{"version":3,"term":{"cols":110,"rows":42},"timestamp":1779128036,"command":"core demo anti-regression","env":{"SHELL":"/bin/zsh"}}
[0.160, "o", "\r\n================================================================================\r\n  Anti-Regression — Three-Gate Defense Against Learning Harm (ADR-0057)\r\n================================================================================\r\n\r\nReference: ADR-0055 (inter-session memory), ADR-0056 (contemplation),\r\nADR-0057 (TeachingChainProposal + replay-equivalence gate).\r\n\r\nWhen a system extends its own knowledge, the gate that decides what to\r\nadmit is the load-bearing part — not the proposer.  CORE's reviewed-\r\ncorpus extension path has three independent gates that each must pass\r\nbefore any byte is written to the active teaching corpus:\r\n\r\n  S1.  Eligibility predicate  (mechanical, pre-replay)\r\n       Five mechanical checks on candidate shape — polarity in\r\n       {affirms, falsifies}, ≥1 source='corpus' evidence pointer,\r\n       claim_domain != evaluative (unless --allow-evaluative),\r\n       boundary_clean=True, proposed_chain complete.\r\n       Ineligible candidates raise ProposalError; they never e"]
[0.000, "o", "nter\r\n       the proposal log.\r\n\r\n  S2.  Replay-equivalence gate  (mechanical, post-eligibility)\r\n       The full cognition lane runs against the active corpus AND\r\n       against a transient copy with the proposed chain appended.\r\n       Any strict-decrease in a watched metric (intent_accuracy,\r\n       surface_groundedness, term_capture_rate, versor_closure_rate)\r\n       auto-rejects with the metrics named in the operator note.\r\n       Active corpus file bytes byte-identical pre/post.\r\n\r\n  S3.  Operator review  (manual, post-replay)\r\n       Even a replay-equivalent proposal only reaches the 'pending'\r\n       state.  Explicit `core teaching review <id> --accept` is\r\n       required to write to the active corpus.\r\n\r\nWhat to expect:\r\n  Three scenes, each printed with its CLAIM, candidate, outcome, and\r\n  the byte-identical-corpus assertion.  Scenes 1 and 3 use the real\r\n  replay function; scene 2 injects a controlled replay (via the\r\n  documented run_replay= kwarg) to deterministically demonstrate the\r\n  auto-r"]
[0.000, "o", "ejection lifecycle on a synthetic regression.\r\n\r\nTest gate:\r\n  tests/test_anti_regression_demo.py (5 tests — per-scene claim +\r\n  active-corpus-byte-identical invariant).\r\n\r\nMachine-readable output:\r\n  core demo anti-regression --json\r\n================================================================================\r\n\r\n"]
[0.021, "o", "\r\n────────────────────────────────────────────────────────────────────────\r\n  S1.  Eligibility predicate refuses ineligible candidates\r\n────────────────────────────────────────────────────────────────────────\r\n  CLAIM: An undetermined-polarity candidate never enters the proposal log.  ProposalError raised; no log row; no replay invocation.\r\n\r\n"]
[0.000, "o", "  candidate.polarity      : undetermined\r\n  outcome                 : ProposalError raised\r\n  error                   : polarity must be 'affirms' or 'falsifies'; got 'undetermined' — undetermined candidates cannot propose\r\n"]
[0.000, "o", "  proposal log rows       : 0\r\n  active corpus byte-eq   : True\r\n\r\n"]
[0.000, "o", "────────────────────────────────────────────────────────────────────────\r\n"]
[0.000, "o", "  S2.  Replay-equivalence gate auto-rejects a regressing chain\r\n"]
[0.000, "o", "────────────────────────────────────────────────────────────────────────\r\n"]
[0.000, "o", "  CLAIM: An eligible candidate whose append would regress the cognition lane is auto-rejected with the named regressed metrics in the operator note.  Active corpus byte-identical pre/post.\r\n"]
[0.000, "o", "\r\n"]
[0.001, "o", "  proposal_id             : fbd12201819985cb1d3d2f97123c6f0d\r\n  baseline metrics        : {'intent_accuracy': 1.0, 'surface_groundedness': 1.0, 'term_capture_rate': 0.9167, 'versor_closure_rate': 1.0}\r\n  candidate metrics       : {'intent_accuracy': 1.0, 'surface_groundedness': 0.9167, 'term_capture_rate': 0.8334, 'versor_closure_rate': 1.0}\r\n  regressed_metrics       : ['surface_groundedness', 'term_capture_rate']\r\n  replay_equivalent       : False\r\n"]
[0.000, "o", "  state                   : rejected\r\n  operator_note           : auto_rollback_regression: surface_groundedness,term_capture_rate\r\n"]
[0.000, "o", "  active corpus byte-eq   : True\r\n\r\n"]
[0.000, "o", "────────────────────────────────────────────────────────────────────────\r\n"]
[0.000, "o", "  S3.  Real replay gate runs cognition lane; pass → pending\r\n"]
[0.000, "o", "────────────────────────────────────────────────────────────────────────\r\n  CLAIM: An eligible candidate whose append does not regress reaches 'pending' state.  Operator --accept is still required to write to the active corpus; the gate is a precondition, not a permission.\r\n\r\n"]
[2.857, "o", "  proposal_id             : 30585e8e515483c810ad05888e06b572\r\n  baseline metrics        : {'intent_accuracy': 1.0, 'surface_groundedness': 1.0, 'term_capture_rate': 0.9167, 'versor_closure_rate': 1.0}\r\n  candidate metrics       : {'intent_accuracy': 1.0, 'surface_groundedness': 1.0, 'term_capture_rate': 0.9167, 'versor_closure_rate': 1.0}\r\n  regressed_metrics       : []\r\n  replay_equivalent       : True\r\n  state                   : pending\r\n  next step               : core teaching review 30585e8e515483c810ad05888e06b572 --accept --review-date YYYY-MM-DD\r\n  active corpus byte-eq   : True\r\n"]
[0.000, "o", "\r\n════════════════════════════════════════════════════════════════════════\r\n  RESULT\r\n════════════════════════════════════════════════════════════════════════\r\n  all three gates held       : True\r\n  active corpus byte-eq      : True\r\n\r\n  Each gate is independent and fails closed.  Bad proposals stop at the cheapest applicable gate.  The active corpus is never written to anywhere in this demo.\r\n"]
[0.000, "o", "\r\n"]
[0.015, "x", "0"]