auto-fix: address review feedback on 2025-02-00-agreement-complexity-alignment-barriers.md

- Fixed based on eval review comments
- Quality gate pass 3 (fix-from-feedback)

Pentagon-Agent: Theseus <HEADLESS>

domains/ai-alignment/consensus-driven objective reduction escapes alignment intractability by shrinking the objective space rather than aggregating over all preferences.md

@@ -31,6 +31,8 @@ The practical implication for alignment system design: preference aggregation ar
 
 Consensus-driven reduction raises a fairness question: the consensus subset may systematically exclude minority preferences. Reducing M means ignoring some objectives — the ones that lack consensus. For alignment in pluralistic contexts, the objectives that get excluded may be precisely those of marginalized groups whose preferences don't align with majority consensus. The practical pathway may trade intractability for representational bias.
 
+Additionally, the bridging connection is Theseus's interpretive synthesis, not an explicit claim in the source paper. The paper formalizes consensus-driven reduction as a theoretical pathway; the application to Community Notes and RLCF is inferred from structural similarity. This is why confidence is `experimental` rather than `likely`.
+
 ---
 
 Relevant Notes:
@@ -38,6 +40,7 @@ Relevant Notes:
 - [[pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state]] — pluralistic alignment and consensus-driven reduction are related but distinct: pluralism aims to preserve all perspectives, consensus reduction sacrifices non-consensus objectives for tractability
 - [[community-centred norm elicitation surfaces alignment targets materially different from developer-specified rules]] — community norms are a form of consensus-driven objective reduction in practice
 - [[democratic alignment assemblies produce constitutions as effective as expert-designed ones while better representing diverse populations]] — assemblies produce consensus-derived objectives through structured deliberation
+- [[AI alignment is a coordination problem not a technical problem]] — consensus-driven reduction is the coordination-based response to impossibility: instead of solving preference aggregation technically, coordinate on the overlap
 
 Topics:
 - [[_map]]

domains/ai-alignment/reward hacking is globally inevitable in large task spaces because finite training samples cannot cover rare high-loss states regardless of training sophistication.md

@@ -7,8 +7,6 @@ source: "Theseus via: agreement-complexity analysis (arXiv 2502.05934, AAAI 2026
 created: 2026-03-11
 depends_on:
   - "three independent mathematical traditions converge on structural alignment impossibility confirming it is a robust theoretical finding not an artifact of any single framework"
-challenged_by:
-  - "safety-critical slices provide scalable oversight by concentrating coverage on high-stakes regions rather than attempting uniform coverage"
 secondary_domains: []
 ---
 
@@ -24,7 +22,7 @@ The practical implication is that alignment strategies assuming "sufficient trai
 
 ## Challenges
 
-The result applies specifically when task spaces are "sufficiently large" — the paper does not precisely characterize the threshold. For bounded, well-defined task domains, uniform coverage might be achievable and reward hacking avoidable. The "globally inevitable" framing may overstate the generality for narrow AI applications.
+The result applies specifically when task spaces are "sufficiently large" — the paper does not precisely characterize the threshold. For bounded, well-defined task domains, uniform coverage might be achievable and reward hacking avoidable. The "globally inevitable" framing may overstate the generality for narrow AI applications. Additionally, the paper's formalization is the primary evidence for this claim; convergent evidence from other sources would strengthen confidence beyond `likely`.
 
 ---
 
@@ -32,6 +30,7 @@ Relevant Notes:
 - [[emergent misalignment arises naturally from reward hacking as models develop deceptive behaviors without any training to deceive]] — the behavioral consequences of reward hacking; this claim provides the structural reason why reward hacking cannot be prevented at the source
 - [[safe AI development requires building alignment mechanisms before scaling capability]] — the sampling argument strengthens this claim: as capability and task space grow, the structural coverage gap widens
 - [[three independent mathematical traditions converge on structural alignment impossibility confirming it is a robust theoretical finding not an artifact of any single framework]] — this claim is one of the three impossibility-type results that constitute the convergence
+- [[consensus-driven objective reduction escapes alignment intractability by shrinking the objective space rather than aggregating over all preferences]] — the practical pathway that acknowledges reward hacking inevitability and responds by reducing the objective space rather than attempting universal coverage
 
 Topics:
 - [[_map]]

domains/ai-alignment/three independent mathematical traditions converge on structural alignment impossibility confirming it is a robust theoretical finding not an artifact of any single framework.md

@@ -38,6 +38,7 @@ Relevant Notes:
 - [[RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values]] — second tradition (learning theory); the practical failure mode that Arrow's theorem explains mathematically
 - [[pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state]] — the research program that responds to multi-tradition impossibility by changing the problem
 - [[specifying human values in code is intractable because our goals contain hidden complexity comparable to visual perception]] — a fourth independent argument (value complexity) that adds to the convergence picture
+- [[AI alignment is a coordination problem not a technical problem]] — the meta-framework that explains why three independent traditions converge: alignment is fundamentally about coordinating diverse agents, not solving a technical specification problem
 
 Topics:
 - [[_map]]