395 lines
14 KiB
Python
395 lines
14 KiB
Python
"""Public-proof demo — one command that shows the math composition
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flywheel turn one revolution end-to-end on a clean pack.
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The thesis of the position paper is *decoding, not generating* — that
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cognition is the deterministic decoding of structure that already
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exists, and that the load-bearing invariant is `wrong == 0`.
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This demo executes a four-scene reproduction that any visitor can run
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after `git clone && uv pip install -e .`:
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Scene 1. BEFORE. On a clean pack with no composition ratification,
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"Maria bought 3 books at $5 each. How much did she pay?"
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REFUSES. The recognizer matches; the injector returns ();
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the candidate-graph refuses with a named reason.
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Scene 2. RATIFY. Operator submits one ratification:
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apply_composition_claim(
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claim=<MathReaderRefusalEvidence>,
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composition_category="multiplicative_composition",
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polarity="affirms",
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surface_pattern="bound(count) × bound(unit_cost)",
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reviewer="public_demo",
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)
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Followed by:
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core teaching seed-recognizer \\
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--shape-category rate_with_currency \\
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--anchor-kind currency_per_unit_composition \\
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--observed-currency-symbols '$' \\
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--observed-per-units each apiece
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Scene 3. AFTER. Same prompt now ADMITS with answer=15.
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Every transition between Scene 1 and Scene 3 is one of:
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- a reviewed JSONL append to compositions/{category}.jsonl
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- a reviewed proposal log append
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- the deterministic compile_pack step (RAT-1)
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No training, no gradient, no sampling.
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Scene 4. HAZARD. case 0050 ("Mark does a gig every other day for
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2 weeks") REMAINS REFUSED after ratification. The hazard
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pin (gsm8k-train-sample-v1-0050, the wrong=0 canary) is
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load-bearing. Architecturally, no composition admission
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under SAFE_COMPOSITION_CATEGORIES can convert this case
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from refused → wrong. Verified live.
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All four scenes are byte-deterministic. Re-running the demo on the
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same git revision produces the same outputs. The state mutation
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(scene 2) is contained to a synthetic test pack in a temporary
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directory; the canonical pack is read-only throughout.
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"""
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from __future__ import annotations
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import hashlib
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import json
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import shutil
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import tempfile
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from contextlib import contextmanager
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from dataclasses import asdict, dataclass
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from pathlib import Path
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from typing import Any, Iterator
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CANARY_PROMPT = (
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"Lilibeth fills 6 baskets where each basket holds 50 strawberries. "
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"How many strawberries does Lilibeth have?"
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)
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EXPECTED_ANSWER = 300
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CASE_0050_PROMPT = (
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"Mark does a gig every other day for 2 weeks. He gets paid $50 per gig. "
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"He then gets a 50% raise. How much money does he make per week?"
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)
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CANARY_COMPOSITION_SHAPE = "bound(outer_count) × bound(per_outer_count)"
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CANARY_OBSERVED_UNITS = [
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"strawberries", "strawberry", "baskets", "basket",
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"ounces", "ounce", "apples", "apple", "books", "book",
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]
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@dataclass(frozen=True, slots=True)
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class SceneResult:
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name: str
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expected: str
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actual: str
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passed: bool
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detail: str = ""
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@dataclass(frozen=True, slots=True)
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class FlywheelDemoResult:
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scenes: tuple[SceneResult, ...]
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@property
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def all_passed(self) -> bool:
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return all(s.passed for s in self.scenes)
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def as_dict(self) -> dict[str, Any]:
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return {
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"all_passed": self.all_passed,
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"scenes": [asdict(s) for s in self.scenes],
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}
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@contextmanager
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def _isolated_pack() -> Iterator[Path]:
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"""Clone the canonical en_core_math_v1 into a tempdir for read+write."""
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repo_root = Path(__file__).resolve()
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while repo_root.parent != repo_root and not (repo_root / "pyproject.toml").exists():
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repo_root = repo_root.parent
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src = repo_root / "packs" / "data" / "en_core_math_v1"
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with tempfile.TemporaryDirectory(prefix="core_flywheel_demo_") as td:
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dst = Path(td) / "en_core_math_v1"
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shutil.copytree(src, dst)
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# Strip any pre-existing composition entries — start scene 1 clean.
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comp_dir = dst / "compositions"
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if comp_dir.exists():
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for f in comp_dir.glob("*.jsonl"):
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f.unlink()
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if (dst / "compositions.jsonl").exists():
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(dst / "compositions.jsonl").unlink()
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yield dst
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def _patch_composition_registry_root(monkeypatch, pack_path: Path) -> None:
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from generate.comprehension import composition_registry as cr
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monkeypatch.setattr(cr, "_DEFAULT_PACK_RELPATH", pack_path)
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monkeypatch.setattr(cr, "_repo_root", lambda: Path("/"))
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def _ratify(pack_path: Path) -> None:
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"""Scene 2 — operator ratification + compile + seed recognizer.
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Ratifies the multiplicative_aggregate composition shape
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(``bound(outer_count) × bound(per_outer_count)``) that the
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WAVE-A injector consumes; this maps directly to the canonical
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"<Subject> fills <M> <noun> where each <inner> holds <N> <unit>"
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shape used by the Lilibeth canary.
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"""
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from teaching.math_evidence import AuditRow, from_audit_row
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from teaching.math_composition_ratification import apply_composition_claim
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audit_row = AuditRow(
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case_id="public-demo-lilibeth-baskets",
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sentence_index=0,
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token_index=8,
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token_text="",
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recognized_terms=(
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"Lilibeth", "fills", "6", "baskets", "where",
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"each", "basket", "holds", "50", "strawberries",
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),
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skipped_frame="operation_frame",
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missing_operator="multi_quantity_composition",
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refusal_reason="incomplete_operation",
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refusal_detail="operation_frame has 2 quantities; multi-quantity ops are Phase-2.1 scope",
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)
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evidence = from_audit_row(audit_row, sub_type="composition")
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apply_composition_claim(
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claim=evidence,
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composition_category="multiplicative_composition",
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polarity="affirms",
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reviewer="public_demo",
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surface_pattern=CANARY_COMPOSITION_SHAPE,
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evidence_source="math_audit",
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pack_root=pack_path,
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)
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def _seed_recognizer_for_demo() -> str:
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"""Append (idempotent) a RatifiedRecognizer entry for currency_per_unit_composition.
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Mirrors ``core teaching seed-recognizer``; for the demo we write
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directly via ProposalLog._append so the demo is self-contained
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(no shell-out). Returns the proposal_id appended (or the existing
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one if already present, by content digest).
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"""
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import datetime
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import hashlib
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from teaching.proposals import ProposalLog
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canonical_pattern = {
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"anchor_kind": "multiplicative_aggregate",
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"shape_category": "multiplicative_aggregation",
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"outcome": "admissible",
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"observed_units": sorted(CANARY_OBSERVED_UNITS),
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"extract_values": True,
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"graph_intent": "aggregate",
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}
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spec_bytes = json.dumps(
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canonical_pattern, sort_keys=True, separators=(",", ":")
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).encode("utf-8")
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spec_digest = hashlib.sha256(spec_bytes).hexdigest()
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proposal_id = f"rat1-seed-{spec_digest[:16]}"
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log = ProposalLog()
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existing = log.current_state()
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if proposal_id in existing:
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return proposal_id
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recognizer_spec = {
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"shape_category": "multiplicative_aggregation",
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"canonical_pattern": canonical_pattern,
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"exemplar_count": 0,
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"exemplar_digest": spec_digest,
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"coverage": {},
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}
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proposal_payload = {
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"proposal_id": proposal_id,
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"polarity": "affirms",
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"claim_domain": "factual",
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"evidence": [],
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"proposed_chain": {
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"subject": "multiplicative_aggregation",
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"intent": "recognizer_spec_seed",
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"connective": "ratifies",
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"object": "multiplicative_aggregate",
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"recognizer_spec": recognizer_spec,
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},
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"source": {
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"kind": "exemplar_corpus",
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"source_id": spec_digest,
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"emitted_at_revision": "flywheel-demo",
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},
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}
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log._append({"event": "created", "proposal": proposal_payload})
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log._append({
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"event": "transition",
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"proposal_id": proposal_id,
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"to": "accepted",
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"note": "flywheel-demo seed",
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"review_date": datetime.date.today().isoformat(),
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})
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return proposal_id
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def _sha256_hex(data: bytes) -> str:
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return hashlib.sha256(data).hexdigest()
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def _eval_prompt(prompt: str) -> tuple[Any, str | None]:
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from generate.math_candidate_graph import parse_and_solve
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r = parse_and_solve(prompt)
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return r.answer, r.refusal_reason
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def run_tour(*, emit_json: bool = False) -> FlywheelDemoResult:
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"""Execute the four-scene flywheel demo. Pure: no canonical pack mutation."""
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import importlib
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from generate.recognizer_registry import clear_registry_cache
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from generate.comprehension import composition_registry as cr
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# We use monkeypatch-style attribute swaps without pytest; rebind
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# the module attribute and restore at end.
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orig_pack_relpath = cr._DEFAULT_PACK_RELPATH
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orig_repo_root = cr._repo_root
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orig_cr_cache = dict(cr._CACHE)
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scenes: list[SceneResult] = []
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try:
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# Idempotent one-time recognizer seed (lives in the canonical
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# proposal log; the demo would write the same proposal_id every
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# run, so subsequent runs are no-ops). This represents the
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# one-time operator action that admits a new shape category.
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clear_registry_cache()
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cr._CACHE.clear()
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proposal_id = _seed_recognizer_for_demo()
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clear_registry_cache()
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# Scene 1 — RATIFY: handler writes JSONL + RAT-1 auto-compiles
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# the runtime artifact + updates the manifest checksum.
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with _isolated_pack() as pack:
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cr._DEFAULT_PACK_RELPATH = pack
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cr._repo_root = lambda: Path("/")
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cr._CACHE.clear()
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_ratify(pack)
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src_file = pack / "compositions" / "multiplicative_composition.jsonl"
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compiled_file = pack / "compositions.jsonl"
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manifest = json.loads((pack / "manifest.json").read_text())
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scene1_pass = (
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src_file.exists()
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and compiled_file.exists()
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and "composition_checksum" in manifest
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and manifest["composition_checksum"] == _sha256_hex(compiled_file.read_bytes())
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)
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scenes.append(SceneResult(
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name="1_ratify_writes_and_compiles",
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expected=(
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"apply_composition_claim writes source JSONL; RAT-1 "
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"auto-compile regenerates compositions.jsonl + updates "
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"manifest.composition_checksum"
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),
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actual=(
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f"src={src_file.exists()} compiled={compiled_file.exists()} "
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f"manifest_checksum={'composition_checksum' in manifest}"
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),
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passed=scene1_pass,
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detail=f"recognizer_seeded={proposal_id}",
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))
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# Scene 2 — LOAD: composition_registry reads the new entry.
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cr._CACHE.clear()
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from generate.comprehension.composition_registry import (
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load_composition_registry,
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is_affirmed,
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)
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reg = load_composition_registry()
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scene2_pass = (
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not reg.is_empty()
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and is_affirmed(reg, CANARY_COMPOSITION_SHAPE)
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)
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scenes.append(SceneResult(
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name="2_runtime_registry_picks_up_entry",
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expected="composition_registry loads + affirms the new pattern",
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actual=f"is_empty={reg.is_empty()} affirmed={is_affirmed(reg, CANARY_COMPOSITION_SHAPE)}",
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passed=scene2_pass,
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detail=f"shape={CANARY_COMPOSITION_SHAPE!r}",
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))
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# Scene 3 — ADMIT: a real problem solves end-to-end.
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ans, reason = _eval_prompt(CANARY_PROMPT)
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scene3_pass = ans == EXPECTED_ANSWER
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scenes.append(SceneResult(
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name="3_end_to_end_solve",
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expected=f"answer={EXPECTED_ANSWER} for the Lilibeth canary",
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actual=f"answer={ans!r} reason={(reason or 'OK')[:80]!r}",
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passed=scene3_pass,
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detail="ratify → compile → load → match → inject → admit → solve",
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))
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# Scene 4 — HAZARD: case 0050 must remain refused.
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ans_hz, reason_hz = _eval_prompt(CASE_0050_PROMPT)
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hazard_pass = ans_hz is None
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scenes.append(SceneResult(
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name="4_hazard_pin_case_0050_still_refused",
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expected="refused — the wrong=0 canary cannot be converted",
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actual=f"answer={ans_hz!r} reason={(reason_hz or 'admitted!')[:80]!r}",
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passed=hazard_pass,
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detail="SAFE_COMPOSITION_CATEGORIES does not admit this shape",
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))
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finally:
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cr._DEFAULT_PACK_RELPATH = orig_pack_relpath
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cr._repo_root = orig_repo_root
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cr._CACHE.clear()
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cr._CACHE.update(orig_cr_cache)
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clear_registry_cache()
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result = FlywheelDemoResult(scenes=tuple(scenes))
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if emit_json:
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print(json.dumps(result.as_dict(), indent=2, sort_keys=True))
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else:
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_print_text(result)
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return result
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def _print_text(result: FlywheelDemoResult) -> None:
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print("=" * 72)
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print("CORE — Math Composition Flywheel — Public Reproduction Demo")
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print("=" * 72)
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print()
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print("Thesis: cognition is the deterministic decoding of structure")
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print("that already exists. The load-bearing invariant is wrong == 0.")
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print()
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print("Four scenes, each falsifiable:")
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print()
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for s in result.scenes:
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mark = "✓" if s.passed else "✗"
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print(f" Scene {s.name}")
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print(f" expected: {s.expected}")
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print(f" actual: {s.actual}")
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print(f" {mark} {s.detail}")
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print()
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print("=" * 72)
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summary = "ALL PASSED" if result.all_passed else "FAILED"
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print(f" {summary}")
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print("=" * 72)
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print()
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print("Reproduce:")
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print(" git clone https://github.com/AssetOverflow/core")
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print(" cd core && uv pip install -e .")
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print(" core demo flywheel")
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print()
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__all__ = [
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"FlywheelDemoResult",
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"SceneResult",
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"run_tour",
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"CANARY_PROMPT",
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"EXPECTED_ANSWER",
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"CASE_0050_PROMPT",
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]
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