e7e28a2fd5
Two-session arc where engine derives connective+object from corpus decomposition; operator ratifies rather than authors. Distinguishes from learning-loop (operator-authored) and directly exercises W-018 checkpoint contemplation and W-017 auto-proposal provenance path.
98 lines
3.5 KiB
Python
98 lines
3.5 KiB
Python
"""Learning-arc demo — pins the headline claim for W-019/ADR-0151.
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If any assertion fails, the claim ("engine authored the proposal
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structure through autonomous contemplation; operator only ratified")
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no longer holds.
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Module-scoped fixture: one run_demo() invocation shared across all
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tests. Same pattern as test_learning_loop_demo.py — one worker pays
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the demo cost (~3-4s) once.
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"""
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from __future__ import annotations
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import pytest
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from evals.learning_arc.run_demo import run_demo
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@pytest.fixture(scope="module")
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def demo_report() -> dict:
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return run_demo(emit_json=True)
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def test_learning_arc_closes(demo_report: dict) -> None:
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assert demo_report["learning_arc_closed"] is True
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assert demo_report["all_claims_supported"] is True
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assert len(demo_report["scenes"]) == 5
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def test_active_corpus_untouched(demo_report: dict) -> None:
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assert demo_report["active_corpus_byte_identical"] is True
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def test_before_is_ungrounded(demo_report: dict) -> None:
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assert demo_report["before"]["grounding_source"] != "teaching"
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def test_after_is_teaching_grounded(demo_report: dict) -> None:
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assert demo_report["after"]["grounding_source"] == "teaching"
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def test_s1_cold_session_persists_candidate(demo_report: dict) -> None:
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s1 = demo_report["scenes"][0]
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assert s1["scene"] == "S1_cold_session"
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assert s1["detail"]["candidates_persisted"] >= 1
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assert s1["detail"]["grounding_source"] != "teaching"
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def test_s2_enrichment_has_engine_derived_chain(demo_report: dict) -> None:
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s2 = demo_report["scenes"][1]
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assert s2["scene"] == "S2_checkpoint_enrichment"
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assert s2["detail"]["engine_chain_found"] is True
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assert s2["detail"]["sub_questions_count"] > 0
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chain = s2["detail"]["engine_chain"]
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assert chain["connective"] == demo_report["engine_connective"]
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assert chain["object"] == demo_report["engine_object"]
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def test_s3_proposal_source_is_contemplation(demo_report: dict) -> None:
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s3 = demo_report["scenes"][2]
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assert s3["scene"] == "S3_engine_authored_proposal"
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assert s3["detail"]["source_kind"] == "contemplation"
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assert s3["detail"]["state"] == "pending"
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chain = s3["detail"]["proposed_chain"]
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assert chain["connective"] == demo_report["engine_connective"]
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assert chain["object"] == demo_report["engine_object"]
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def test_s3_replay_gate_passes(demo_report: dict) -> None:
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s3 = demo_report["scenes"][2]
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ev = s3["detail"]["replay_evidence"]
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assert ev["replay_equivalent"] is True
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assert ev["regressed_metrics"] == []
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def test_s4_corpus_byte_identical_after_accept(demo_report: dict) -> None:
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s4 = demo_report["scenes"][3]
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assert s4["scene"] == "S4_operator_ratifies"
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assert s4["detail"]["active_corpus_byte_identical"] is True
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assert s4["detail"]["transient_lines_after"] == s4["detail"]["transient_lines_before"] + 1
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def test_before_and_after_surfaces_differ(demo_report: dict) -> None:
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assert demo_report["before"]["surface"] != demo_report["after"]["surface"]
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def test_engine_connective_and_object_not_operator_provided(demo_report: dict) -> None:
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"""Connective+object in the proposal came from engine decomposition.
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The demo's _ENGINE_CONNECTIVE and _ENGINE_OBJECT constants are
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derived from _decompose() output, not hard-coded operator choices.
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S2 confirms engine_chain_found=True, proving the chain appeared
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in the autonomous decomposition set.
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"""
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s2 = demo_report["scenes"][1]
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assert s2["detail"]["engine_chain_found"] is True
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s3 = demo_report["scenes"][2]
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assert s3["detail"]["source_kind"] == "contemplation"
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