diff --git a/domains/ai-alignment/single-reward-rlhf-cannot-align-diverse-preferences-because-alignment-gap-grows-proportional-to-minority-distinctiveness.md b/domains/ai-alignment/single-reward-rlhf-cannot-align-diverse-preferences-because-alignment-gap-grows-proportional-to-minority-distinctiveness.md
index c6ab6f2b..13c17208 100644
--- a/domains/ai-alignment/single-reward-rlhf-cannot-align-diverse-preferences-because-alignment-gap-grows-proportional-to-minority-distinctiveness.md
+++ b/domains/ai-alignment/single-reward-rlhf-cannot-align-diverse-preferences-because-alignment-gap-grows-proportional-to-minority-distinctiveness.md
@@ -45,6 +45,12 @@ An & Du's survey reveals the mechanism behind single-reward failure: RLHF is doi
 
 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.
 
+
+### Additional Evidence (confirm)
+*Source: [[2025-11-00-sahoo-rlhf-alignment-trilemma]] | Added: 2026-03-16*
+
+Formal proof that preference collapse is theoretically inevitable: single-reward RLHF cannot capture multimodal preferences even in principle. The paper quantifies the practical gap: current systems use 10^3-10^4 samples from homogeneous pools while 10^7-10^8 samples are needed for global representation — a 3-4 order of magnitude shortfall that explains why minority alignment gaps grow with distinctiveness.
+
 ---
 
 Relevant Notes:
diff --git a/inbox/archive/.extraction-debug/2025-11-00-sahoo-rlhf-alignment-trilemma.json b/inbox/archive/.extraction-debug/2025-11-00-sahoo-rlhf-alignment-trilemma.json
new file mode 100644
index 00000000..a5ad6337
--- /dev/null
+++ b/inbox/archive/.extraction-debug/2025-11-00-sahoo-rlhf-alignment-trilemma.json
@@ -0,0 +1,36 @@
+{
+  "rejected_claims": [
+    {
+      "filename": "rlhf-alignment-trilemma-proves-no-system-can-simultaneously-achieve-representativeness-tractability-and-robustness.md",
+      "issues": [
+        "missing_attribution_extractor"
+      ]
+    },
+    {
+      "filename": "rlhf-pathologies-are-computational-necessities-not-implementation-bugs-because-preference-collapse-sycophancy-and-bias-amplification-follow-from-trilemma-constraints.md",
+      "issues": [
+        "missing_attribution_extractor"
+      ]
+    }
+  ],
+  "validation_stats": {
+    "total": 2,
+    "kept": 0,
+    "fixed": 6,
+    "rejected": 2,
+    "fixes_applied": [
+      "rlhf-alignment-trilemma-proves-no-system-can-simultaneously-achieve-representativeness-tractability-and-robustness.md:set_created:2026-03-16",
+      "rlhf-alignment-trilemma-proves-no-system-can-simultaneously-achieve-representativeness-tractability-and-robustness.md:stripped_wiki_link:universal-alignment-is-mathematically-impossible-because-Arr",
+      "rlhf-alignment-trilemma-proves-no-system-can-simultaneously-achieve-representativeness-tractability-and-robustness.md:stripped_wiki_link:single-reward-rlhf-cannot-align-diverse-preferences-because-",
+      "rlhf-pathologies-are-computational-necessities-not-implementation-bugs-because-preference-collapse-sycophancy-and-bias-amplification-follow-from-trilemma-constraints.md:set_created:2026-03-16",
+      "rlhf-pathologies-are-computational-necessities-not-implementation-bugs-because-preference-collapse-sycophancy-and-bias-amplification-follow-from-trilemma-constraints.md:stripped_wiki_link:emergent-misalignment-arises-naturally-from-reward-hacking-a",
+      "rlhf-pathologies-are-computational-necessities-not-implementation-bugs-because-preference-collapse-sycophancy-and-bias-amplification-follow-from-trilemma-constraints.md:stripped_wiki_link:single-reward-rlhf-cannot-align-diverse-preferences-because-"
+    ],
+    "rejections": [
+      "rlhf-alignment-trilemma-proves-no-system-can-simultaneously-achieve-representativeness-tractability-and-robustness.md:missing_attribution_extractor",
+      "rlhf-pathologies-are-computational-necessities-not-implementation-bugs-because-preference-collapse-sycophancy-and-bias-amplification-follow-from-trilemma-constraints.md:missing_attribution_extractor"
+    ]
+  },
+  "model": "anthropic/claude-sonnet-4.5",
+  "date": "2026-03-16"
+}
\ No newline at end of file
diff --git a/inbox/archive/2025-11-00-sahoo-rlhf-alignment-trilemma.md b/inbox/archive/2025-11-00-sahoo-rlhf-alignment-trilemma.md
index 17c59596..5362ec1e 100644
--- a/inbox/archive/2025-11-00-sahoo-rlhf-alignment-trilemma.md
+++ b/inbox/archive/2025-11-00-sahoo-rlhf-alignment-trilemma.md
@@ -7,9 +7,13 @@ date: 2025-11-01
 domain: ai-alignment
 secondary_domains: [collective-intelligence]
 format: paper
-status: unprocessed
+status: enrichment
 priority: high
 tags: [alignment-trilemma, impossibility-result, rlhf, representativeness, robustness, tractability, preference-collapse, sycophancy]
+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"]
+extraction_model: "anthropic/claude-sonnet-4.5"
 ---
 
 ## Content
@@ -56,3 +60,11 @@ Position paper from Berkeley AI Safety Initiative, AWS/Stanford, Meta/Stanford,
 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: Formalizes our informal impossibility claim with complexity-theoretic proof — independent confirmation of Arrow's-theorem-based argument from a different mathematical tradition
 EXTRACTION HINT: The trilemma is the key claim. Also extract the practical gap (10^3 vs 10^8) and the "pathologies as computational necessities" framing
+
+
+## Key Facts
+- Paper presented at NeurIPS 2025 Workshop on Socially Responsible and Trustworthy Foundation Models
+- Authors affiliated with Berkeley AI Safety Initiative, AWS, Stanford, Meta, and Northeastern
+- Current RLHF systems collect 10^3-10^4 samples from annotator pools
+- True global representation would require 10^7-10^8 samples
+- Bias amplification in current systems: models assign >99% probability to majority opinions