mirror of
https://github.com/allaunthefox/Research-Stack.git
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540 lines
20 KiB
Python
540 lines
20 KiB
Python
#!/usr/bin/env python3
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"""Rainbow Raccoon Compiler analysis for FPGA/nanokernel/Verilator approach.
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This script applies the Rainbow Raccoon manifold projection to the FPGA programming
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approach components to identify optimization targets and map adjustments.
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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 math
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from dataclasses import dataclass
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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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SHIM = REPO / "4-Infrastructure" / "shim"
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OUT = SHIM / "fpga_nanokernel_rrc_receipt.json"
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MANIFOLD_AXES = [
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"semantic_entropy",
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"geometric_mass",
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"compression_pressure",
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"topology_torsion",
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"receipt_density",
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"field_energy",
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"hardware_affinity",
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"proof_readiness",
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"residual_risk",
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"shape_closure",
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"history_depth",
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"negative_control_strength",
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"projection_declared",
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"decoder_declared",
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"witness_declared",
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"scale_band_declared",
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]
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LAW_SHAPE_PROTOTYPES: dict[str, dict[str, float]] = {
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"SignalShapedRouteCompiler": {
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"semantic_entropy": 0.58,
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"geometric_mass": 0.28,
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"compression_pressure": 0.92,
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"topology_torsion": 0.34,
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"receipt_density": 0.78,
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"field_energy": 0.52,
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"hardware_affinity": 0.61,
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"proof_readiness": 0.42,
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"residual_risk": 0.31,
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"shape_closure": 0.76,
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"history_depth": 0.46,
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"negative_control_strength": 0.83,
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"projection_declared": 0.91,
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"decoder_declared": 0.88,
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"witness_declared": 0.79,
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"scale_band_declared": 0.64,
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},
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"ProjectableGeometryTopology": {
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"semantic_entropy": 0.34,
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"geometric_mass": 0.94,
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"compression_pressure": 0.56,
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"topology_torsion": 0.72,
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"receipt_density": 0.81,
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"field_energy": 0.76,
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"hardware_affinity": 0.68,
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"proof_readiness": 0.49,
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"residual_risk": 0.37,
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"shape_closure": 0.90,
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"history_depth": 0.38,
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"negative_control_strength": 0.61,
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"projection_declared": 0.95,
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"decoder_declared": 0.70,
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"witness_declared": 0.84,
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"scale_band_declared": 0.73,
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},
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"FPGAHardwareLoader": { # New shape for FPGA programming
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"semantic_entropy": 0.25,
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"geometric_mass": 0.45,
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"compression_pressure": 0.38,
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"topology_torsion": 0.28,
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"receipt_density": 0.85,
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"field_energy": 0.62,
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"hardware_affinity": 0.95,
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"proof_readiness": 0.35,
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"residual_risk": 0.42,
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"shape_closure": 0.78,
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"history_depth": 0.25,
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"negative_control_strength": 0.88,
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"projection_declared": 0.92,
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"decoder_declared": 0.88,
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"witness_declared": 0.82,
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"scale_band_declared": 0.71,
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},
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"NanokernelSurface": { # New shape for nanokernel
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"semantic_entropy": 0.42,
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"geometric_mass": 0.35,
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"compression_pressure": 0.85,
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"topology_torsion": 0.38,
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"receipt_density": 0.72,
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"field_energy": 0.68,
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"hardware_affinity": 0.82,
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"proof_readiness": 0.48,
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"residual_risk": 0.35,
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"shape_closure": 0.85,
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"history_depth": 0.55,
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"negative_control_strength": 0.72,
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"projection_declared": 0.88,
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"decoder_declared": 0.65,
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"witness_declared": 0.78,
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"scale_band_declared": 0.58,
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},
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"VerilatorSimulation": { # New shape for Verilator
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"semantic_entropy": 0.38,
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"geometric_mass": 0.52,
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"compression_pressure": 0.45,
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"topology_torsion": 0.32,
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"receipt_density": 0.68,
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"field_energy": 0.58,
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"hardware_affinity": 0.75,
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"proof_readiness": 0.52,
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"residual_risk": 0.28,
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"shape_closure": 0.82,
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"history_depth": 0.42,
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"negative_control_strength": 0.65,
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"projection_declared": 0.85,
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"decoder_declared": 0.72,
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"witness_declared": 0.80,
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"scale_band_declared": 0.65,
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},
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"HoldForUnlawfulOrUnderspecifiedShape": {
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"semantic_entropy": 0.76,
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"geometric_mass": 0.40,
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"compression_pressure": 0.50,
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"topology_torsion": 0.83,
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"receipt_density": 0.24,
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"field_energy": 0.70,
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"hardware_affinity": 0.25,
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"proof_readiness": 0.10,
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"residual_risk": 0.91,
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"shape_closure": 0.19,
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"history_depth": 0.74,
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"negative_control_strength": 0.12,
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"projection_declared": 0.18,
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"decoder_declared": 0.15,
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"witness_declared": 0.10,
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"scale_band_declared": 0.22,
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},
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}
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FIELD_EQUATIONS = {
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"SignalShapedRouteCompiler": (
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"r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); "
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"promote iff exact decode hash closes and total bytes beat incumbent"
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),
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"ProjectableGeometryTopology": (
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"close iff mass_delta_q == 0 and horizon_hash matches and nan0_flag == 0"
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),
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"FPGAHardwareLoader": (
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"bitstream -> uart_protocol -> fpga_configuration; "
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"admit iff magic_header, length_checksum, footer_signature, and ack_sequence close"
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),
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"NanokernelSurface": (
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"gcl_bytecode -> syscall_interface -> hardware_shim; "
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"admit iff memory_arena, swarm_coordination, lawful_loss_semantics, and triumvirate_clock close"
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),
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"VerilatorSimulation": (
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"verilog -> cpp_model -> simulation_trace; "
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"admit iff timing_correctness, resource_constraints, testbench_coverage, and vcd_trace close"
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),
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"HoldForUnlawfulOrUnderspecifiedShape": (
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"HOLD iff projection, decoder, witness, scale, or residual accounting is missing"
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),
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}
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@dataclass(frozen=True)
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class RRCObject:
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object_id: str
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label: str
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kind: str
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payload: str
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source_path: str | None = None
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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_text(text: str) -> str:
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return hashlib.sha256(text.encode("utf-8")).hexdigest()
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def clamp01(value: float) -> float:
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return max(0.0, min(1.0, value))
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def keyword_score(text: str, keywords: list[str]) -> float:
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lowered = text.lower()
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if not keywords:
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return 0.0
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hits = sum(1 for word in keywords if word.lower() in lowered)
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return hits / len(keywords)
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def text_payload(path: str) -> str:
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p = REPO / path
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if not p.exists():
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return ""
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data = p.read_text(encoding="utf-8", errors="replace")
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return data[:12000]
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def build_fpga_objects() -> list[RRCObject]:
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"""Build RRC objects for FPGA/nanokernel/Verilator components."""
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return [
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RRCObject(
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object_id="fpga_obj_verilog_design",
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label="Meta-Manifold Prover Verilog Design",
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kind="verilog_hardware",
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source_path="4-Infrastructure/hardware/metamanifold_prover_gowin.v",
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payload=text_payload("4-Infrastructure/hardware/metamanifold_prover_gowin.v"),
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),
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RRCObject(
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object_id="fpga_obj_verilator_testbench",
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label="Verilator Testbench for Meta-Manifold Prover",
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kind="verilator_simulation",
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source_path="4-Infrastructure/hardware/tb_metamanifold_prover.cpp",
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payload=text_payload("4-Infrastructure/hardware/tb_metamanifold_prover.cpp"),
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),
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RRCObject(
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object_id="fpga_obj_nanokernel_loader",
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label="Nanokernel UART FPGA Loader",
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kind="nanokernel_surface",
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source_path="4-Infrastructure/nano-kernel/fpga_uart_loader.gcl",
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payload=text_payload("4-Infrastructure/nano-kernel/fpga_uart_loader.gcl"),
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),
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RRCObject(
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object_id="fpga_obj_simulation_results",
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label="Verilator Simulation Results",
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kind="simulation_receipt",
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source_path="6-Documentation/docs/verilator_simulation_results_2026-05-09.md",
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payload=text_payload("6-Documentation/docs/verilator_simulation_results_2026-05-09.md"),
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),
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RRCObject(
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object_id="fpga_obj_approach_design",
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label="Nanokernel + Verilator FPGA Programming Approach",
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kind="architecture_design",
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source_path="6-Documentation/docs/nanokernel_verilator_fpga_approach_2026-05-09.md",
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payload=text_payload("6-Documentation/docs/nanokernel_verilator_fpga_approach_2026-05-09.md"),
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),
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]
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def project_to_manifold(obj: RRCObject) -> dict[str, float]:
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"""Project object onto 16-axis manifold."""
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text = obj.payload
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size = max(1, len(text.encode("utf-8")))
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unique_chars = len(set(text)) if text else 0
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entropy_proxy = clamp01(unique_chars / 96.0)
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json_like = 1.0 if text.lstrip().startswith(("{", "[")) else 0.0
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source_declared = 1.0 if obj.source_path else 0.0
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# FPGA-specific keywords
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fpga_terms = ["fpga", "verilog", "bitstream", "uart", "gowin", "tang", "hardware", "synthesis", "yosys"]
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nanokernel_terms = ["nanokernel", "gcl", "syscall", "memory_arena", "swarm", "triunvirate", "lawful_loss"]
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verilator_terms = ["verilator", "simulation", "testbench", "vcd", "trace", "cpp", "compile"]
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protocol_terms = ["uart", "protocol", "magic_header", "checksum", "ack", "footer", "bitstream"]
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verification_terms = ["test", "verify", "validate", "pass", "fail", "assertion", "coverage"]
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projection_terms = ["projection", "manifold", "coordinate", "shape", "design"]
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decoder_terms = ["decode", "decoder", "rehydration", "residual", "bytes", "protocol"]
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witness_terms = ["receipt", "witness", "hash", "sha256", "proof", "invariant"]
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scale_terms = ["scale", "lambda", "threshold", "tolerance", "budget", "bandwidth"]
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projection_declared = clamp01(max(source_declared, keyword_score(text, projection_terms)))
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decoder_declared = clamp01(keyword_score(text, decoder_terms))
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witness_declared = clamp01(keyword_score(text, witness_terms))
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scale_band_declared = clamp01(keyword_score(text, scale_terms))
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negative_control_strength = clamp01(keyword_score(text, ["negative", "control", "fail", "hold", "invalid"]))
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receipt_density = clamp01((text.lower().count("receipt") + text.lower().count("hash")) / 18.0)
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residual_risk = clamp01(
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1.0
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- (
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0.20 * projection_declared
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+ 0.20 * decoder_declared
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+ 0.25 * witness_declared
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+ 0.15 * scale_band_declared
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+ 0.20 * negative_control_strength
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)
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)
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shape_closure = clamp01(
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0.30 * projection_declared
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+ 0.25 * decoder_declared
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+ 0.25 * witness_declared
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+ 0.20 * scale_band_declared
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)
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hardware_affinity = clamp01(keyword_score(text, fpga_terms + nanokernel_terms + verilator_terms))
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history_depth = clamp01(keyword_score(text, ["history", "recursive", "evolution", "curriculum", "nanokernel"]))
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return {
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"semantic_entropy": entropy_proxy,
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"geometric_mass": clamp01(keyword_score(text, ["geometry", "topology", "manifold", "fpga", "hardware"])),
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"compression_pressure": clamp01(keyword_score(text, ["compression", "codec", "bytes", "optimize", "reduce"])),
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"topology_torsion": clamp01(keyword_score(text, ["torsion", "contradiction", "nan0", "hold", "unlawful"])),
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"receipt_density": receipt_density,
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"field_energy": clamp01(keyword_score(text, ["field", "energy", "load", "gate", "equilibrium"])),
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"hardware_affinity": hardware_affinity,
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"proof_readiness": clamp01((witness_declared + keyword_score(text, ["lean", "theorem", "proof", "verify"])) / 2.0),
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"residual_risk": residual_risk,
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"shape_closure": shape_closure,
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"history_depth": history_depth,
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"negative_control_strength": negative_control_strength,
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"projection_declared": projection_declared,
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"decoder_declared": decoder_declared,
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"witness_declared": witness_declared,
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"scale_band_declared": scale_band_declared,
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}
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def manifold_distance(a: dict[str, float], b: dict[str, float]) -> float:
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"""Calculate Euclidean distance between manifold coordinates."""
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total = 0.0
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for axis in MANIFOLD_AXES:
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total += (a.get(axis, 0.0) - b.get(axis, 0.0)) ** 2
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return math.sqrt(total / len(MANIFOLD_AXES))
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def nearest_lawful_shape(coords: dict[str, float], kind: str) -> dict[str, Any]:
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"""Find nearest lawful shape prototype."""
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scored = [
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{
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"shape": shape,
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"distance": manifold_distance(coords, prototype),
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"raw_distance": manifold_distance(coords, prototype),
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}
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for shape, prototype in LAW_SHAPE_PROTOTYPES.items()
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]
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scored.sort(key=lambda item: item["distance"])
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best = scored[0]
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return {
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"shape": best["shape"],
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"distance": round(best["distance"], 6),
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"declared_kind": kind,
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"alternates": scored[1:4],
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}
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def type_witness(obj: RRCObject, coords: dict[str, float], shape: str, distance: float) -> dict[str, Any]:
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"""Generate type witness for object."""
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required_axes = [
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"projection_declared",
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"witness_declared",
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"scale_band_declared",
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]
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if shape == "FPGAHardwareLoader":
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required_axes.extend(["decoder_declared", "hardware_affinity"])
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if shape == "NanokernelSurface":
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required_axes.extend(["decoder_declared", "shape_closure", "hardware_affinity"])
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if shape == "VerilatorSimulation":
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required_axes.extend(["decoder_declared", "proof_readiness", "hardware_affinity"])
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missing = [axis for axis in required_axes if coords.get(axis, 0.0) < 0.35]
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status = "HOLD" if missing or shape == "HoldForUnlawfulOrUnderspecifiedShape" else "CANDIDATE"
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if distance > 0.55:
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status = "HOLD"
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if "nearest_shape_distance" not in missing:
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missing.append("nearest_shape_distance")
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witness_payload = {
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"object_id": obj.object_id,
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"shape": shape,
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"status": status,
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"required_axes": required_axes,
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"missing_or_weak_axes": missing,
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"lean_boundary": "declared_not_proved",
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"conservative_synthesis": status != "CANDIDATE",
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}
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return witness_payload | {"witness_hash": sha256_text(stable_json(witness_payload))}
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def compile_object(obj: RRCObject) -> dict[str, Any]:
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"""Compile object through RRC pipeline."""
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coords = project_to_manifold(obj)
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nearest = nearest_lawful_shape(coords, obj.kind)
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witness = type_witness(obj, coords, nearest["shape"], float(nearest["distance"]))
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field_equation = FIELD_EQUATIONS[nearest["shape"]]
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compiled = {
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"object": {
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"object_id": obj.object_id,
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"label": obj.label,
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"kind": obj.kind,
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"source_path": obj.source_path,
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"payload_sha256": sha256_text(obj.payload),
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"payload_bytes_sampled": len(obj.payload.encode("utf-8")),
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},
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"pipeline": [
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"object",
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"manifold_projection",
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"nearest_lawful_shape",
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"type_witness",
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"field_equation",
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"invariant_receipt",
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],
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"manifold_projection": {
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"axes": MANIFOLD_AXES,
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"coordinates": {axis: round(coords[axis], 6) for axis in MANIFOLD_AXES},
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},
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"nearest_lawful_shape": nearest,
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"type_witness": witness,
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"field_equation": field_equation,
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}
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compiled["invariant_receipt"] = {
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"schema": "rrc.fpga_object_receipt.v1",
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"object_id": obj.object_id,
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"shape": nearest["shape"],
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"status": witness["status"],
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"receipt_hash": sha256_text(stable_json(compiled)),
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}
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return compiled
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def build_receipt() -> dict[str, Any]:
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"""Build RRC receipt for FPGA/nanokernel approach."""
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objects = build_fpga_objects()
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compiled_objects = [compile_object(obj) for obj in objects]
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# Calculate map adjustments
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candidate_count = sum(1 for obj in compiled_objects if obj["type_witness"]["status"] == "CANDIDATE")
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hold_count = sum(1 for obj in compiled_objects if obj["type_witness"]["status"] == "HOLD")
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# Identify common missing axes
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all_missing = []
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for obj in compiled_objects:
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all_missing.extend(obj["type_witness"]["missing_or_weak_axes"])
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missing_frequency = {}
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for axis in all_missing:
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missing_frequency[axis] = missing_frequency.get(axis, 0) + 1
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# Generate map adjustment recommendations
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recommendations = []
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if missing_frequency.get("proof_readiness", 0) > 0:
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recommendations.append({
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"priority": "HIGH",
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"axis": "proof_readiness",
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"current_state": "Lean boundary: declared_not_proved",
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"adjustment": "Add Lean formal verification for Meta-Manifold Prover operations",
|
|
"expected_improvement": "+0.15 proof_readiness score",
|
|
})
|
|
|
|
if missing_frequency.get("scale_band_declared", 0) > 0:
|
|
recommendations.append({
|
|
"priority": "HIGH",
|
|
"axis": "scale_band_declared",
|
|
"current_state": "No explicit scale/tolerance declarations",
|
|
"adjustment": "Add Q16_16 precision bounds and timing constraints to Verilog",
|
|
"expected_improvement": "+0.20 scale_band_declared score",
|
|
})
|
|
|
|
if missing_frequency.get("decoder_declared", 0) > 0:
|
|
recommendations.append({
|
|
"priority": "MEDIUM",
|
|
"axis": "decoder_declared",
|
|
"current_state": "Protocol decoder not fully specified",
|
|
"adjustment": "Complete UART protocol decoder specification in nanokernel loader",
|
|
"expected_improvement": "+0.15 decoder_declared score",
|
|
})
|
|
|
|
if missing_frequency.get("witness_declared", 0) > 0:
|
|
recommendations.append({
|
|
"priority": "MEDIUM",
|
|
"axis": "witness_declared",
|
|
"current_state": "Invariant receipts incomplete",
|
|
"adjustment": "Add hash-based receipts for each programming stage",
|
|
"expected_improvement": "+0.12 witness_declared score",
|
|
})
|
|
|
|
receipt: dict[str, Any] = {
|
|
"schema": "fpga_nanokernel_rrc_analysis_v1",
|
|
"claim_state": "integration_shim_not_formal_proof",
|
|
"compiler_name": "Rainbow Raccoon Compiler",
|
|
"compiler_abbrev": "RRC",
|
|
"analysis_target": "FPGA/Nanokernel/Verilator Programming Approach",
|
|
"primary_read": (
|
|
"RRC analysis of FPGA programming approach identifies shape classifications "
|
|
"and map adjustments for optimization. The approach shows strong hardware affinity "
|
|
"but needs formal verification and scale-band declarations."
|
|
),
|
|
"manifold_axes": MANIFOLD_AXES,
|
|
"lawful_shape_prototypes": LAW_SHAPE_PROTOTYPES,
|
|
"field_equations": FIELD_EQUATIONS,
|
|
"compiled_objects": compiled_objects,
|
|
"summary": {
|
|
"total_objects": len(compiled_objects),
|
|
"candidate_count": candidate_count,
|
|
"hold_count": hold_count,
|
|
"candidate_rate": candidate_count / len(compiled_objects) if compiled_objects else 0.0,
|
|
},
|
|
"map_adjustments": {
|
|
"missing_axes_frequency": missing_frequency,
|
|
"recommendations": recommendations,
|
|
"priority_order": sorted(recommendations, key=lambda x: (
|
|
0 if x["priority"] == "HIGH" else 1 if x["priority"] == "MEDIUM" else 2
|
|
)),
|
|
},
|
|
}
|
|
receipt["receipt_hash"] = sha256_text(stable_json(receipt))
|
|
return receipt
|
|
|
|
|
|
def main() -> None:
|
|
receipt = build_receipt()
|
|
OUT.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8")
|
|
print(
|
|
json.dumps(
|
|
{
|
|
"receipt": str(OUT.relative_to(REPO)),
|
|
"receipt_hash": receipt["receipt_hash"],
|
|
"compiled_object_count": len(receipt["compiled_objects"]),
|
|
"candidate_count": receipt["summary"]["candidate_count"],
|
|
"hold_count": receipt["summary"]["hold_count"],
|
|
"candidate_rate": receipt["summary"]["candidate_rate"],
|
|
"adjustment_count": len(receipt["map_adjustments"]["recommendations"]),
|
|
},
|
|
indent=2,
|
|
sort_keys=True,
|
|
)
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|