auto-fix: strip 6 broken wiki links

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

domains/ai-alignment/no research group is building alignment through collective intelligence infrastructure despite the field converging on problems that require it.md

@@ -19,7 +19,7 @@ The alignment field has converged on a problem they cannot solve with their curr
 
 
 ### Additional Evidence (challenge)
-*Source: [[2024-11-00-ai4ci-national-scale-collective-intelligence]] | Added: 2026-03-15 | Extractor: anthropic/claude-sonnet-4.5*
+*Source: 2024-11-00-ai4ci-national-scale-collective-intelligence | Added: 2026-03-15 | Extractor: anthropic/claude-sonnet-4.5*
 
 The UK AI for Collective Intelligence Research Network represents a national-scale institutional commitment to building CI infrastructure with explicit alignment goals. Funded by UKRI/EPSRC, the network proposes the 'AI4CI Loop' (Gathering Intelligence → Informing Behaviour) as a framework for multi-level decision making. The research strategy includes seven trust properties (human agency, security, privacy, transparency, fairness, value alignment, accountability) and specifies technical requirements including federated learning architectures, secure data repositories, and foundation models adapted for collective intelligence contexts. This is not purely academic—it's a government-backed infrastructure program with institutional resources. However, the strategy is prospective (published 2024-11) and describes a research agenda rather than deployed systems, so it represents institutional intent rather than operational infrastructure.
 
@@ -49,4 +49,4 @@ Relevant Notes:
 Topics:
 - [[livingip overview]]
 - [[coordination mechanisms]]
-- [[domains/ai-alignment/_map]]
+- domains/ai-alignment/_map

domains/ai-alignment/pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations.md

@@ -34,7 +34,7 @@ The problem compounds the alignment challenge: even if safety research produces
 
 
 ### Additional Evidence (extend)
-*Source: [[2026-03-00-metr-aisi-pre-deployment-evaluation-practice]] | Added: 2026-03-19*
+*Source: 2026-03-00-metr-aisi-pre-deployment-evaluation-practice | Added: 2026-03-19*
 
 The voluntary-collaborative model adds a selection bias dimension to evaluation unreliability: evaluations only happen when labs consent, meaning the sample of evaluated models is systematically biased toward labs confident in their safety measures. Labs with weaker safety practices can avoid evaluation entirely.
 
@@ -52,5 +52,5 @@ Relevant Notes:
 - [[the gap between theoretical AI capability and observed deployment is massive across all occupations because adoption lag not capability limits determines real-world impact]]
 
 Topics:
-- [[domains/ai-alignment/_map]]
-- [[core/grand-strategy/_map]]
+- domains/ai-alignment/_map
+- core/grand-strategy/_map

inbox/queue/2026-01-00-kim-third-party-ai-assurance-framework.md

@@ -44,7 +44,7 @@ CMU researchers propose a comprehensive third-party AI assurance framework with
 
 **KB connections:**
 - Directly relevant to the "missing correction mechanism" identified in Session 2026-03-18b — third-party performance measurement that is genuinely independent, not collaborative
-- [[no research group is building alignment through collective intelligence infrastructure]] — this paper is one of the first to try to build the assurance infrastructure, but at a small scale
+- no research group is building alignment through collective intelligence infrastructure — this paper is one of the first to try to build the assurance infrastructure, but at a small scale
 
 **Extraction hints:**
 - Could support a claim about the early stage of AI assurance methodology: "third-party AI assurance methodology is at the proof-of-concept stage, validated in small deployment contexts but not yet applicable to frontier AI at scale"