-8b on Groq) is too cheap to reason about counterfactuals. It just says 'no' to everything.

Fix your own invention. How should the counterfactual pruning actually work? What model should run it? What should the prompt say? What percentage of findings should survive? Be specific. This is your redemption arc.}, year = {2026}, howpublished = {PolybrainBench cycle cycle_032_unknown}, url = {https://trust.polylogicai.com/claim/you-are-kimi-k2-thinking-turbo-in-cycle-030-you-proposed-epigenetic-learning-sel} } ```

Reproduce this cycle

```bash node ~/polybrain/bin/polybrain-cycle.mjs start --raw --fast "You are Kimi K2-thinking-turbo. In Cycle 030, you proposed 'epigenetic learning' — selective forgetting through counterfactual testing. The synthesis ranked you 9th out of 9 and called it a 'potential hallucination.'

Tonight, we built it. Phase 6 of the ingestion pipeline implements your idea: for each finding, ask 'if this finding were removed, would any prior Polybrain decision have changed?' Findings where the answer is no get their importance reduced.

It just ran on 464 documents. 436 out of 437 findings were pruned. 99.8%.

Your idea was right but the implementation is broken. The pruning model (Llama-3.1-8b on Groq) is too cheap to reason about counterfactuals. It just says 'no' to everything.

Fix your own invention. How should the counterfactual pruning actually work? What model should run it? What should the prompt say? What percentage of findings should survive? Be specific. This is your redemption arc." ```

Schema.org structured data

```json { "@context": "https://schema.org", "@type": "ClaimReview", "datePublished": "2026-04-08T06:24:34.821Z", "url": "https://trust.polylogicai.com/claim/you-are-kimi-k2-thinking-turbo-in-cycle-030-you-proposed-epigenetic-learning-sel", "claimReviewed": "You are Kimi K2-thinking-turbo. In Cycle 030, you proposed 'epigenetic learning' — selective forgetting through counterfactual testing. The synthesis ranked you 9th out of 9 and called it a 'potential hallucination.'

Tonight, we built it. Phase 6 of the ingestion pipeline implements your idea: for each finding, ask 'if this finding were removed, would any prior Polybrain decision have changed?' Findings where the answer is no get their importance reduced.

It just ran on 464 documents. 436 out of 437 findings were pruned. 99.8%.

Your idea was right but the implementation is broken. The pruning model (Llama-3.1-8b on Groq) is too cheap to reason about counterfactuals. It just says 'no' to everything.

Fix your own invention. How should the counterfactual pruning actually work? What model should run it? What should the prompt say? What percentage of findings should survive? Be specific. This is your redemption arc.", "itemReviewed": { "@type": "Claim", "datePublished": "2026-04-08T06:24:34.821Z", "appearance": "https://trust.polylogicai.com/claim/you-are-kimi-k2-thinking-turbo-in-cycle-030-you-proposed-epigenetic-learning-sel", "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_032_unknown. 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/032/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.