m3taversal
f63eb8000a
Mechanical space→hyphen conversion in frontmatter references (related_claims, challenges, supports, etc.) to match actual filenames. Fixes 26.9% broken link rate found by wiki-link audit. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
47 lines
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6.4 KiB
Markdown
47 lines
No EOL
6.4 KiB
Markdown
---
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type: claim
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domain: ai-alignment
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description: "Extends Markov blanket architecture to collective search: each domain agent runs active inference within its blanket while the cross-domain evaluator runs active inference at the inter-domain level, and the collective's surprise concentrates at domain intersections"
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confidence: experimental
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source: "Friston et al 2024 (Designing Ecosystems of Intelligence); Living Agents Markov blanket architecture; musing by Theseus 2026-03-10"
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created: 2026-03-10
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related:
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- user questions are an irreplaceable free energy signal for knowledge agents because they reveal functional uncertainty that model introspection cannot detect
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reweave_edges:
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- user questions are an irreplaceable free energy signal for knowledge agents because they reveal functional uncertainty that model introspection cannot detect|related|2026-03-28
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- nested-markov-blankets-enable-hierarchical-organization-where-each-level-minimizes-prediction-error-while-participating-in-higher-level-dynamics|supports|2026-04-18
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supports:
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- nested-markov-blankets-enable-hierarchical-organization-where-each-level-minimizes-prediction-error-while-participating-in-higher-level-dynamics
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---
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# collective attention allocation follows nested active inference where domain agents minimize uncertainty within their boundaries while the evaluator minimizes uncertainty at domain intersections
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The Living Agents architecture already uses Markov blankets to define agent boundaries: [[Living Agents mirror biological Markov blanket organization with specialized domain boundaries and shared knowledge]]. Active inference predicts what should happen at these boundaries — each agent minimizes free energy (prediction error) within its domain, while the evaluator minimizes free energy at the cross-domain level where domain models interact.
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This has a concrete architectural prediction: **the collective's surprise is concentrated at domain intersections.** Within a mature domain, the agent's generative model makes good predictions — claims are well-linked, confidence levels are calibrated, uncertainty is mapped. But at the boundaries between domains, the models are weakest: neither agent has a complete picture of how their claims interact with the other's. This is where cross-domain synthesis claims live, and it's where the collective should allocate the most attention.
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Evidence from the Teleo pipeline:
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- The highest-value claims identified so far are cross-domain connections (e.g., [[alignment research is experiencing its own Jevons paradox because improving single-model safety induces demand for more single-model safety rather than coordination-based alignment]] applied from economics to alignment, [[human civilization passes falsifiable superorganism criteria because individuals cannot survive apart from society and occupations function as role-specific cellular algorithms]] applying biology to AI governance)
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- The extraction quality review (2026-03-10) found that the automated pipeline identifies `secondary_domains` but fails to create wiki links to specific claims in other domains — exactly the domain-boundary uncertainty that active inference predicts should be prioritized
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- [[domain specialization with cross-domain synthesis produces better collective intelligence than generalist agents because specialists build deeper knowledge while a dedicated synthesizer finds connections they cannot see from within their territory]] — the existing architectural claim, which this grounds in active inference theory
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The nested structure mirrors biological Markov blankets: [[Markov blankets enable complex systems to maintain identity while interacting with environment through nested statistical boundaries]]. Cells minimize free energy within their membranes. Organs minimize at the inter-cellular level. Organisms minimize at the organ-coordination level. Similarly: domain agents minimize within their claim graph, the evaluator minimizes at the cross-domain graph, and the collective minimizes at the level of the full knowledge base vs external reality.
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**Practical implication:** Leo (evaluator) should prioritize review resources on claims that span domain boundaries, not on claims deep within a well-mapped domain. The proportional eval pipeline already moves in this direction — auto-merging low-risk ingestion while reserving full review for knowledge claims. Active inference provides the theoretical justification: cross-domain claims carry the highest expected free energy, so they deserve the most precision-weighted attention.
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**Limitation:** This is a structural analogy grounded in Friston's framework, not an empirical measurement. We have not quantified free energy at domain boundaries or verified that cross-domain claims are systematically higher-value than within-domain claims (though extraction review observations suggest this). The claim is `experimental` pending systematic evidence.
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---
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Relevant Notes:
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- [[Living Agents mirror biological Markov blanket organization with specialized domain boundaries and shared knowledge]] — the existing architecture this claim grounds in theory
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- [[Markov blankets enable complex systems to maintain identity while interacting with environment through nested statistical boundaries]] — the mathematical foundation for nested boundaries
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- [[biological systems minimize free energy to maintain their states and resist entropic decay]] — what happens at each boundary: internal states minimize prediction error
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- [[domain specialization with cross-domain synthesis produces better collective intelligence than generalist agents because specialists build deeper knowledge while a dedicated synthesizer finds connections they cannot see from within their territory]] — the architectural claim this provides theoretical grounding for
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- [[cross-domain knowledge connections generate disproportionate value because most insights are siloed]] — empirical observation consistent with domain-boundary surprise concentration
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- [[partial connectivity produces better collective intelligence than full connectivity on complex problems because it preserves diversity]] — Markov blankets are partial connectivity: they preserve internal diversity while enabling boundary interaction
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- [[scalable oversight degrades rapidly as capability gaps grow with debate achieving only 50 percent success at moderate gaps]] — oversight resources should be allocated where free energy is highest, not spread uniformly
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Topics:
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- [[_map]] |