What question did this study set out to answer?

This paper aims to update on the Phase 2b verification of LayerForge v9, focusing on a new two-layer fidelity structure and hybrid ensemble performance.

May 19, 2026Open Access

LayerForge v9: Phase 2b Verification Update — Two-Layer Fidelity Structure and Hybrid Ensemble Path

Key Points

This paper aims to update on the Phase 2b verification of LayerForge v9, focusing on a new two-layer fidelity structure and hybrid ensemble performance.
Provided a summary of the v9 main paper, along with peer review history and relevant JSON output files for independent verification.
Assessed the hybrid ensemble path using paired t-tests on two datasets, hotpotqa (N=100) and livedoor (N=27).
Implemented a bounded ablation budget with multiple parameter tests to ensure robustness without exceeding the improvement threshold.
Hybrid ensemble demonstrates statistical significance with hotpotqa showing p=2.7e-05 and livedoor p=5.1e-08.
The two-layer fidelity structure effectively differentiates between thematic and lexical accuracy across datasets.
No parameter changes exceeded the predetermined IMPROVEMENT-LARGE threshold during the ablation testing.

Abstract

Supplementary deposit for LayerForge v9 (Phase 2b Verification Update). Includes the v9 main paper PDF, appendix PDF, 3-round peer review history (Reviewer Agent / Author Agent loop, Accept), public verification index, and 22 raw JSON output files sufficient for independent re-verification of the paper's numeric claims (p-values, compₘean, accuracy). Core contributions of v9: Two-layer fidelity structure (theme-level semantic PASS / fact-level lexical FAIL) corroborated by multiple metric families on two datasets (hotpotqa EN, livedoor JA). Statistically significant Hybrid ensemble path (LayerForge + K-means): hotpotqa N=100 p=2. 7e-05, livedoor N=27 p=5. 1e-08 (paired t-test, ensemble vs LayerForge alone). Baseline parameter values frozen under bounded ablation budget (14 ablations, tested N=5-30, no parameter change reached the IMPROVEMENT-LARGE threshold). Explicit extrapolation boundaries per pattern coverage. The v8. 1 core algorithm is unchanged in v9; this update adds verification depth and an evidence-grounded improvement direction (Hybrid ensemble) without modifying the v8. 1 spec. Self-preference bias disclosure: The v9 paper was drafted with Claude Code assistance, and the 3-round peer review loop was conducted by Claude Code subagents (same model family). Per Anthropic's LLM-as-judge guidance, formal acceptance evidence requires cross-check by either (a) a human reviewer, (b) a different LLM family (GPT-4 / Gemini / Mistral), or (c) Anthropic API direct + Haiku 4. 5. This deposit's verificationᵣesults/ contains only the metric-only subset (22 JSON files of the 27 total verifications cited in the paper). Driver scripts and per-record verification narratives (ADR-022 Sections 1-8 format) are maintained in a separate author-maintained repository, currently private during pre-publication review, available to peer reviewers on request from the corresponding author. Note on versioning: The paper itself is titled "LayerForge v9" (= the author's internal version corresponding to the Phase 2b verification update of the v8. 1 published baseline). In Zenodo's version system this is registered as Version 1. 1 (minor update of the v8. 1 baseline deposit, Zenodo Version 1. 0, DOI 10. 5281/zenodo. 19651035).

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