#!/usr/bin/env python3 """N-space tuning manifest for the local physics/math/compression LLM. The advantage of this stack is not just more examples. It is explicit coordinate tooling: manifold deltas, oriented-volume adapters, fixed-width hardware cells, n-dimensional behavioral vectors, and eigen-basis priors. This script turns those into compact SFT curriculum records. """ from __future__ import annotations import argparse import json from pathlib import Path from typing import Any NSPACE_AXES = [ { "axis": "ns_md_hardware_delta", "dimension": "addressed manifold cell", "source": "0-Core-Formalism/otom/hardware/verilog/core/ns_md_decoder.v", "primitive": "[32-bit Addr][8-bit Control][optional Count][64-bit Witness]", "compression_use": "delta-coded manifold updates with nibble switch payloads", "receipt_rule": "addr/control/count/witness must survive transport", }, { "axis": "oriented_volume_adapter", "dimension": "n-dimensional basis cell", "source": "0-Core-Formalism/otom/specs/Cramers-Rule-Oriented-Volume-Adapter.md", "primitive": "x_k = det(A_k) / det(A)", "compression_use": "coordinate extraction by shared reference-face cancellation", "receipt_rule": "det(A) nonzero and replacement-column index recorded", }, { "axis": "behavioral_manifold_31", "dimension": "31 coordinates", "source": "0-Core-Formalism/otom/tools/lean/Semantics/Semantics/MarketFilter.lean", "primitive": "identity/conservation/transformation/scaling/dynamics coordinate blocks", "compression_use": "compare behavior by weighted fixed-point distance, not labels", "receipt_rule": "Q16.16 coordinates, weights, and claim state retained", }, { "axis": "cross_domain_eigen_basis", "dimension": "term-domain similarity space", "source": "4-Infrastructure/shim/cross_domain_registry_eigenvectors.json", "primitive": "leading eigenvector over registry-derived term/domain matrix", "compression_use": "shared coordinates such as bond/matrix/geometry/provenance or kmer/long_context", "receipt_rule": "eigenvector is ranking prior only, never domain truth", }, { "axis": "bitpack_hardware_cell", "dimension": "fixed bit width", "source": "6-Documentation/tiddlywiki-local/wiki/tiddlers/Lean BitPack Hardware Encoding.tid", "primitive": "value -> BitVec n -> UART/PBACS/Tang receipt", "compression_use": "turn symbolic/logogram tokens into witnessable fixed-width cells", "receipt_rule": "bit width and roundtrip representation must be explicit", }, ] PIPELINE_STAGES = [ { "stage": "retrieve", "action": "load local registry/wiki/eigen/prover receipts", "failure_mode": "unverified memory or stale web claims", }, { "stage": "embed_nspace", "action": "map candidate into an explicit coordinate axis", "failure_mode": "free prose without coordinates", }, { "stage": "compress", "action": "choose shortest lawful payload: delta, kmer, bond matrix, bitpack cell, or template token", "failure_mode": "large chatty prompt instead of compact surface cell", }, { "stage": "route", "action": "select Lean/source/Tang/Ollama/metaprobe channel by claim boundary", "failure_mode": "model confidence replacing receipts", }, { "stage": "witness", "action": "emit JSON receipt and optional hardware receipt", "failure_mode": "summary without durable artifact", }, ] def existing_receipt_summary() -> dict[str, Any]: paths = [ Path("4-Infrastructure/shim/metaprobe_physics_math_llm_direct_receipt.json"), Path("4-Infrastructure/shim/cross_domain_registry_eigenvectors.json"), Path("4-Infrastructure/shim/molecular_registry_eigenvectors.json"), Path("4-Infrastructure/shim/genomic_registry_eigenvectors.json"), ] out = {} for path in paths: if not path.exists(): continue data = json.loads(path.read_text(encoding="utf-8")) out[str(path)] = { "schema": data.get("schema"), "lawful": data.get("lawful", data.get("overall_lawful")), "domain_count": data.get("domain_count"), "top_terms": [item.get("term") for item in data.get("top_terms", [])[:8]], "top_domains": [item.get("domain") for item in data.get("weighted_domains", [])[:5]], } return out def curriculum_records(receipt: dict[str, Any]) -> list[dict[str, Any]]: system = "You are an n-space compression router. Return compact JSON with evidence boundaries." records = [] for axis in receipt["nspace_axes"]: prompt = { "task": "route_with_nspace_axis", "axis": axis["axis"], "dimension": axis["dimension"], "primitive": axis["primitive"], "instruction": "Use this axis to compress and route a local research claim.", } answer = { "selected": True, "use_as": axis["compression_use"], "claim_boundary": "coordinate-routing-prior", "surface_payload_hint": axis["axis"][:16].upper(), "receipt_rule": axis["receipt_rule"], } records.append(chat_record(system, prompt, answer)) prompt = { "task": "apply_nspace_pipeline", "pipeline": receipt["pipeline_stages"], "instruction": "Choose the pipeline behavior for tuning the local LLM.", } answer = { "selected": True, "use_as": "nspace_llm_pipeline_policy", "claim_boundary": "pipeline-guidance-only", "decision": "Prefer coordinate-bearing examples over prose-only examples; every answer should choose an axis, payload, route, and receipt.", "surface_payload_hint": "NSPACE-ROUTE", } records.append(chat_record(system, prompt, answer)) return records def chat_record(system: str, prompt: dict[str, Any], answer: dict[str, Any]) -> dict[str, Any]: return { "messages": [ {"role": "system", "content": system}, {"role": "user", "content": json.dumps(prompt, ensure_ascii=False)}, {"role": "assistant", "content": json.dumps(answer, ensure_ascii=False)}, ] } def main() -> int: parser = argparse.ArgumentParser() parser.add_argument("--receipt", type=Path, default=Path("4-Infrastructure/shim/nspace_llm_pipeline_tuning_receipt.json")) parser.add_argument("--curriculum", type=Path, default=Path("4-Infrastructure/shim/nspace_llm_pipeline_tuning_curriculum.jsonl")) args = parser.parse_args() receipt = { "schema": "nspace_llm_pipeline_tuning_receipt_v1", "claim_boundary": "N-space axes tune routing/compression behavior; they do not prove domain claims.", "nspace_axes": NSPACE_AXES, "pipeline_stages": PIPELINE_STAGES, "existing_receipts": existing_receipt_summary(), "lawful": True, } args.receipt.parent.mkdir(parents=True, exist_ok=True) args.receipt.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") with args.curriculum.open("w", encoding="utf-8") as handle: for record in curriculum_records(receipt): handle.write(json.dumps(record, ensure_ascii=False) + "\n") print(json.dumps(receipt, indent=2, ensure_ascii=False)) return 0 if __name__ == "__main__": raise SystemExit(main())