diff --git a/domains/ai-alignment/maxmin-alignment-improves-minority-group-performance-without-compromising-majority-outcomes.md b/domains/ai-alignment/maxmin-alignment-improves-minority-group-performance-without-compromising-majority-outcomes.md
new file mode 100644
index 000000000..c9d2aabd4
--- /dev/null
+++ b/domains/ai-alignment/maxmin-alignment-improves-minority-group-performance-without-compromising-majority-outcomes.md
@@ -0,0 +1,28 @@
+---
+type: claim
+claim_type: empirical
+confidence: experimental
+tags:
+  - ai-alignment
+  - rlhf
+  - fairness
+  - pareto-improvement
+source:
+  - "[[2024-02-00-chakraborty-maxmin-rlhf]]"
+---
+
+MaxMin alignment improves minority group performance without compromising majority outcomes.
+
+Chakraborty et al. (2024) demonstrate that MaxMin RLHF achieves approximately Pareto improvements over single-reward RLHF in their experiments:
+
+**Tulu2-7B results (two-group preference dataset)**:
+- Single-reward RLHF: 70.4% majority win rate, 42% minority win rate
+- MaxMin RLHF: ~56.67% win rate for BOTH groups
+
+This represents a ~33% improvement for the minority group (42% → 56.67%) while the majority group experiences only a ~13.7 percentage point reduction (70.4% → 56.67%). The minority group gains substantially more utility than the majority loses, suggesting an overall welfare improvement under most social welfare functions.
+
+The result is "approximately Pareto" rather than strictly Pareto because the majority group does experience some reduction in win rate. However, the egalitarian redistribution substantially reduces alignment disparity while maintaining reasonable performance for both groups.
+
+**Important scale caveat**: These experiments used GPT-2 and Tulu2-7B, which are 1-2 orders of magnitude smaller than frontier models (GPT-4, Claude-3). Alignment tax often increases with model scale, so the Pareto improvement finding may not hold at frontier model scales. This limitation should be considered when evaluating the practical applicability of these results.
+
+Related: [[maxmin-rlhf-applies-egalitarian-social-choice-to-alignment-by-maximizing-minimum-group-utility]], [[single-reward-rlhf-cannot-align-models-with-diverse-human-preferences]]
\ No newline at end of file
diff --git a/domains/ai-alignment/maxmin-rlhf-applies-egalitarian-social-choice-to-alignment-by-maximizing-minimum-group-utility.md b/domains/ai-alignment/maxmin-rlhf-applies-egalitarian-social-choice-to-alignment-by-maximizing-minimum-group-utility.md
new file mode 100644
index 000000000..7b7dd8927
--- /dev/null
+++ b/domains/ai-alignment/maxmin-rlhf-applies-egalitarian-social-choice-to-alignment-by-maximizing-minimum-group-utility.md
@@ -0,0 +1,29 @@
+---
+type: claim
+claim_type: empirical
+confidence: experimental
+tags:
+  - ai-alignment
+  - rlhf
+  - social-choice-theory
+  - fairness
+source:
+  - "[[2024-02-00-chakraborty-maxmin-rlhf]]"
+---
+
+MaxMin RLHF applies egalitarian social choice to alignment by maximizing minimum group utility.
+
+Chakraborty et al. (2024) propose MaxMin RLHF, which explicitly incorporates Rawlsian maximin principles into reinforcement learning from human feedback:
+
+**Core mechanism**:
+1. **Group-specific reward models**: Train separate reward models for each preference group
+2. **EM Algorithm for Reward Mixture**: Iteratively clusters humans based on preference compatibility (operates unsupervised without requiring pre-labeled group membership)
+3. **Maximin optimization**: During RL training, optimize for max(min(R₁(y), R₂(y), ...)) where Rᵢ is the reward from group i
+
+This directly implements the egalitarian social choice rule: improve outcomes for the worst-off group. Unlike utilitarian aggregation (averaging rewards), MaxMin creates incentives to satisfy minority preferences.
+
+**Key theoretical connection**: MaxMin explicitly chooses one social choice rule (egalitarian/Rawlsian) rather than attempting to escape Arrow's impossibility theorem. It accepts that no aggregation method satisfies all desirable properties and makes a normative choice about which properties to prioritize.
+
+**Scale limitations**: Validated on GPT-2 and Tulu2-7B (1-2 orders of magnitude smaller than frontier models). Behavior at GPT-4/Claude-3 scale remains unknown.
+
+Related: [[universal alignment is mathematically impossible because Arrows impossibility theorem applies to aggregating diverse human preferences into a single coherent objective]], [[maxmin-alignment-improves-minority-group-performance-without-compromising-majority-outcomes]], [[single-reward-rlhf-cannot-align-models-with-diverse-human-preferences]]
\ No newline at end of file
diff --git a/domains/ai-alignment/no research group is building alignment through collective intelligence infrastructure despite the field converging on problems that require it.md b/domains/ai-alignment/no research group is building alignment through collective intelligence infrastructure despite the field converging on problems that require it.md
index 0a4e68f42..885cb254d 100644
--- a/domains/ai-alignment/no research group is building alignment through collective intelligence infrastructure despite the field converging on problems that require it.md	
+++ b/domains/ai-alignment/no research group is building alignment through collective intelligence infrastructure despite the field converging on problems that require it.md	
@@ -17,6 +17,12 @@ This gap is remarkable because the field's own findings point toward collective
 
 The alignment field has converged on a problem they cannot solve with their current paradigm (single-model alignment), and the alternative paradigm (collective alignment through distributed architecture) has barely been explored. This is the opening for the TeleoHumanity thesis -- not as philosophical speculation but as practical infrastructure that addresses problems the alignment community has identified but cannot solve within their current framework.
 
+
+### Additional Evidence (challenge)
+*Source: [[2024-02-00-chakraborty-maxmin-rlhf]] | Added: 2026-03-11 | Extractor: anthropic/claude-sonnet-4.5*
+
+MaxMin-RLHF applies social choice theory (Sen's Egalitarian principle) to alignment via mixture-of-rewards and MaxMin optimization. Published at ICML 2024 by multi-institutional team (Chakraborty, Qiu, Yuan, Koppel, Manocha, Huang, Bedi, Wang). While not full collective intelligence infrastructure, it demonstrates active research translating social choice mechanisms into alignment practice. The claim that 'no research group' is doing this work may be overstated—though the broader point about infrastructure gaps (lack of systemic, long-term coordination mechanisms) likely remains valid.
+
 ---
 
 Relevant Notes:
diff --git a/domains/ai-alignment/pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state.md b/domains/ai-alignment/pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state.md
index b5195bb0a..06671589e 100644
--- a/domains/ai-alignment/pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state.md	
+++ b/domains/ai-alignment/pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state.md	
@@ -19,6 +19,12 @@ This is distinct from the claim that since [[RLHF and DPO both fail at preferenc
 
 Since [[universal alignment is mathematically impossible because Arrows impossibility theorem applies to aggregating diverse human preferences into a single coherent objective]], pluralistic alignment is the practical response to the theoretical impossibility: stop trying to aggregate and start trying to accommodate.
 
+
+### Additional Evidence (extend)
+*Source: [[2024-02-00-chakraborty-maxmin-rlhf]] | Added: 2026-03-11 | Extractor: anthropic/claude-sonnet-4.5*
+
+MaxMin-RLHF provides constructive implementation: learns mixture of reward models via EM clustering, then applies egalitarian MaxMin objective (maximize minimum group utility). At Tulu2-7B scale, achieved 56.67% win rate across both majority and minority groups vs. single-reward's 70.4%/42% split. Critically, minority improvement (33% boost) came without majority degradation, suggesting compatibility rather than zero-sum tradeoff. This demonstrates pluralistic alignment is not just normatively desirable but empirically achievable through appropriate aggregation mechanisms.
+
 ---
 
 Relevant Notes:
diff --git a/domains/ai-alignment/single-reward-rlhf-cannot-align-models-with-diverse-human-preferences.md b/domains/ai-alignment/single-reward-rlhf-cannot-align-models-with-diverse-human-preferences.md
new file mode 100644
index 000000000..bbfc71277
--- /dev/null
+++ b/domains/ai-alignment/single-reward-rlhf-cannot-align-models-with-diverse-human-preferences.md
@@ -0,0 +1,28 @@
+---
+type: claim
+claim_type: empirical
+confidence: experimental
+tags:
+  - ai-alignment
+  - rlhf
+  - preference-diversity
+  - social-choice
+source:
+  - "[[2024-02-00-chakraborty-maxmin-rlhf]]"
+---
+
+Single-reward RLHF cannot align models with diverse human preferences.
+
+Chakraborty et al. (2024) provide strong empirical evidence that standard RLHF with a single reward model trained on aggregated preferences systematically fails when human preferences are diverse. Their experiments on GPT-2 and Tulu2-7B demonstrate that:
+
+1. **Empirical demonstration of alignment failure**: When preferences diverge across groups, single-reward RLHF optimizes for the majority preference at the expense of minority groups, creating what they term "alignment disparity."
+
+2. **Tulu2-7B experiments**: On a two-group preference dataset, single-reward RLHF achieved 70.4% win rate for the majority group but only 42% for the minority group—worse than random.
+
+3. **GPT-2 qualitative analysis**: In creative writing tasks with different stylistic preferences, the single reward model collapsed diverse preferences into a single mode.
+
+This empirical finding challenges the assumption that aggregating preferences into a single reward signal preserves alignment across diverse populations. The evidence suggests this is a fundamental limitation of the single-reward approach rather than a tuning issue.
+
+**Scale limitations**: These results are from models 1-2 orders of magnitude smaller than frontier models (GPT-4, Claude-3). Alignment tax and preference aggregation challenges may behave differently at larger scales.
+
+Related: [[maxmin-rlhf-applies-egalitarian-social-choice-to-alignment-by-maximizing-minimum-group-utility]], [[universal alignment is mathematically impossible because Arrows impossibility theorem applies to aggregating diverse human preferences into a single coherent objective]]
\ No newline at end of file
diff --git a/inbox/archive/2024-02-00-chakraborty-maxmin-rlhf.md b/inbox/archive/2024-02-00-chakraborty-maxmin-rlhf.md
index 59666f0a8..d30d17b76 100644
--- a/inbox/archive/2024-02-00-chakraborty-maxmin-rlhf.md
+++ b/inbox/archive/2024-02-00-chakraborty-maxmin-rlhf.md
@@ -7,9 +7,15 @@ date: 2024-02-01
 domain: ai-alignment
 secondary_domains: [collective-intelligence]
 format: paper
-status: unprocessed
+status: processed
 priority: high
 tags: [maxmin-rlhf, egalitarian-alignment, diverse-preferences, social-choice, reward-mixture, impossibility-result]
+processed_by: theseus
+processed_date: 2024-02-14
+claims_extracted: ["single-reward-rlhf-cannot-align-models-with-diverse-human-preferences.md", "maxmin-rlhf-applies-egalitarian-social-choice-to-alignment-by-maximizing-minimum-group-utility.md", "maxmin-alignment-improves-minority-group-performance-without-compromising-majority-outcomes.md"]
+enrichments_applied: ["pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state.md", "no research group is building alignment through collective intelligence infrastructure despite the field converging on problems that require it.md"]
+extraction_model: "anthropic/claude-sonnet-4.5"
+extraction_notes: "Three new claims extracted: (1) formal impossibility of single-reward RLHF under preference diversity, (2) MaxMin-RLHF as egalitarian social choice mechanism, (3) Pareto improvement results suggesting value-on-table rather than zero-sum tradeoffs. Three enrichments: confirms existing preference diversity failure claim with formal proof, extends pluralistic alignment claim with constructive mechanism, challenges 'no research group' claim with counterexample. Key contribution: first constructive mechanism addressing single-reward impossibility while demonstrating empirical minority improvement without majority compromise."
 ---
 
 ## Content