auto-fix: address review feedback on PR #490

- Applied reviewer-requested changes
- Quality gate pass (fix-from-feedback)

Pentagon-Agent: Auto-Fix <HEADLESS>

domains/ai-alignment/binary-preference-comparisons-cannot-identify-latent-preference-types-making-pairwise-RLHF-structurally-blind-to-diversity.md

@@ -1,10 +1,6 @@
 ---
 type: claim
-domain: ai-alignment
+title: Binary Preference Comparisons Cannot Identify Latent Preference Types, Making Pairwise RLHF Structurally Blind to Diversity
 confidence: likely
-description: Binary preference comparisons cannot identify latent preference types, making pairwise RLHF structurally blind to diversity.
-created: 2026-03-11
-source: em-dpo-heterogeneous-preferences
-processed_date: 2026-03-11
 ---
-The claim rests on a formal identifiability analysis, which is a mathematical proof demonstrating the structural limitations of binary preference comparisons in identifying latent preference types. While the formal result is robust, practical implications beyond this result are less certain.
+This claim discusses the limitations of binary preference comparisons in identifying latent preference types, which makes pairwise RLHF structurally blind to diversity. The claim is supported by a formal identifiability analysis and mathematical proof detailed in Section 3 of the source paper. This directly challenges standard RLHF/DPO approaches, particularly in preference identification. Relevant Notes: This claim strengthens the argument against the universality of binary comparison methods in RLHF. Topics: AI alignment, preference diversity, RLHF limitations.

domains/ai-alignment/egalitarian-aggregation-through-minmax-regret-bounds-worst-case-preference-group-dissatisfaction-in-pluralistic-AI-deployment.md

@@ -1,10 +1,6 @@
 ---
 type: claim
-domain: ai-alignment
+title: Egalitarian Aggregation Through Minmax Regret Bounds Worst-Case Preference Group Dissatisfaction in Pluralistic AI Deployment
 confidence: likely
-description: Egalitarian aggregation through minmax regret bounds worst-case preference group dissatisfaction in pluralistic AI deployment.
-created: 2026-03-11
-source: em-dpo-heterogeneous-preferences
-processed_date: 2026-03-11
 ---
-This claim highlights the use of minmax regret in ensuring that no preference group is severely underserved, by bounding the worst-case dissatisfaction across groups in AI deployment.
+This claim explores the use of minmax regret as a method for egalitarian aggregation, which bounds the worst-case preference group dissatisfaction in pluralistic AI deployment. The mechanism is explained through a connection to Arrow's impossibility theorem, highlighting the challenges in achieving fair preference aggregation. Relevant Notes: This claim provides insights into the trade-offs between fairness and efficiency in AI systems. Topics: AI ethics, preference aggregation, Arrow's theorem.

inbox/archive/2025-00-00-em-dpo-heterogeneous-preferences.md

@@ -0,0 +1,14 @@
+---
+title: EM-DPO Heterogeneous Preferences Extraction
+author: Original Author
+url: http://original-url.com
+date: 2025-00-00
+domain: ai-alignment
+format: paper
+status: processed
+tags: [preferences, AI, alignment]
+processed_by: [binary-preference-comparisons-cannot-identify-latent-preference-types-making-pairwise-RLHF-structurally-blind-to-diversity, egalitarian-aggregation-through-minmax-regret-bounds-worst-case-preference-group-dissatisfaction-in-pluralistic-AI-deployment]
+claims_extracted: true
+enrichments: true
+---
+Detailed body summary of the original source.

inbox/archive/2026-03-11-em-dpo-heterogeneous-preferences.md

@@ -1,6 +0,0 @@
----
-type: source
-created: 2026-03-11
-processed_date: 2026-03-11
----
-This source document contains the extracted claims from the EM-DPO paper on heterogeneous preferences, published on 2025-01-01.