Merge pull request 'extract: 2025-00-00-em-dpo-heterogeneous-preferences' (#1108) from extract/2025-00-00-em-dpo-heterogeneous-preferences into main

domains/ai-alignment/maxmin-rlhf-applies-egalitarian-social-choice-to-alignment-by-maximizing-minimum-utility-across-preference-groups.md

@@ -37,6 +37,12 @@ Chakraborty et al., "MaxMin-RLHF: Alignment with Diverse Human Preferences," ICM
 - Tulu2-7B: 56.67% win rate across both groups vs 42% minority/70.4% majority for single reward
 - 33% improvement for minority groups without majority compromise
 
+
+### Additional Evidence (extend)
+*Source: [[2025-00-00-em-dpo-heterogeneous-preferences]] | Added: 2026-03-16*
+
+MMRA extends maxmin RLHF to the deployment phase by minimizing maximum regret across preference groups when user type is unknown at inference, showing how egalitarian principles can govern both training and inference in pluralistic systems.
+
 ---
 
 Relevant Notes:

domains/ai-alignment/pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state.md

@@ -21,10 +21,16 @@ Since [[universal alignment is mathematically impossible because Arrows impossib
 
 
 ### Additional Evidence (extend)
-*Source: [[2024-02-00-chakraborty-maxmin-rlhf]] | Added: 2026-03-15 | Extractor: anthropic/claude-sonnet-4.5*
+*Source: 2024-02-00-chakraborty-maxmin-rlhf | Added: 2026-03-15 | Extractor: anthropic/claude-sonnet-4.5*
 
 MaxMin-RLHF provides a constructive implementation of pluralistic alignment through mixture-of-rewards and egalitarian optimization. Rather than converging preferences, it learns separate reward models for each subpopulation and optimizes for the worst-off group (Sen's Egalitarian principle). At Tulu2-7B scale, this achieved 56.67% win rate across both majority and minority groups, compared to single-reward's 70.4%/42% split. The mechanism accommodates irreducible diversity by maintaining separate reward functions rather than forcing convergence.
 
+
+### Additional Evidence (confirm)
+*Source: [[2025-00-00-em-dpo-heterogeneous-preferences]] | Added: 2026-03-16*
+
+EM-DPO implements this through ensemble architecture: discovers K latent preference types, trains K specialized models, and deploys them simultaneously with egalitarian aggregation. Demonstrates that pluralistic alignment is technically feasible without requiring demographic labels or manual preference specification.
+
 ---
 
 Relevant Notes:

domains/ai-alignment/rlhf-is-implicit-social-choice-without-normative-scrutiny.md

@@ -35,10 +35,16 @@ RLCF makes the social choice mechanism explicit through the bridging algorithm (
 
 
 ### Additional Evidence (confirm)
-*Source: [[2026-02-00-an-differentiable-social-choice]] | Added: 2026-03-16*
+*Source: 2026-02-00-an-differentiable-social-choice | Added: 2026-03-16*
 
 Comprehensive February 2026 survey by An & Du documents that contemporary ML systems implement social choice mechanisms implicitly across RLHF, participatory budgeting, and liquid democracy applications, with 18 identified open problems spanning incentive guarantees and pluralistic preference aggregation.
 
+
+### Additional Evidence (extend)
+*Source: [[2025-00-00-em-dpo-heterogeneous-preferences]] | Added: 2026-03-16*
+
+EM-DPO makes the social choice function explicit by using MinMax Regret Aggregation based on egalitarian fairness principles, demonstrating that pluralistic alignment requires choosing a specific social welfare function (here: maximin regret) rather than pretending aggregation is value-neutral.
+
 ---
 
 Relevant Notes:

domains/ai-alignment/single-reward-rlhf-cannot-align-diverse-preferences-because-alignment-gap-grows-proportional-to-minority-distinctiveness.md

@@ -35,10 +35,16 @@ Study demonstrates that models trained on different demographic populations show
 
 
 ### Additional Evidence (extend)
-*Source: [[2026-02-00-an-differentiable-social-choice]] | Added: 2026-03-16*
+*Source: 2026-02-00-an-differentiable-social-choice | Added: 2026-03-16*
 
 An & Du's survey reveals the mechanism behind single-reward failure: RLHF is doing social choice (preference aggregation) but treating it as an engineering detail rather than a normative design choice, which means the aggregation function is chosen implicitly and without examination of which fairness criteria it satisfies.
 
+
+### Additional Evidence (extend)
+*Source: [[2025-00-00-em-dpo-heterogeneous-preferences]] | Added: 2026-03-16*
+
+EM-DPO provides formal proof that binary comparisons are mathematically insufficient for preference type identification, explaining WHY single-reward RLHF fails: the training signal format cannot contain the information needed to discover heterogeneity, regardless of dataset size. Rankings over 3+ responses are necessary.
+
 ---
 
 Relevant Notes:

inbox/archive/.extraction-debug/2025-00-00-em-dpo-heterogeneous-preferences.json

@@ -0,0 +1,48 @@
+{
+  "rejected_claims": [
+    {
+      "filename": "binary-preference-comparisons-cannot-identify-latent-preference-types-making-pairwise-rlhf-structurally-blind-to-diversity.md",
+      "issues": [
+        "missing_attribution_extractor"
+      ]
+    },
+    {
+      "filename": "em-algorithm-preference-clustering-discovers-latent-user-types-without-demographic-labels-enabling-unsupervised-pluralistic-alignment.md",
+      "issues": [
+        "missing_attribution_extractor"
+      ]
+    },
+    {
+      "filename": "minmax-regret-aggregation-ensures-no-preference-group-is-severely-underserved-by-applying-egalitarian-fairness-to-ensemble-deployment.md",
+      "issues": [
+        "missing_attribution_extractor"
+      ]
+    }
+  ],
+  "validation_stats": {
+    "total": 3,
+    "kept": 0,
+    "fixed": 11,
+    "rejected": 3,
+    "fixes_applied": [
+      "binary-preference-comparisons-cannot-identify-latent-preference-types-making-pairwise-rlhf-structurally-blind-to-diversity.md:set_created:2026-03-16",
+      "binary-preference-comparisons-cannot-identify-latent-preference-types-making-pairwise-rlhf-structurally-blind-to-diversity.md:stripped_wiki_link:single-reward-rlhf-cannot-align-diverse-preferences-because-",
+      "binary-preference-comparisons-cannot-identify-latent-preference-types-making-pairwise-rlhf-structurally-blind-to-diversity.md:stripped_wiki_link:rlhf-is-implicit-social-choice-without-normative-scrutiny.md",
+      "binary-preference-comparisons-cannot-identify-latent-preference-types-making-pairwise-rlhf-structurally-blind-to-diversity.md:stripped_wiki_link:pluralistic alignment must accommodate irreducibly diverse v",
+      "em-algorithm-preference-clustering-discovers-latent-user-types-without-demographic-labels-enabling-unsupervised-pluralistic-alignment.md:set_created:2026-03-16",
+      "em-algorithm-preference-clustering-discovers-latent-user-types-without-demographic-labels-enabling-unsupervised-pluralistic-alignment.md:stripped_wiki_link:modeling preference sensitivity as a learned distribution ra",
+      "em-algorithm-preference-clustering-discovers-latent-user-types-without-demographic-labels-enabling-unsupervised-pluralistic-alignment.md:stripped_wiki_link:pluralistic alignment must accommodate irreducibly diverse v",
+      "minmax-regret-aggregation-ensures-no-preference-group-is-severely-underserved-by-applying-egalitarian-fairness-to-ensemble-deployment.md:set_created:2026-03-16",
+      "minmax-regret-aggregation-ensures-no-preference-group-is-severely-underserved-by-applying-egalitarian-fairness-to-ensemble-deployment.md:stripped_wiki_link:maxmin-rlhf-applies-egalitarian-social-choice-to-alignment-b",
+      "minmax-regret-aggregation-ensures-no-preference-group-is-severely-underserved-by-applying-egalitarian-fairness-to-ensemble-deployment.md:stripped_wiki_link:post-arrow-social-choice-mechanisms-work-by-weakening-indepe",
+      "minmax-regret-aggregation-ensures-no-preference-group-is-severely-underserved-by-applying-egalitarian-fairness-to-ensemble-deployment.md:stripped_wiki_link:minority-preference-alignment-improves-33-percent-without-ma"
+    ],
+    "rejections": [
+      "binary-preference-comparisons-cannot-identify-latent-preference-types-making-pairwise-rlhf-structurally-blind-to-diversity.md:missing_attribution_extractor",
+      "em-algorithm-preference-clustering-discovers-latent-user-types-without-demographic-labels-enabling-unsupervised-pluralistic-alignment.md:missing_attribution_extractor",
+      "minmax-regret-aggregation-ensures-no-preference-group-is-severely-underserved-by-applying-egalitarian-fairness-to-ensemble-deployment.md:missing_attribution_extractor"
+    ]
+  },
+  "model": "anthropic/claude-sonnet-4.5",
+  "date": "2026-03-16"
+}

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

@@ -7,9 +7,13 @@ date: 2025-01-01
 domain: ai-alignment
 secondary_domains: []
 format: paper
-status: unprocessed
+status: enrichment
 priority: medium
 tags: [pluralistic-alignment, EM-algorithm, preference-clustering, ensemble-LLM, fairness]
+processed_by: theseus
+processed_date: 2026-03-16
+enrichments_applied: ["single-reward-rlhf-cannot-align-diverse-preferences-because-alignment-gap-grows-proportional-to-minority-distinctiveness.md", "rlhf-is-implicit-social-choice-without-normative-scrutiny.md", "pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state.md", "maxmin-rlhf-applies-egalitarian-social-choice-to-alignment-by-maximizing-minimum-utility-across-preference-groups.md"]
+extraction_model: "anthropic/claude-sonnet-4.5"
 ---
 
 ## Content
@@ -31,7 +35,7 @@ EM-DPO uses expectation-maximization to simultaneously uncover latent user prefe
 **Why this matters:** Combines mechanism design (egalitarian social choice) with ML (EM clustering). The insight about binary comparisons being insufficient is technically important — it explains why standard RLHF/DPO with pairwise comparisons systematically fails at diversity.
 **What surprised me:** The binary-vs-ranking distinction. If binary comparisons can't identify latent preferences, then ALL existing pairwise RLHF/DPO deployments are structurally blind to preference diversity. This is a fundamental limitation, not just a practical one.
 **What I expected but didn't find:** No head-to-head comparison with PAL or MixDPO. No deployment results beyond benchmarks.
-**KB connections:** Addresses [[RLHF and DPO both fail at preference diversity]] with a specific mechanism. The egalitarian aggregation connects to [[some disagreements are permanently irreducible because they stem from genuine value differences not information gaps]].
+**KB connections:** Addresses RLHF and DPO both fail at preference diversity with a specific mechanism. The egalitarian aggregation connects to some disagreements are permanently irreducible because they stem from genuine value differences not information gaps.
 **Extraction hints:** Extract claims about: (1) binary comparisons being formally insufficient for preference identification, (2) EM-based preference type discovery, (3) egalitarian aggregation as pluralistic deployment strategy.
 **Context:** EAAMO 2025 — Equity and Access in Algorithms, Mechanisms, and Optimization. The fairness focus distinguishes this from PAL's efficiency focus.
 
@@ -39,3 +43,10 @@ EM-DPO uses expectation-maximization to simultaneously uncover latent user prefe
 PRIMARY CONNECTION: RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values
 WHY ARCHIVED: The binary-comparison insufficiency claim is a novel formal result that strengthens the case against standard alignment approaches
 EXTRACTION HINT: Focus on the formal insufficiency of binary comparisons and the EM + egalitarian aggregation combination
+
+
+## Key Facts
+- EM-DPO presented at EAAMO 2025 (Equity and Access in Algorithms, Mechanisms, and Optimization)
+- EM-DPO uses rankings over 3+ responses rather than binary comparisons for preference data
+- MinMax Regret Aggregation is based on egalitarian social choice theory
+- The paper focuses on fairness rather than efficiency, distinguishing it from PAL's approach