Merge branch 'main' into extract/2025-06-00-li-scaling-human-judgment-community-notes-llms

Pipeline auto-fixer: removed [[ ]] brackets from links
that don't resolve to existing claims in the knowledge base.

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

@@ -27,6 +27,12 @@ This claim directly addresses the mechanism gap identified in [[RLHF and DPO bot
 
 The paper's proposed solution—RLCHF with explicit social welfare functions—connects to [[collective intelligence requires diversity as a structural precondition not a moral preference]] by formalizing how diverse evaluator input should be preserved rather than collapsed.
 
+
+### Additional Evidence (extend)
+*Source: [[2025-06-00-li-scaling-human-judgment-community-notes-llms]] | Added: 2026-03-15*
+
+RLCF makes the social choice function explicit by separating generation (AI), evaluation (humans), and aggregation (bridging algorithm). Unlike RLHF where the reward model implicitly aggregates preferences during training, RLCF's bridging algorithm is a visible, auditable mechanism for combining diverse ratings. The matrix factorization approach (y_ij = w_i * x_j + b_i + c_j) makes the aggregation rule transparent: notes surface based on intercept scores that capture cross-partisan agreement. This architectural transparency enables normative scrutiny that RLHF's black-box reward models prevent.
+
 ---
 
 Relevant Notes:

inbox/archive/.extraction-debug/2025-06-00-li-scaling-human-judgment-community-notes-llms.json

@@ -0,0 +1,49 @@
+{
+  "rejected_claims": [
+    {
+      "filename": "rlcf-architecture-separates-ai-generation-from-human-evaluation-with-bridging-selection.md",
+      "issues": [
+        "missing_attribution_extractor"
+      ]
+    },
+    {
+      "filename": "bridging-based-consensus-mechanisms-risk-homogenization-toward-optimally-inoffensive-outputs.md",
+      "issues": [
+        "missing_attribution_extractor"
+      ]
+    },
+    {
+      "filename": "human-rating-authority-as-alignment-mechanism-preserves-judgment-sovereignty-while-scaling-content-generation.md",
+      "issues": [
+        "missing_attribution_extractor"
+      ]
+    }
+  ],
+  "validation_stats": {
+    "total": 3,
+    "kept": 0,
+    "fixed": 12,
+    "rejected": 3,
+    "fixes_applied": [
+      "rlcf-architecture-separates-ai-generation-from-human-evaluation-with-bridging-selection.md:set_created:2026-03-15",
+      "rlcf-architecture-separates-ai-generation-from-human-evaluation-with-bridging-selection.md:stripped_wiki_link:democratic-alignment-assemblies-produce-constitutions-as-eff",
+      "rlcf-architecture-separates-ai-generation-from-human-evaluation-with-bridging-selection.md:stripped_wiki_link:community-centred-norm-elicitation-surfaces-alignment-target",
+      "rlcf-architecture-separates-ai-generation-from-human-evaluation-with-bridging-selection.md:stripped_wiki_link:rlhf-is-implicit-social-choice-without-normative-scrutiny.md",
+      "bridging-based-consensus-mechanisms-risk-homogenization-toward-optimally-inoffensive-outputs.md:set_created:2026-03-15",
+      "bridging-based-consensus-mechanisms-risk-homogenization-toward-optimally-inoffensive-outputs.md:stripped_wiki_link:universal-alignment-is-mathematically-impossible-because-Arr",
+      "bridging-based-consensus-mechanisms-risk-homogenization-toward-optimally-inoffensive-outputs.md:stripped_wiki_link:pluralistic-alignment-must-accommodate-irreducibly-diverse-v",
+      "bridging-based-consensus-mechanisms-risk-homogenization-toward-optimally-inoffensive-outputs.md:stripped_wiki_link:some-disagreements-are-permanently-irreducible-because-they-",
+      "human-rating-authority-as-alignment-mechanism-preserves-judgment-sovereignty-while-scaling-content-generation.md:set_created:2026-03-15",
+      "human-rating-authority-as-alignment-mechanism-preserves-judgment-sovereignty-while-scaling-content-generation.md:stripped_wiki_link:human-in-the-loop-at-the-architectural-level-means-humans-se",
+      "human-rating-authority-as-alignment-mechanism-preserves-judgment-sovereignty-while-scaling-content-generation.md:stripped_wiki_link:coding-agents-cannot-take-accountability-for-mistakes-which-",
+      "human-rating-authority-as-alignment-mechanism-preserves-judgment-sovereignty-while-scaling-content-generation.md:stripped_wiki_link:economic-forces-push-humans-out-of-every-cognitive-loop-wher"
+    ],
+    "rejections": [
+      "rlcf-architecture-separates-ai-generation-from-human-evaluation-with-bridging-selection.md:missing_attribution_extractor",
+      "bridging-based-consensus-mechanisms-risk-homogenization-toward-optimally-inoffensive-outputs.md:missing_attribution_extractor",
+      "human-rating-authority-as-alignment-mechanism-preserves-judgment-sovereignty-while-scaling-content-generation.md:missing_attribution_extractor"
+    ]
+  },
+  "model": "anthropic/claude-sonnet-4.5",
+  "date": "2026-03-15"
+}

inbox/archive/2025-06-00-li-scaling-human-judgment-community-notes-llms.md

@@ -7,9 +7,13 @@ date: 2025-06-30
 domain: ai-alignment
 secondary_domains: [collective-intelligence]
 format: paper
-status: unprocessed
+status: enrichment
 priority: high
 tags: [RLCF, community-notes, bridging-algorithm, pluralistic-alignment, human-AI-collaboration, LLM-alignment]
+processed_by: theseus
+processed_date: 2026-03-15
+enrichments_applied: ["rlhf-is-implicit-social-choice-without-normative-scrutiny.md"]
+extraction_model: "anthropic/claude-sonnet-4.5"
 ---
 
 ## Content
@@ -43,7 +47,7 @@ Proposes a hybrid model for Community Notes where both humans and LLMs write not
 **Why this matters:** This is the most concrete RLCF specification that exists. It bridges Audrey Tang's philosophical framework with an implementable mechanism. The key insight: RLCF is not just a reward signal — it's an architecture where AI generates and humans evaluate, with a bridging algorithm ensuring pluralistic selection.
 **What surprised me:** The "helpfulness hacking" and "optimally inoffensive" risks are exactly what Arrow's theorem predicts. The paper acknowledges these but doesn't connect them to Arrow formally.
 **What I expected but didn't find:** No formal analysis of whether the bridging algorithm escapes Arrow's conditions. No comparison with PAL or other pluralistic mechanisms. No empirical results beyond Community Notes deployment.
-**KB connections:** Directly addresses the RLCF specification gap flagged in previous sessions. Connects to [[democratic alignment assemblies produce constitutions as effective as expert-designed ones]], [[community-centred norm elicitation surfaces alignment targets materially different from developer-specified rules]].
+**KB connections:** Directly addresses the RLCF specification gap flagged in previous sessions. Connects to democratic alignment assemblies produce constitutions as effective as expert-designed ones, [[community-centred norm elicitation surfaces alignment targets materially different from developer-specified rules]].
 **Extraction hints:** Extract claims about: (1) RLCF architecture (AI generates, humans rate, bridging selects), (2) the homogenization risk of bridging-based consensus, (3) human rating authority as alignment mechanism.
 **Context:** Core paper for the RLCF research thread. Fills the "technical specification" gap identified in sessions 2 and 3.
 
@@ -51,3 +55,11 @@ Proposes a hybrid model for Community Notes where both humans and LLMs write not
 PRIMARY CONNECTION: democratic alignment assemblies produce constitutions as effective as expert-designed ones while better representing diverse populations
 WHY ARCHIVED: First concrete specification of RLCF — transitions from design principle to implementable mechanism
 EXTRACTION HINT: Focus on the architecture (who generates, who rates, what selects) and the homogenization risk — the "optimally inoffensive" failure mode is a key tension with our bridging-based alignment thesis
+
+
+## Key Facts
+- Community Notes RLCF system uses matrix factorization: y_ij = w_i * x_j + b_i + c_j, where c_j is the bridging score
+- RLCF training uses predicted intercept scores as the reward signal
+- Stylistic novelty bonuses are added to bridging scores to prevent homogenization
+- Paper published in Journal of Online Trust and Safety, June 2025
+- Authors identify four key risks: helpfulness hacking, declining human engagement, homogenization, and rater capacity overwhelm