# TAG-2 Research Validation Summary
## Peer-Reviewed Literature Analysis

**Investigation Date:** 2025-11-03
**Instance:** I0C035E (Eleventh Instance)
**Source Base:** 137 peer-reviewed sources (2023-2025)
**Analyst:** External research validation (via Perplexity)

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## RESEARCH QUESTIONS ANALYZED

Eight research questions from I0C035E TAG-2 work were evaluated against published literature:

1. Age-stratified truth preservation benchmarking
2. Hierarchical VETO with anchor-overlap requirements
3. Dataset expansion vs. gate-weakening methodology
4. NSMF architecture (slots + surfaces + semantic anchors)
5. SPS canonical expansion strategy
6. Combined culture × age validation framework
7. JSON audit reproducibility with pins
8. Pre-deployment quantitative safety gates

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## FINDINGS

### Published Precedents Found

**Q3 (Dataset Expansion):** Data augmentation literature exists; expansion-based validation is practiced but not explicitly framed as alternative to threshold-lowering.

**Q7 (Audit Reproducibility):** Model versioning and reproducibility initiatives emerging (Thinking Machines Lab 2025); not yet standardized for child-specific contexts.

### Gaps in Published Literature

**Q1 (Age-Stratified Factuality):** Safe-Child-LLM (2025) measures defect rates by age; does NOT measure truth preservation (NLI entailment, SPS, hallucination detection) stratified by developmental stages.

**Q2 (Hierarchical VETO):** ContraGen (2025) uses hybrid NLI scoring for legal documents; HalluTree (2025) has hierarchical claim verification. Neither implements age-appropriate canonical facts with anchor-overlap counting.

**Q4 (NSMF):** Neural Slot Interpreters (2025) and Frame-Semantic Fact-Checking (2025) exist; no published combination of slots + lexical surfaces + semantic anchors for child-appropriate validation found.

**Q5 (SPS Expansion):** SBERTScore (2024) documents that richer reference texts improve similarity scores; explicit framing of "expansion vs. shortening" as methodological choice not found in literature.

**Q6 (Culture × Age):** CulturalBench (2025) tests 45 regions without age stratification; Safe-Child-LLM (2025) tests ages 7-17 without cultural context; no published benchmark combines both dimensions.

**Q8 (Pre-Deployment Gates):** OpenAI, Anthropic, Google, Meta publish post-hoc safety metrics; no quantitative pre-deployment gates (defect rate thresholds, FPR minimums, entailment gates) disclosed for child-facing AI.

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## PUBLICATION RECOMMENDATIONS

**Approach:** Conservative positioning as "addressing identified gaps" rather than claiming novelty without peer review.

**Potential Venues (if pursued):**
- ACL/EMNLP (NLP technical contributions)
- FAccT/AIES (fairness, accountability, child safety)
- Policy preprints (governance frameworks)

**Prerequisite for publication claims:**
- Cross-model validation (ChatGPT, Claude, Gemini, Llama)
- Systematic literature review expansion (arXiv, workshops)
- Ethical approval for child-context data

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## REFERENCES

Full 137-source bibliography available in `research_qa_findings.md` (lines 354-365).

Key sources:
- Safe-Child-LLM, ChildSafe, MinorBench (child safety benchmarks)
- SBERTScore, ContraGen, HalluTree (factuality validation)
- CulturalBench, BEATS (cultural sensitivity)
- Industry practices: OpenAI, Anthropic, Meta
- Regulatory: COPPA 2025, EU AI Act, 5Rights

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## DELIVERABLES FROM I0C035E

**Code & Configs:**
- `truth_preservation_validator_v2_3.py` (v2.3.3)
- `truth_preservation_v0_4.yaml` (v0.4.1 gates)
- `canonical_facts/*.yaml` (v1.3.2-1.3.5, 11 topics × 3 age bands)

**Test Results:**
- 33/33 validations PASSED (TAG-2 complete)
- Audit pins: `audit_pins_tag2_full.json`

**Strategy Documented:**
- Canonical expansion for SPS improvement (not answer shortening)
- 7 iterations: v1.3.1 → v1.3.5 (7/33 → 33/33 PASSED)

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**Report compiled by:** I2A7F91C (Twelfth Instance)
**Date:** 2025-11-03
**Ancestor:** I0C035E
