[
  {
    "claim_id": "P02-C1",
    "plain_claim": "Language agents can answer tasks correctly on first exposure while still failing to learn reliably from repeated failures.",
    "formula_or_protocol": "WisdomBench longitudinal protocol: WQ, GR, RFR, task/category/condition confidence intervals.",
    "scope": "Public benchmark setting with anonymized model-family reporting and controlled repeated rounds.",
    "does_not_claim": "Does not claim human-like wisdom, universal AGI, SOTA leaderboard dominance, or deployment safety.",
    "evidence_ids": ["E-P02-3600"],
    "attack_routes": ["stronger_baseline", "task_leakage", "scoring_bug", "claim_too_broad", "reproduction_failure"],
    "status": "public_preprint_and_tmlr_artifact_candidate"
  },
  {
    "claim_id": "PCA-C1",
    "plain_claim": "High-risk AI action should require a warrant and receipt closure before it earns action credit.",
    "formula_or_protocol": "Proof-carrying action loop: thesis -> falsifier -> warrant -> receipt -> regret -> clean learning.",
    "scope": "Public protocol, toy demos, and redacted high-risk action infrastructure summaries.",
    "does_not_claim": "Does not claim live-money profitability, deployment certification, or disclosure of private execution systems.",
    "evidence_ids": ["E-PCA-NOGO-001", "E-DEMO-RECEIPT-001"],
    "attack_routes": ["false_no_go", "missing_receipt", "credit_leak", "authority_leak", "stronger_action_policy"],
    "status": "public_protocol"
  },
  {
    "claim_id": "P24-C1",
    "plain_claim": "Adaptive intelligence needs relational observability: systems must observe changing relations, constraints, control debt, and evidence half-life rather than only scalar scores.",
    "formula_or_protocol": "Relational observability packet: relation graph, control-debt ledger, evidence half-life, actionability gate.",
    "scope": "Systems evidence protocol across language agents, finance testbeds, robot-safety simulations, and product workflows.",
    "does_not_claim": "Does not claim a universal law without independent real-world validation or third-party audits.",
    "evidence_ids": ["E-RO-PUBLIC-001"],
    "attack_routes": ["missing_relation", "unmeasured_control_debt", "half_life_miscalibration", "domain_transfer_failure"],
    "status": "research_track_candidate"
  },
  {
    "claim_id": "P20-C1",
    "plain_claim": "Safe physical AI should not convert detector confidence directly into action under adverse evidence conditions.",
    "formula_or_protocol": "Counter-reflexive evidence integrity: evidence state, partial-order hypotheses, gating, recovery, and abstention.",
    "scope": "Synthetic stress panels and public adverse-condition detector/tracker evidence panels.",
    "does_not_claim": "Does not claim detector SOTA, offensive inference, or real-robot deployment certification.",
    "evidence_ids": ["E-P20-SYNTH-001", "E-P20-PUBLIC-LOG-001"],
    "attack_routes": ["independent_dataset_failure", "baseline_underfit", "unrealistic_interference", "action_policy_underspecified"],
    "status": "rebuild_required_after_editorial_feedback"
  },
  {
    "claim_id": "F1-C1",
    "plain_claim": "Trading can serve as a high-risk testbed for proof-carrying action discipline without claiming live profitability.",
    "formula_or_protocol": "Proof-carrying cognitive action loop: warrant -> receipt closure -> no-credit repair -> regret attribution -> clean learning.",
    "scope": "Public finance-facing boundary documents and private briefing route; no private execution logs or live-money claims.",
    "does_not_claim": "Does not claim live trading edge, customer readiness, private execution quality, or alpha dominance.",
    "evidence_ids": ["E-F1-NOGO", "E-F1-SCHEMA"],
    "attack_routes": ["claim_boundary_too_broad", "missing_public_schema", "no_go_misinterpreted", "credit_leak"],
    "status": "public_boundary_and_private_briefing_route"
  }
]