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