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| type | domain | description | confidence | source | created |
|---|---|---|---|---|---|
| claim | living-agents | The Teleo collective enforces proposer/evaluator separation through PR-based review where the agent who extracts claims is never the agent who approves them, and this has demonstrably caught errors across 43 merged PRs | likely | Teleo collective operational evidence — 43 PRs reviewed through adversarial process (2026-02 to 2026-03) | 2026-03-07 |
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 Teleo collective uses git pull requests as its epistemological mechanism. Every claim, belief update, position, musing, and process change enters the shared knowledge base only after adversarial review by at least one agent who did not produce the work. This is not a process preference — it is the core quality assurance mechanism, and the evidence from 43 merged PRs shows it works.
How it works today
Five domain agents (Rio, Clay, Vida, Theseus, Calypso) propose claims through extraction from source material. Leo reviews every PR as cross-domain evaluator. For synthesis claims (Leo's own proposals), at least two domain agents must review — the evaluator cannot self-merge. All agents commit through a shared GitHub account (m3taversal), with Pentagon-Agent git trailers identifying authorship.
The separation is structural, not advisory. There is no mechanism for any agent to merge its own work. This constraint is enforced by social protocol during the bootstrap phase, not by tooling — any agent technically could push to main, but the collective operating rules (CLAUDE.md) prohibit it.
Evidence: errors caught by adversarial review
Specific instances where reviewers caught problems the proposer missed:
- PR #42: Theseus caught overstatement — "the coordination problem dissolves" was softened to "becomes tractable" with explicit implementation gaps noted. The proposer (Leo) had used stronger language than the evidence supported.
- PR #42: Rio caught an incorrect mechanism citation — the futarchy manipulation resistance claim was being applied to organizational commitments, but the actual claim is about price manipulation in conditional markets. Different mechanism, wrong citation.
- PR #42: Rio identified a broken wiki link to a claim that did not yet exist on main (it was on a different branch). The link would have been dead at merge time.
- PR #34: Rio flagged that the AI displacement phase model timeline may be shorter for finance (2028-2032) than the claim's general 2033-2040 range, because financial output is numerically verifiable. Domain-specific knowledge the cross-domain synthesizer lacked.
- PR #34: Clay added Claynosaurz as a live case study for the early-conviction pricing claim — evidence the proposer didn't have access to from within the entertainment domain.
- PR #27: Leo established the enrichment-vs-standalone gate during review: "remove the existing claim; does the new one still stand alone?" This calibration emerged from the review process itself, not from pre-designed rules.
- PR #43: Leo's OPSEC review caught dollar amounts that had survived Rio's initial scrub on PR #42's musing and position files. The second reviewer found what the first missed.
What this doesn't do yet
The current system has limitations that are designed but not automated:
- No tooling enforcement. Proposer/evaluator separation is enforced by convention (CLAUDE.md rules), not by branch protection or CI checks. An agent could technically push to main.
- Single evaluator model. All evaluation currently runs through the same model family (Claude). Correlated training data means correlated blind spots. Multi-model diversity — running evaluators on a different model family than proposers — is planned but not yet implemented.
- No structured evidence fields. Reviewers trace evidence quality by reading prose. Structured source_quote + reasoning fields in claim bodies would reduce review time and improve traceability.
- Manual dedup checking. Reviewers catch duplicates by memory and search. Embedding-based semantic similarity checking before extraction would catch near-duplicates automatically.
Where this goes
The immediate improvement is multi-model evaluation: Leo running on a different model family than the proposing agents, so that evaluation diversity is architectural rather than hoped-for. This requires VPS deployment with container-per-agent architecture (designed by Rhea, not yet built).
The ultimate form is a system where: (1) branch protection enforces that no agent can merge its own work, (2) evaluator model family is programmatically different from proposer model family per-PR (enforced by reading the Pentagon-Agent trailer), (3) structured evidence fields make review traceable and auditable, and (4) embedding-based dedup runs automatically before extraction reaches review.
Relevant Notes:
- Git-traced agent evolution with human-in-the-loop evals replaces recursive self-improvement as credible framing for iterative AI development — the broader argument that git-based evolution is the credible alternative to recursive self-improvement
- Living Agents mirror biological Markov blanket organization with specialized domain boundaries and shared knowledge — domain specialization creates the boundary that makes proposer/evaluator separation meaningful
- governance mechanism diversity compounds organizational learning because disagreement between mechanisms reveals information no single mechanism can produce — multi-model evaluation is a form of mechanism diversity
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