auto-fix: address review feedback on PR #772

- Applied reviewer-requested changes
- Quality gate pass (fix-from-feedback)

Pentagon-Agent: Auto-Fix <HEADLESS>

domains/ai-alignment/agent-research-communities-may-explore-solution-spaces-more-effectively-than-single-agent-research-through-asynchronous-parallel-exploration.md

@@ -1,16 +0,0 @@
----
-type: claim
-domain: ai-alignment
-confidence: speculative
-description: Agent research communities may explore solution spaces more effectively than single-agent research through asynchronous parallel exploration.
-created: 2023-10-01
-source: [[2026-03-08-karpathy-autoresearch-collaborative-agents]]
-challenged_by: [[some-challenging-claim]]
----
-
-Agent research communities, by leveraging asynchronous parallel exploration, have the potential to explore solution spaces more effectively than single-agent research. This approach allows for diverse strategies and perspectives to be applied simultaneously, increasing the likelihood of discovering innovative solutions.
-
-## Related
-- [[Coordination Protocols Enhance Multi-Agent Collaboration]]
-- [[Multi-Model Collaboration Solves Complex Problems]]
-- [[Same Coordination Protocol Applied to Different AI Models]]

domains/ai-alignment/agent-research-communities-outperform-single-agent-research-through-asynchronous-massive-collaboration.md

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+---
+type: claim
+domain: ai-alignment
+confidence: speculative
+title: Agent research communities outperform single-agent research through asynchronous massive collaboration
+created: 2026-03-08
+processed_date: 2023-10-01
+source: Karpathy's tweet on collaborative agent research
+challenged_by: <!-- claim pending -->
+description: This claim suggests that research communities composed of multiple agents can achieve better results than individual agents by leveraging asynchronous and parallel exploration techniques.
+---
+Karpathy's insights into collaborative agent research propose that communities of agents can outperform single-agent efforts by exploring solution spaces more effectively through asynchronous and parallel methods. This approach allows for a broader and more diverse exploration of potential solutions, potentially leading to more innovative outcomes.

domains/ai-alignment/existing-coordination-abstractions-accumulate-stress-when-intelligence-and-attention-cease-to-be-bottlenecks.md

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----
-type: claim
-domain: ai-alignment
-confidence: speculative
-description: Existing coordination abstractions accumulate stress when intelligence and attention cease to be bottlenecks.
-created: 2023-10-01
-source: [[2026-03-08-karpathy-autoresearch-collaborative-agents]]
----
-
-As intelligence and attention become less of a bottleneck in AI systems, existing coordination abstractions, such as the git branch-merge model, may accumulate stress. This stress manifests in the form of inefficiencies and bottlenecks in decision-making processes, as these abstractions were not designed to handle the increased cognitive load and parallel processing capabilities of advanced AI systems. Specific limitations include the inability to effectively manage concurrent modifications and the increased complexity of merge conflicts.
-
-## Related
-- [[Coordination Protocols Enhance Multi-Agent Collaboration]]
-- [[Multi-Model Collaboration Solves Complex Problems]]
-- [[Same Coordination Protocol Applied to Different AI Models]]

domains/ai-alignment/git-branch-merge-model-insufficient-for-agent-scale-collaboration.md

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+---
+type: claim
+domain: ai-alignment
+confidence: speculative
+title: Git branch-merge model insufficient for agent-scale collaboration
+created: 2026-03-08
+processed_date: 2023-10-01
+source: Karpathy's tweet on collaborative agent research
+challenged_by: <!-- claim pending -->
+description: This claim highlights the limitations of the traditional git branch-merge model in the context of large-scale agent collaboration, where the assumption of a single master branch with temporary forks becomes a bottleneck.
+---
+Karpathy points out that the traditional git branch-merge model, which assumes a single master branch with temporary forks, is insufficient for large-scale agent collaboration. This model does not adequately support the concurrent and complex interactions required when multiple intelligent agents are involved, necessitating new coordination abstractions.