mirror of
https://github.com/allaunthefox/Research-Stack.git
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263 lines
9.5 KiB
Python
263 lines
9.5 KiB
Python
#!/usr/bin/env python3
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"""External AI model prior ingest for Hutter/topology routing.
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This probe records outside model/paper links as priors only. It does not import
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weights, run external code, or promote an external result into the stack. The
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goal is to keep DMax, NTv3, and PhysMaster as typed HOLD surfaces with explicit
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promotion gates.
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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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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Any
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REPO = Path(__file__).resolve().parents[2]
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OUT_DIR = REPO / "shared-data" / "data" / "external_ai_model_prior_ingest"
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RECEIPT = OUT_DIR / "external_ai_model_prior_ingest_receipt.json"
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SUMMARY = OUT_DIR / "external_ai_model_prior_ingest.md"
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TIDDLER = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "External AI Model Prior Ingest.tid"
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PRIORS = [
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{
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"prior_id": "DMax.parallel_self_revision",
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"title": "DMax: Aggressive Parallel Decoding for dLLMs",
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"sources": [
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"https://arxiv.org/pdf/2604.08302",
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"https://github.com/czg1225/DMax",
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"https://huggingface.co/collections/Zigeng/dmax-training-data",
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"https://huggingface.co/collections/Zigeng/dmax-models",
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],
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"observed_claim": (
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"DMax reframes dLLM decoding as self-revising embedding-space refinement "
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"with on-policy uniform training and soft parallel decoding."
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),
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"stack_mapping": [
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"hutter_differential_frame_chain",
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"hutter_frame_invariant_root",
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"parallel_route_repair",
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"self_revision_without_truth_promotion",
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],
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"use_as": "decoding_parallelism_prior",
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"decision": "HOLD_EXTERNAL_DECODING_PRIOR",
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"promotion_gate": (
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"local benchmark required: exact replay, counted bytes, baseline comparison, "
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"resource envelope, and deterministic receipt over any adapted decoding path"
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),
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},
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{
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"prior_id": "NTv3.long_range_sequence_function",
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"title": "A foundational model for joint sequence-function multi-species modeling at scale for long-range genomic prediction",
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"sources": [
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"https://www.biorxiv.org/content/10.64898/2025.12.22.695963v1",
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"https://huggingface.co/spaces/InstaDeepAI/ntv3",
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"https://huggingface.co/collections/InstaDeepAI/nucleotide-transformer-v3",
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"https://github.com/instadeepai/nucleotide-transformer",
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],
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"observed_claim": (
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"NTv3 is presented as a foundation-model surface for long-range genomics "
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"and joint sequence-function modeling."
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),
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"stack_mapping": [
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"genomic_sequence_prior_surface",
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"cross_domain_sequence_function_prior",
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"long_range_dependency_probe",
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"biological_adapter_hold_lane",
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],
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"use_as": "long_range_sequence_function_prior",
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"decision": "HOLD_BIORXIV_PREPRINT_PRIOR",
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"promotion_gate": (
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"hold until preprint/source/model cards are locally receipted, benchmarked, "
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"license-checked, and mapped through a declared biological adapter"
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),
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},
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{
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"prior_id": "PhysMaster.LANDAU_agent_trace",
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"title": "PhysMaster: Building an Autonomous AI Physicist for Theoretical and Computational Physics Research",
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"sources": [
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"https://arxiv.org/pdf/2512.19799",
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],
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"observed_claim": (
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"PhysMaster presents an LLM-based physics research agent with a LANDAU "
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"library/priors/methodology substrate and code-based numerical loops."
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),
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"stack_mapping": [
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"forward_foundation_equation_compiler",
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"godel_gauntlet",
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"equation_atom_receipts",
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"research_agent_trace_prior",
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],
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"use_as": "research_agent_methodology_prior",
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"decision": "HOLD_EXTERNAL_AGENT_PRIOR",
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"promotion_gate": (
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"hold until task traces, retrieved-paper roots, numerical artifacts, "
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"critic failures, and reproduction receipts are local and inspectable"
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),
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},
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]
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def stable_json(obj: Any) -> str:
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return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True)
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def sha256_bytes(data: bytes) -> str:
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return hashlib.sha256(data).hexdigest()
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def hash_obj(obj: Any) -> str:
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return sha256_bytes(stable_json(obj).encode("utf-8"))
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def rel(path: Path) -> str:
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try:
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return str(path.relative_to(REPO))
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except ValueError:
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return str(path)
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def prior_entry(raw: dict[str, Any]) -> dict[str, Any]:
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entry = {
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**raw,
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"admission_status": "external_prior_hold",
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"claim_boundary": "metadata and routing prior only; no imported model/output is admitted",
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}
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entry["prior_hash"] = hash_obj({k: v for k, v in entry.items() if k != "prior_hash"})
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return entry
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def build_payload() -> dict[str, Any]:
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priors = [prior_entry(item) for item in PRIORS]
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payload = {
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"schema": "external_ai_model_prior_ingest_v1",
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"claim_boundary": (
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"External model/paper prior ingest only. These sources can suggest "
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"routing, benchmark, or architecture experiments, but they do not "
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"become trusted dependencies until local receipts close."
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),
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"priors": priors,
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"prior_root": hash_obj([item["prior_hash"] for item in priors]),
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"aggregates": {
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"prior_count": len(priors),
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"source_url_count": sum(len(item["sources"]) for item in priors),
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"hold_count": sum(1 for item in priors if item["decision"].startswith("HOLD")),
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},
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"decision": "ADMIT_EXTERNAL_AI_MODEL_PRIORS_AS_HOLD",
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}
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payload["payload_hash"] = hash_obj({k: v for k, v in payload.items() if k != "payload_hash"})
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return payload
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def build_receipt(payload: dict[str, Any]) -> dict[str, Any]:
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receipt = {
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"schema": "external_ai_model_prior_ingest_receipt_v1",
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"generated_at_utc": datetime.now(timezone.utc).isoformat(),
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"timestamp_role": "metadata_only",
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"generated_at_utc_included_in_receipt_hash": False,
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"payload_hash": payload["payload_hash"],
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"prior_root": payload["prior_root"],
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"aggregates": payload["aggregates"],
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"decision": payload["decision"],
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"claim_boundary": payload["claim_boundary"],
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}
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receipt["receipt_hash"] = sha256_bytes(
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stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8")
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)
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return receipt
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def write_summary(payload: dict[str, Any], receipt: dict[str, Any]) -> None:
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lines = [
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"# External AI Model Prior Ingest",
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"",
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f"Decision: `{receipt['decision']}` ",
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f"Receipt hash: `{receipt['receipt_hash']}` ",
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f"Prior root: `{payload['prior_root']}`",
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"",
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payload["claim_boundary"],
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"",
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"## Priors",
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"",
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"| Prior | Use as | Decision | Promotion gate |",
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"|---|---|---|---|",
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]
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for item in payload["priors"]:
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lines.append(f"| {item['prior_id']} | {item['use_as']} | {item['decision']} | {item['promotion_gate']} |")
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lines.extend(["", "## Source URLs", ""])
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for item in payload["priors"]:
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lines.append(f"### {item['prior_id']}")
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for url in item["sources"]:
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lines.append(f"- {url}")
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lines.append("")
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SUMMARY.write_text("\n".join(lines), encoding="utf-8")
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def write_tiddler(payload: dict[str, Any], receipt: dict[str, Any]) -> None:
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text = [
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"title: External AI Model Prior Ingest",
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"tags: ExternalPrior Hutter DMax PhysMaster Genomics HOLD Receipt",
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"type: text/vnd.tiddlywiki",
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"",
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"! External AI Model Prior Ingest",
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"",
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f"Decision: `{receipt['decision']}`",
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"",
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f"Receipt hash: `{receipt['receipt_hash']}`",
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"",
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f"Prior root: `{payload['prior_root']}`",
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"",
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"!! Priors",
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"",
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"| Prior | Use as | Decision |h",
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]
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for item in payload["priors"]:
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text.append(f"| {item['prior_id']} | {item['use_as']} | {item['decision']} |")
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text.extend(
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[
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"",
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"!! Links",
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"",
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"* [[Hutter Differential Frame Chain]]",
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"* [[Hutter Frame Invariant Root]]",
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"* [[Network Topology Model Reweighting]]",
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"* [[Underverse Variant Accounting]]",
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f"* Receipt: `{rel(RECEIPT)}`",
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f"* Summary: `{rel(SUMMARY)}`",
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]
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)
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TIDDLER.write_text("\n".join(text) + "\n", encoding="utf-8")
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def main() -> int:
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OUT_DIR.mkdir(parents=True, exist_ok=True)
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payload = build_payload()
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receipt = build_receipt(payload)
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(OUT_DIR / "external_ai_model_prior_ingest.json").write_text(
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json.dumps(payload, indent=2, sort_keys=True) + "\n", encoding="utf-8"
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)
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RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8")
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write_summary(payload, receipt)
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write_tiddler(payload, receipt)
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print(
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json.dumps(
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{
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"receipt": rel(RECEIPT),
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"receipt_hash": receipt["receipt_hash"],
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"prior_root": payload["prior_root"],
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"summary": rel(SUMMARY),
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"tiddler": rel(TIDDLER),
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"aggregates": payload["aggregates"],
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},
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indent=2,
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sort_keys=True,
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)
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)
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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