passages: exact relief skip + per-tile BVH node masks (L82); FI/TN LOD0-2 -12%
- Wall relief noise is skipped when its hard-bounded contribution cannot change the final max; mouth surface/tread noise only on paths that use it. - Exact active BVH-node masks cached per tile/passage/segment range; no per-sample lower_bound. - Full-view audit imports the exported Core plan (not the stale 26-point root route): 21,425/21,425 walkability samples pass. Cold (L80 protocol, medians) LOD0-2: SW 1.16->1.11, FI 2.86->2.51, TN 2.88->2.54, VS 1.23->1.22 s. Passage share FI 64.6->58.7%, TN 65.4->58.4%. Hashes identical (v214). TN "slowdown" from L81 not reproduced (5 pairs, 3.08 -> 2.75 s). Fast tier PASS; Linux UE host builds. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_016dhxM43dnWvUu2B5Ph1iG1
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#!/usr/bin/env python3
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"""Five interleaved TN LOD0-2 readiness pairs: 90378a5 vs 9cb5e53."""
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from __future__ import annotations
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import json
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import re
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import statistics
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import subprocess
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import time
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from pathlib import Path
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ROOT = Path(__file__).resolve().parents[4]
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OUT = ROOT / "Saved/Linux/l82/tn-regression"
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DATA = ROOT / "Standalone/data/owner_world.json"
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BINARIES = {
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"90378a5": ROOT / "Saved/Linux/l76/verify/bin/VoxelForgeCoreHeadless-verify-clang",
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"9cb5e53": ROOT / "Saved/Linux/l81/bin/VoxelForgeCoreHeadless-l81-clang",
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}
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CAMERA = (19.166015625, -14.2001953125, -753.3740234375)
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PIPELINE = re.compile(r"^\[StreamPipeline\] (.*)$", re.M)
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FIELDS = re.compile(r"([a-zA-Z0-9_]+)=([^ ]+)")
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NEAR = re.compile(r"^\[WorldAssemblerNearLODReady\] lods=0-2 tiles=(\d+)/(\d+) "
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r"cold_ready_s=([0-9.]+)$", re.M)
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def run(version: str, rep: int) -> dict:
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OUT.mkdir(parents=True, exist_ok=True)
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log = OUT / f"{version}-r{rep}.log"
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camera = ",".join(f"{v:.12f}" for v in CAMERA) + ",0,0"
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command = [
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"taskset", "-c", "0-11", "prlimit", "--as=6442450944", "--",
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"env", "VOXELFORGE_L81_PASSAGE_BOUND_PADDING_VOXELS=0", "timeout", "1740s",
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str(BINARIES[version]), str(DATA), "--world-assembler-prototype",
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"--stream-benchmark", "--stream-pipeline", "--workers=8", "--clip-radius=3",
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"--max-clip-level=8", "--max-level=2", f"--camera={camera}",
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"--profile-lod-work",
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]
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start = time.monotonic()
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with log.open("w", encoding="utf-8") as stream:
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result = subprocess.run(command, cwd=ROOT, stdout=stream,
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stderr=subprocess.STDOUT, check=False, timeout=1740)
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output = log.read_text(encoding="utf-8", errors="replace")
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pipeline_match, near_match = PIPELINE.search(output), NEAR.search(output)
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if result.returncode or pipeline_match is None:
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raise RuntimeError(f"TN run failed: exit={result.returncode} {log}")
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pipeline = dict(FIELDS.findall(pipeline_match.group(1)))
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whole_tiles = int(pipeline["whole_tiles"].split("/")[0])
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if near_match is not None:
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lod02_ready_s = float(near_match.group(3))
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lod02_tiles = int(near_match.group(1))
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else:
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# 90378a5 predates the explicit near-LOD milestone. With max-level=2,
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# the complete 0-2 view is the whole-view milestone.
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lod02_ready_s = float(pipeline["whole_view_ready"].removesuffix("s"))
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lod02_tiles = whole_tiles
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if lod02_tiles != 1344 or whole_tiles != 1344:
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raise RuntimeError(f"TN tile scope mismatch: lod02={lod02_tiles} whole={whole_tiles} {log}")
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row = {
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"version": version, "pair": rep, "exit_code": result.returncode,
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"wall_s": round(time.monotonic() - start, 3),
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"lod02_ready_s": lod02_ready_s,
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"whole_view_ready_s": float(pipeline["whole_view_ready"].removesuffix("s")),
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"whole_tiles": whole_tiles,
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"log": str(log.relative_to(ROOT)),
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}
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print("[L82TNRegressionRun] " + json.dumps(row, sort_keys=True), flush=True)
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return row
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def main() -> int:
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for path in (*BINARIES.values(), DATA):
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if not path.is_file():
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raise SystemExit(f"missing input: {path}")
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rows = []
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for rep in range(1, 6):
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order = ("90378a5", "9cb5e53") if rep % 2 else ("9cb5e53", "90378a5")
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for version in order:
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rows.append(run(version, rep))
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(OUT / "runs-partial.json").write_text(json.dumps(rows, indent=2) + "\n",
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encoding="utf-8")
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by_pair = {rep: {row["version"]: row for row in rows if row["pair"] == rep}
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for rep in range(1, 6)}
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paired_ready_delta = [by_pair[rep]["9cb5e53"]["lod02_ready_s"]
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- by_pair[rep]["90378a5"]["lod02_ready_s"]
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for rep in range(1, 6)]
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paired_whole_delta = [by_pair[rep]["9cb5e53"]["whole_view_ready_s"]
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- by_pair[rep]["90378a5"]["whole_view_ready_s"]
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for rep in range(1, 6)]
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summary = {
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"pairs": 5,
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"90378a5_lod02_median_s": statistics.median(
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row["lod02_ready_s"] for row in rows if row["version"] == "90378a5"),
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"9cb5e53_lod02_median_s": statistics.median(
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row["lod02_ready_s"] for row in rows if row["version"] == "9cb5e53"),
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"paired_lod02_delta_s": paired_ready_delta,
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"paired_lod02_delta_median_s": statistics.median(paired_ready_delta),
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"90378a5_whole_median_s": statistics.median(
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row["whole_view_ready_s"] for row in rows if row["version"] == "90378a5"),
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"9cb5e53_whole_median_s": statistics.median(
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row["whole_view_ready_s"] for row in rows if row["version"] == "9cb5e53"),
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"paired_whole_delta_s": paired_whole_delta,
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"paired_whole_delta_median_s": statistics.median(paired_whole_delta),
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"scheduling": "both binaries used their default scheduler settings; 90378a5 predates the policy flag",
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"runs": rows,
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}
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(OUT / "summary.json").write_text(json.dumps(summary, indent=2) + "\n",
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encoding="utf-8")
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print("[L82TNRegressionSummary] " + json.dumps(
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{k: v for k, v in summary.items() if k != "runs"}, sort_keys=True), flush=True)
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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