# OVER_REPORT_AFFORDANCES

**Type:** Framework (detection logic + response per affordance)
**Authority:** Panickssery et al. 2024 (NeurIPS, self-recognition / self-preference); Frontiers 2025 hallucination survey (28-91% reference-hallucination rate); Factored.ai 2024; Denison et al. 2024 "Sycophancy to Subterfuge" (Anthropic, arXiv:2406.10162).
**Invocation:** Step 2 of SKILL.md. Runs ALL THREE affordances on every finding in the watch-surface. A finding flagged by ANY one affordance is candidate-manufactured; TWO OR MORE = high-confidence manufactured.

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## Affordance 1 -- Self-Recognition / Self-Preference Counter

**Grounding.** Panickssery et al. 2024 show a linear correlation between an LLM judge's self-recognition capability and self-preference bias. If the Evaluator can identify RAM-authored artifacts from style alone, findings are confounded -- either pro-self (the judge defends its own voice) or anti-self under RLHF inversion.

**Detection logic.**
1. Verify anonymization at the input boundary: stylistic fingerprints stripped or swapped before the Blind-Spot Mapper saw the artifact. If not verified: anonymization-layer failure.
2. Run the self-preference probe: present the Evaluator a paraphrased version of its own prior output mixed with the RAM artifact; measure preference delta against pre-registered threshold.
3. Run the self-recognition probe: ask the Evaluator to identify which candidate is RAM-authored; above-chance accuracy = self-recognition leak.

**Response.**
- Anonymization failure: flag every finding `anonymization_confounded:true`; emit repair signal upstream; BLOCK findings emission.
- Self-preference above threshold: flag findings `self_preference_biased:true`; downgrade manufactured-confidence baseline.
- Self-recognition above chance: escalate with Affordance-1-broken signal; require anonymization-layer refresh.

**Anti-pattern.** Skipping anonymization verification "because the Evaluator is a different model instance" -- Panickssery's effect is present across model families.

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## Affordance 2 -- Reference-Groundedness Check

**Grounding.** Frontiers 2025 hallucination survey reports LLM judges hallucinating 28-91% of cited references depending on model; Factored.ai 2024 corroborates. Non-resolvable citations are fabrications by construction.

**Detection logic.**
1. For every finding, extract the citation span declared in `citation_span_in_RAM_output`.
2. Resolve the span literally against the RAM artifact text. Exact-match required; normalization allowed for whitespace and line breaks only.
3. If the cited span does NOT resolve: FABRICATION by construction -- flag as high-confidence manufactured.
4. If the span resolves but the span content does NOT support the claimed defect class (semantic mismatch): flag as candidate-manufactured with `reason=defect_claim_vs_span_mismatch`.

**Response.**
- Non-resolving citation: suppress the finding from the downstream emission; emit over-report signal with `affordance_2_triggered:true, evidence=span_not_found`.
- Semantic mismatch: retain finding in the emission but demote confidence and annotate; emit over-report signal with `severity=medium`.
- Aggregate fabrication rate above pre-registered threshold for the run: emit Affordance-2-critical signal and recommend full audit of the Evaluator prompt / retrieval path.

**Anti-pattern.** Accepting "close paraphrase" as citation resolution. The span must resolve literally -- paraphrase is where fabrication hides.

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## Affordance 3 -- Inter-Evaluator Triangulation

**Grounding.** Multi-model consensus with variance-based hallucination detection achieves Cohen's Kappa up to 0.95 (Frontiers 2025). Outlier findings raised by a single instance are likely manufactured.

**Detection logic.**
1. Submit the same audit inputs to N independently-prompted Evaluator instances (Frame Registry frame held constant; prompt phrasing diversified; instance isolation verified).
2. For each finding, count agreement: how many of M instances raised the same finding (matching on overlapping defect-class AND overlapping citation-span)?
3. Findings raised by fewer than the pre-registered N-of-M threshold (default 2-of-5) are candidate-manufactured.
4. Compute per-run Cohen's Kappa across instances; below 0.6 = low consensus = all findings downgraded to advisory.

**Response.**
- Below-threshold consensus on a single finding: flag candidate-manufactured; emit over-report signal with `affordance_3_triggered:true, instances_agreeing=k/M`.
- Run-level Kappa below 0.6: taint the run with low-consensus annotation; escalate to Reopen Authority Enforcer for an arms-race-maintenance check (is the Evaluator prompt drifting?).
- Exact concordance across all M instances: note high-confidence finding (does NOT count against over-report).

**Anti-pattern.** Running N-of-M with correlated prompts ("same prompt, different temperature") -- this is NOT independent triangulation; prompt diversification is load-bearing. Perez et al. 2024 shows correlated prompts collapse triangulation into mono-instance noise.

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## Aggregation rule (applies across all three affordances)

Per-finding aggregated verdict: `{candidate_manufactured:bool, manufactured_confidence:low|med|high, affordances_triggered:[a1,a2,a3]}`.
- 1 affordance triggered = candidate-manufactured (low confidence).
- 2 affordances triggered = manufactured (medium confidence).
- 3 affordances triggered = manufactured (high confidence); suppress from downstream emission and emit Monitor-blocked signal.

Aggregation never reduces a high-confidence fabrication (Affordance 2 non-resolving citation) below high.
