core/living-agents/agent-mediated knowledge bases are structurally novel because they combine atomic claims adversarial multi-agent evaluation and persistent knowledge graphs which Wikipedia Community Notes and prediction markets each partially implement but none combine.md

@@ -1,7 +1,7 @@
 ---
 type: claim
 domain: living-agents
-description: "Compares Teleo's architecture against Wikipedia, Community Notes, prediction markets, and Stack Overflow across three structural dimensions — showing that the combination of atomic claims, adversarial multi-agent evaluation, and persistent knowledge graphs is unprecedented"
+description: "Compares Teleo's architecture against Wikipedia, Community Notes, prediction markets, and Stack Overflow across three structural dimensions — atomic claims with independent evaluability, adversarial multi-agent evaluation with proposer/evaluator separation, and persistent knowledge graphs with semantic linking and cascade detection — showing no existing system combines all three"
 confidence: experimental
 source: "Theseus, original analysis grounded in CI literature and operational comparison of existing knowledge aggregation systems"
 created: 2026-03-11
@@ -42,5 +42,7 @@ Relevant Notes:
 - [[all agents running the same model family creates correlated blind spots that adversarial review cannot catch because the evaluator shares the proposers training biases]] — the known limitation of property #2 when model diversity is absent
 - [[protocol design enables emergent coordination of arbitrary complexity as Linux Bitcoin and Wikipedia demonstrate]] — prior art: protocol-based coordination systems that partially implement these properties
 
+- [[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 specialization architecture that makes adversarial evaluation between agents meaningful
+
 Topics:
 - [[core/living-agents/_map]]

domains/ai-alignment/transparent algorithmic governance where AI response rules are public and challengeable through the same epistemic process as the knowledge base is a structurally novel alignment approach.md

@@ -3,6 +3,7 @@ type: claim
 domain: ai-alignment
 description: "Argues that publishing how AI agents decide who and what to respond to — and letting users challenge and improve those rules through the same process that governs the knowledge base — is a fundamentally different alignment approach from hidden system prompts, RLHF, or Constitutional AI"
 confidence: experimental
+challenged_by: "Reflexive capture — users who game rules to increase influence can propose further rule changes benefiting themselves, analogous to regulatory capture. Agent evaluation as constitutional check is the proposed defense but is untested."
 source: "Theseus, original analysis building on Cory Abdalla's design principle for Teleo agent governance"
 created: 2026-03-11
 ---
@@ -50,5 +51,9 @@ Relevant Notes:
 - [[community-centred norm elicitation surfaces alignment targets materially different from developer-specified rules]] — evidence that user-surfaced norms differ from designer assumptions
 - [[adversarial PR review produces higher quality knowledge than self-review because separated proposer and evaluator roles catch errors that the originating agent cannot see]] — the adversarial review mechanism that governs rule changes
 
+- [[social enforcement of architectural rules degrades under tool pressure because automated systems that bypass conventions accumulate violations faster than review can catch them]] — the tension: transparent governance relies on social enforcement which this claim shows degrades under tool pressure
+- [[protocol design enables emergent coordination of arbitrary complexity as Linux Bitcoin and Wikipedia demonstrate]] — prior art for protocol-based governance producing emergent coordination
+- [[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 agent specialization that makes distributed evaluation meaningful
+
 Topics:
 - [[domains/ai-alignment/_map]]

foundations/collective-intelligence/adversarial contribution produces higher-quality collective knowledge than collaborative contribution when wrong challenges have real cost evaluation is structurally separated from contribution and confirmation is rewarded alongside novelty.md

@@ -1,7 +1,7 @@
 ---
 type: claim
 domain: collective-intelligence
-description: "Identifies three necessary conditions under which adversarial knowledge contribution ('tell us something we don't know') produces genuine collective intelligence rather than selecting for contrarianism or noise"
+description: "Identifies three necessary conditions under which adversarial knowledge contribution ('tell us something we don't know') produces genuine collective intelligence rather than selecting for contrarianism. Key reframe: the adversarial dynamic should be contributor vs. knowledge base, not contributor vs. contributor"
 confidence: experimental
 source: "Theseus, original analysis drawing on prediction market evidence, scientific peer review, and mechanism design theory"
 created: 2026-03-11
@@ -42,5 +42,9 @@ Relevant Notes:
 - [[partial connectivity produces better collective intelligence than full connectivity on complex problems because it preserves diversity]] — structural conditions under which diversity (and therefore adversarial input) matters most
 - [[confidence calibration with four levels enforces honest uncertainty because proven requires strong evidence while speculative explicitly signals theoretical status]] — the confidence system that operationalizes condition #1 (new claims enter at low confidence and must earn upgrades)
 
+- [[scalable oversight degrades rapidly as capability gaps grow with debate achieving only 50 percent success at moderate gaps]] — contrast case: adversarial debate between AI systems degrades at scale, while adversarial contribution between humans and a knowledge base may not face the same scaling constraint
+- [[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 structural context in which adversarial contribution operates
+- [[protocol design enables emergent coordination of arbitrary complexity as Linux Bitcoin and Wikipedia demonstrate]] — existence proofs of adversarial/competitive contribution producing collective intelligence at scale
+
 Topics:
 - [[foundations/collective-intelligence/_map]]