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Reports coverage, score, question-type and dataset breakdowns, response/prompt/token
lengths, latency, limit/forced rates, spatial-code size for B/C, score relationships,
and pairwise deltas on exact question intersections. Stored per-question scores are
used directly; ``mean_score`` is not the category-weighted official VSI overall.
"""
from __future__ import annotations
import argparse
import json
import os
import math
import statistics
import random
from collections import Counter, defaultdict
from itertools import combinations
from pathlib import Path
ROOT = Path(__file__).resolve().parent.parent
DEFAULT_DIRS = {h: Path("/root/results") / h for h in "ABCE"}
NUMERIC_FIELDS = (
"input_token_count",
"output_token_count",
"reasoning_token_count",
"generation_seconds",
"forced_input_token_count",
)
TEXT_FIELDS = (
"answer_given",
"answer_raw",
"reasoning_text",
"full_prompt",
"rendered_prompt",
)
def iter_records(directory):
root = Path(directory)
if not root.is_dir():
return
for path in sorted(root.rglob("*.json")):
try:
with path.open(encoding="utf-8") as stream:
record = json.load(stream)
except (OSError, json.JSONDecodeError):
continue
if (
isinstance(record, dict)
and "question_id" in record
and "condition" in record
):
yield record
def protocol_selected(protocol, selectors):
if protocol is None:
return not selectors
return not selectors or any(
protocol == item or ("/" not in item and protocol.startswith(item + "/"))
for item in selectors
)
def cell_identity(harness, record):
protocol = record.get("protocol") or record["condition"].split(":", 1)[0]
selection = record.get("frame_selection", record.get("input_selection"))
common = {
"harness": harness,
"model": record.get("model"),
"protocol": protocol,
"selection": selection,
"frames": str(record.get("frame_count")),
}
if harness in ("B", "C"):
common.update(
{
"format": record.get("spatial_code_format"),
"depth": record.get("depth"),
"tracking": record.get("tracking"),
}
)
return tuple(sorted(common.items()))
def identity_dict(identity):
return dict(identity)
def cell_label(identity):
d = identity_dict(identity)
parts = [
d["harness"],
d.get("model"),
d.get("protocol"),
d.get("selection"),
d.get("frames"),
]
if d["harness"] in ("B", "C"):
parts += [d.get("format"), d.get("depth"), d.get("tracking")]
return "/".join("?" if value is None else str(value) for value in parts)
def comparison_key(identity):
d = identity_dict(identity)
return d.get("model"), d.get("protocol"), d.get("selection"), d.get("frames")
def _numbers(records, getter):
out = []
for record in records:
value = getter(record)
if (
isinstance(value, (int, float))
and not isinstance(value, bool)
and math.isfinite(value)
):
out.append(float(value))
return out
def numeric_summary(values):
values = sorted(values)
if not values:
return None
def percentile(p):
position = (len(values) - 1) * p
low, high = math.floor(position), math.ceil(position)
if low == high:
return values[low]
return values[low] + (values[high] - values[low]) * (position - low)
return {
"n": len(values),
"mean": statistics.mean(values),
"median": statistics.median(values),
"min": values[0],
"p25": percentile(0.25),
"p75": percentile(0.75),
"max": values[-1],
"stdev": statistics.stdev(values) if len(values) > 1 else 0.0,
}
def pearson(xs, ys):
pairs = [
(float(x), float(y))
for x, y in zip(xs, ys)
if isinstance(x, (int, float))
and isinstance(y, (int, float))
and not isinstance(x, bool)
and not isinstance(y, bool)
and math.isfinite(x)
and math.isfinite(y)
]
if len(pairs) < 2:
return None
x, y = zip(*pairs)
mx, my = statistics.mean(x), statistics.mean(y)
dx, dy = [v - mx for v in x], [v - my for v in y]
denom = math.sqrt(sum(v * v for v in dx) * sum(v * v for v in dy))
return sum(a * b for a, b in zip(dx, dy)) / denom if denom else None
def spatial_code_bytes(record, cache):
path = record.get("spatial_code_path")
if not path:
return None
if path not in cache:
try:
cache[path] = Path(path).stat().st_size
except OSError:
cache[path] = None
return cache[path]
def breakdown(records, field):
groups = defaultdict(list)
for record in records:
groups[str(record.get(field) or "<missing>")].append(record)
return {
name: {
"count": len(group),
"mean_score": (
numeric_summary(_numbers(group, lambda r: r.get("score")))["mean"]
if _numbers(group, lambda r: r.get("score"))
else None
),
"scenes": len({r.get("scene") for r in group}),
}
for name, group in sorted(groups.items())
}
def summarize_cell(records, code_cache):
scores = _numbers(records, lambda r: r.get("score"))
numeric = {
field: numeric_summary(_numbers(records, lambda r, f=field: r.get(f)))
for field in NUMERIC_FIELDS
}
text = {
field
+ "_chars": numeric_summary(
_numbers(
records,
lambda r, f=field: len(r[f]) if isinstance(r.get(f), str) else None,
)
)
for field in TEXT_FIELDS
}
code_sizes = _numbers(records, lambda r: spatial_code_bytes(r, code_cache))
relationships = {}
measures = {
**{field: lambda r, f=field: r.get(f) for field in NUMERIC_FIELDS},
**{
field
+ "_chars": lambda r, f=field: (
len(r[f]) if isinstance(r.get(f), str) else None
)
for field in TEXT_FIELDS
},
"spatial_code_bytes": lambda r: spatial_code_bytes(r, code_cache),
}
for name, getter in measures.items():
pairs = [(r.get("score"), getter(r)) for r in records]
relationships["score_vs_" + name] = pearson(
[p[1] for p in pairs], [p[0] for p in pairs]
)
return {
"questions": len(records),
"unique_question_ids": len({r["question_id"] for r in records}),
"scenes": len({r.get("scene") for r in records}),
"mean_score": statistics.mean(scores) if scores else None,
"score_distribution": numeric_summary(scores),
"question_types": breakdown(records, "question_type"),
"datasets": breakdown(records, "dataset"),
"numeric": numeric,
"text_lengths": text,
"rates": {
"hit_token_limit": (
statistics.mean(bool(r.get("hit_token_limit")) for r in records)
if records
else None
),
"reasoning_hit_limit": (
statistics.mean(bool(r.get("reasoning_hit_limit")) for r in records)
if records
else None
),
"reasoning_present": (
statistics.mean(
bool(r.get("reasoning_text") or r.get("reasoning_raw"))
for r in records
)
if records
else None
),
"forced": (
statistics.mean(bool(r.get("forced")) for r in records)
if records
else None
),
"scored": len(scores) / len(records) if records else None,
},
"spatial_codes": {
"records_with_path": sum(bool(r.get("spatial_code_path")) for r in records),
"unique_paths": len(
{
r.get("spatial_code_path")
for r in records
if r.get("spatial_code_path")
}
),
"readable_file_bytes": numeric_summary(code_sizes),
},
"relationships": relationships,
}
def paired_breakdown(x, y, common, field):
groups = defaultdict(list)
for qid in common:
name = str(x[qid].get(field) or y[qid].get(field) or "<missing>")
groups[name].append(y[qid].get("score") - x[qid].get("score"))
return {
name: {"count": len(vals), "mean_delta": statistics.mean(vals)}
for name, vals in sorted(groups.items())
if vals
}
def _scene_bootstrap(x, y, common, iterations=1000, seed=0):
by_scene = defaultdict(list)
for qid in common:
by_scene[str(x[qid].get("scene") or y[qid].get("scene") or "<missing>")].append(
y[qid]["score"] - x[qid]["score"]
)
if not by_scene:
return {
"scenes": 0,
"iterations": iterations,
"ci_low": None,
"ci_high": None,
"p_value": None,
}
scenes = sorted(by_scene)
rng = random.Random(seed)
draws = []
for _ in range(iterations):
values = []
for _ in scenes:
values.extend(by_scene[rng.choice(scenes)])
draws.append(statistics.mean(values))
draws.sort()
low = int(0.025 * iterations)
high = min(iterations - 1, int(0.975 * iterations))
below = sum(v <= 0 for v in draws) / iterations
above = sum(v >= 0 for v in draws) / iterations
return {
"scenes": len(scenes),
"iterations": iterations,
"seed": seed,
"confidence": 0.95,
"ci_low": draws[low],
"ci_high": draws[high],
"p_value": max(1 / iterations, min(1.0, 2 * min(below, above))),
}
def paired_report(x_records, y_records):
x = {
r["question_id"]: r
for r in x_records
if isinstance(r.get("score"), (int, float))
}
y = {
r["question_id"]: r
for r in y_records
if isinstance(r.get("score"), (int, float))
}
common = sorted(set(x) & set(y))
deltas = [y[q]["score"] - x[q]["score"] for q in common]
solved_x = {q for q in common if x[q]["score"] >= 1.0}
solved_y = {q for q in common if y[q]["score"] >= 1.0}
union = solved_x | solved_y
telemetry = {}
for field in NUMERIC_FIELDS:
vals = [
y[q].get(field) - x[q].get(field)
for q in common
if isinstance(x[q].get(field), (int, float))
and isinstance(y[q].get(field), (int, float))
]
telemetry[field + "_delta"] = numeric_summary(vals)
return {
"common_questions": len(common),
"x_full_questions": len(x),
"y_full_questions": len(y),
"mean_score_delta_y_minus_x": statistics.mean(deltas) if deltas else None,
"score_delta_distribution": numeric_summary(deltas),
"wins_y": sum(d > 0 for d in deltas),
"ties": sum(d == 0 for d in deltas),
"wins_x": sum(d < 0 for d in deltas),
"scene_clustered_bootstrap": _scene_bootstrap(x, y, common),
"solved_overlap": {
"x": len(solved_x),
"y": len(solved_y),
"both": len(solved_x & solved_y),
"only_x": len(solved_x - solved_y),
"only_y": len(solved_y - solved_x),
"jaccard": len(solved_x & solved_y) / len(union) if union else None,
},
"by_question_type": paired_breakdown(x, y, common, "question_type"),
"by_dataset": paired_breakdown(x, y, common, "dataset"),
"telemetry_deltas": telemetry,
}
def analyze(directories=None, protocols=()):
directories = directories or DEFAULT_DIRS
cells = defaultdict(list)
for harness, directory in directories.items():
for record in iter_records(directory):
protocol = record.get("protocol") or record["condition"].split(":", 1)[0]
if protocol_selected(protocol, protocols):
cells[cell_identity(harness, record)].append(record)
code_cache = {}
report = {"cells": {}, "comparison_groups": {}}
for identity, records in cells.items():
report["cells"][cell_label(identity)] = {
"identity": identity_dict(identity),
"summary": summarize_cell(records, code_cache),
}
grouped = defaultdict(list)
for identity in cells:
grouped[comparison_key(identity)].append(identity)
for key, identities in grouped.items():
name = "/".join("?" if v is None else str(v) for v in key)
pairs = {}
for first, second in combinations(sorted(identities, key=cell_label), 2):
pairs[cell_label(first) + " -> " + cell_label(second)] = paired_report(
cells[first], cells[second]
)
id_sets = [{r["question_id"] for r in cells[i]} for i in identities]
report["comparison_groups"][name] = {
"cells": [cell_label(i) for i in identities],
"all_cell_common_questions": (
len(set.intersection(*id_sets)) if id_sets else 0
),
"pairwise": pairs,
}
return report
def main():
parser = argparse.ArgumentParser()
for harness in "abc":
parser.add_argument(f"--{harness}-results-dir", default=None)
parser.add_argument(
"--protocol",
action="append",
default=[],
help="repeatable; select base or thinking protocol families",
)
parser.add_argument(
"--output-dir",
default=str(ROOT / "reports"),
help="report directory (default: workspace/reports)",
)
parser.add_argument(
"--json-out",
default=None,
help="override the JSON report path (default: <output-dir>/comprehensive.json)",
)
args = parser.parse_args()
dirs = {
h.upper(): Path(getattr(args, f"{h}_results_dir") or DEFAULT_DIRS[h.upper()])
for h in "abc"
}
report = analyze(dirs, args.protocol)
text = json.dumps(report, indent=1)
output_path = (
Path(args.json_out)
if args.json_out
else Path(args.output_dir) / "comprehensive.json"
)
output_path.parent.mkdir(parents=True, exist_ok=True)
output_path.write_text(text + "\n", encoding="utf-8")
print(f"wrote {output_path}")
# --- Modular profile-driven interface (v2) ---
# Built-in, versioned harness profiles.
PROFILE_VERSION = 1
BUILTINS = {
"A": {
"letter": "A",
"kind": "vlm",
"input_source": "frames",
"axes": ["model", "protocol", "selection", "frames"],
"capabilities": ["tokens", "latency", "reasoning", "frames"],
},
"B": {
"letter": "B",
"kind": "vlm",
"input_source": "perceived",
"axes": [
"model",
"protocol",
"format",
"depth",
"tracking",
"selection",
"frames",
],
"capabilities": ["tokens", "latency", "reasoning", "spatial_code"],
},
"C": {
"letter": "C",
"kind": "vlm",
"input_source": "frames_perceived",
"axes": [
"model",
"protocol",
"format",
"depth",
"tracking",
"selection",
"frames",
],
"capabilities": ["tokens", "latency", "reasoning", "frames", "spatial_code"],
},
"F": {
"letter": "F",
"kind": "solver",
"input_source": "dynamic",
"axes": [
"source",
"depth",
"tracking",
"selection",
"frames",
"format",
"spatial_code_model",
],
"capabilities": ["spatial_code", "solver"],
},
}
def validate_profile(profile):
p = dict(profile)
letter = str(p.get("letter", "")).upper()
if len(letter) != 1 or not letter.isalpha():
raise ValueError("profile letter must be one alphabetic character")
p["letter"] = letter
p.setdefault("kind", "generic")
p.setdefault("input_source", "unknown")
p.setdefault("axes", ["model", "protocol"])
p.setdefault("capabilities", [])
p["profile_version"] = PROFILE_VERSION
return p
def load_profile(letter, path=None):
letter = letter.upper()
if path:
p = json.loads(Path(path).read_text())
p.setdefault("letter", letter)
if p["letter"].upper() != letter:
raise ValueError(f"profile letter mismatch for {letter}")
return validate_profile(p)
return validate_profile(
BUILTINS.get(
letter,
{
"letter": letter,
"kind": "generic",
"input_source": "unknown",
"axes": [
"model",
"protocol",
"format",
"depth",
"tracking",
"selection",
"frames",
],
},
)
)
ANALYSIS_VERSION = 2
def discover_records(letter, directory, profile, protocols=(), spatial_codes_dir=None):
root = Path(directory)
records = []
warnings = []
if not root.is_dir():
return records, [{"code": "missing_directory", "path": str(root)}]
for path in sorted(root.rglob("*.json")):
if path.name.startswith("_"):
continue
try:
record = json.loads(path.read_text(encoding="utf-8"))
except (OSError, json.JSONDecodeError) as exc:
warnings.append(
{"code": "unreadable_json", "path": str(path), "detail": str(exc)}
)
continue
if (
not isinstance(record, dict)
or record.get("question_id") is None
or record.get("score") is None
):
warnings.append({"code": "not_question_record", "path": str(path)})
continue
record = dict(record)
record["_result_path"] = str(path)
record["_relative_path"] = path.relative_to(root).parts
record = _normalize_record(letter, record, profile)
code_path = record.get("spatial_code_path")
if code_path and not Path(code_path).is_file() and spatial_codes_dir:
marker = "spatial codes/"
suffix = (
str(code_path).split(marker, 1)[-1]
if marker in str(code_path)
else None
)
candidate = Path(spatial_codes_dir) / suffix if suffix else None
if candidate and candidate.is_file():
record["spatial_code_path"] = str(candidate)
else:
warnings.append(
{
"code": "unresolved_spatial_code_path",
"path": str(path),
"recorded_path": str(code_path),
}
)
if letter != "F" and not protocol_selected(record.get("protocol"), protocols):
continue
records.append(record)
return records, warnings
def _normalize_record(letter, r, profile):
r["format"] = r.get("spatial_code_format") or r.get("format")
r["selection"] = (
r.get("frame_selection") or r.get("input_selection") or r.get("input")
)
r["frames"] = r.get("frame_count") or r.get("number_of_frames")
if not r.get("protocol") and r.get("condition") and letter != "F":
r["protocol"] = r["condition"].split(":", 1)[0]
if letter == "F":
parts = list(r.get("_relative_path", ()))
top = parts[0].lower() if parts else ""
r["source"] = "perceived"
offset = 1
if top == "perceived":
r["depth"] = r.get("depth") or (parts[1] if len(parts) > 1 else None)
offset = 2
elif top in ("metric", "relative"):
r["depth"] = r.get("depth") or top
r["tracking"] = r.get("tracking") or (
parts[offset] if len(parts) > offset else None
)
r["selection"] = r.get("selection") or (
parts[offset + 1] if len(parts) > offset + 1 else None
)
r["frames"] = r.get("frames") or (
parts[offset + 2] if len(parts) > offset + 2 else None
)
candidate = parts[offset + 3] if len(parts) > offset + 3 else None
if candidate and not candidate.startswith("scene") and len(candidate) != 10:
r["format"] = r.get("format") or candidate
r["spatial_code_model"] = r.get("spatial_code_model")
r["protocol"] = None
return r
def modular_identity(letter, record, profile):
values = {"harness": letter}
for axis in profile["axes"]:
values[axis] = str(record.get(axis)) if record.get(axis) is not None else None
return tuple(sorted(values.items()))
def modular_label(identity):
d = dict(identity)
return "/".join(
[d.pop("harness")] + [f"{k}={v or '?'}" for k, v in sorted(d.items())]
)
def _controlled(first, second, profile):
a, b = dict(first), dict(second)
diffs = [axis for axis in profile["axes"] if a.get(axis) != b.get(axis)]
return len(diffs) == 1, diffs
def _compatible(a, b, profiles):
x, y = dict(a), dict(b)
lx, ly = x["harness"], y["harness"]
warnings = []
if lx == ly:
return False, [], ["same_harness"]
# F source semantics.
f = x if lx == "F" else y if ly == "F" else None
other = y if lx == "F" else x
if f:
expected = "perceived" if other["harness"] in ("B", "C") else None
if expected and f.get("source") != expected:
return False, [], ["incompatible_F_source"]
shared = []
for axis in ("model", "format", "depth", "tracking", "selection", "frames"):
av, bv = x.get(axis), y.get(axis)
if axis == "model" and f:
continue
if av is not None and bv is not None:
if av != bv:
return False, [], [f"conflicting_{axis}"]
shared.append(axis)
else:
warnings.append(f"unmatched_{axis}")
if not f and x.get("protocol") is not None and y.get("protocol") is not None:
if x["protocol"] != y["protocol"]:
return False, [], ["conflicting_protocol"]
shared.append("protocol")
return True, shared, warnings
def _generated_at():
return os.environ.get("VSI_ANALYSIS_GENERATED_AT", "reproducible")
def analyze_modular(
cells, profiles, protocols=(), requested_pairs=(), spatial_codes_dir=None
):
all_cells = defaultdict(list)
warnings = {}
sources = {}
for letter, directory in cells.items():
recs, warns = discover_records(
letter, directory, profiles[letter], protocols, spatial_codes_dir
)
warnings[letter] = warns
sources[letter] = str(directory)
for r in recs:
all_cells[modular_identity(letter, r, profiles[letter])].append(r)
cache = {}
per = {
letter: {
"manifest": {
"analysis_version": ANALYSIS_VERSION,
"profile_version": PROFILE_VERSION,
"generated_at": _generated_at(),
"letter": letter,
"profile": profiles[letter],
"source": sources[letter],
"protocols": list(protocols),
},
"cells": {},
"within_harness_comparisons": {},
"integrity_warnings": warnings[letter],
}
for letter in cells
}
for ident, recs in all_cells.items():
per[dict(ident)["harness"]]["cells"][modular_label(ident)] = {
"identity": dict(ident),
"summary": summarize_cell(recs, cache),
}
for letter in cells:
ids = [i for i in all_cells if dict(i)["harness"] == letter]
for a, b in combinations(ids, 2):
ok, diffs = _controlled(a, b, profiles[letter])
if ok:
per[letter]["within_harness_comparisons"][
modular_label(a) + " -> " + modular_label(b)
] = {
"varied_axis": diffs[0],
**paired_report(all_cells[a], all_cells[b]),
}
allowed = {tuple(sorted(p)) for p in requested_pairs}
cross = {}
ids = list(all_cells)
for a, b in combinations(ids, 2):
letters = tuple(sorted((dict(a)["harness"], dict(b)["harness"])))
if letters[0] == letters[1] or (allowed and letters not in allowed):
continue
ok, shared, warns = _compatible(a, b, profiles)
if ok:
cross[modular_label(a) + " -> " + modular_label(b)] = {
"letters": letters,
"shared_axes": shared,
"alignment_warnings": warns,
**paired_report(all_cells[a], all_cells[b]),
}
manifest = {
"analysis_version": ANALYSIS_VERSION,
"profile_version": PROFILE_VERSION,
"generated_at": _generated_at(),
"letters": sorted(cells),
"sources": sources,
"protocols": list(protocols),
"requested_pairs": [":".join(p) for p in requested_pairs],
}
return per, {
"manifest": manifest,
"cross_harness_comparisons": cross,
"harness_summaries": {
l: {
"cell_count": len(per[l]["cells"]),
"warning_count": len(per[l]["integrity_warnings"]),
}
for l in per
},
}
def parse_assignment(value, option):
if "=" not in value:
raise argparse.ArgumentTypeError(f"{option} must be LETTER=PATH")
letter, path = value.split("=", 1)
letter = letter.upper()
if len(letter) != 1 or not letter.isalpha() or letter == "D":
raise argparse.ArgumentTypeError(
"letter must be one alphabetic character other than D"
)
return letter, path
def export_reports(per, combined, output_dir):
out = Path(output_dir)
out.mkdir(parents=True, exist_ok=True)
paths = []
for letter, report in sorted(per.items()):
path = out / f"{letter}_report.json"
path.write_text(json.dumps(report, indent=1) + "\n")
paths.append(path)
if len(per) > 1:
name = "".join(sorted(per)) + "_report.json"
path = out / name
path.write_text(json.dumps(combined, indent=1) + "\n")
paths.append(path)
return paths
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--cell",
action="append",
default=[],
help="repeatable LETTER=PATH; D is removed",
)
parser.add_argument(
"--profile", action="append", default=[], help="optional LETTER=profile.json"
)
parser.add_argument(
"--compare",
action="append",
default=[],
help="optional pair restriction, e.g. A:B",
)
parser.add_argument(
"--protocol",
action="append",
default=[],
help="repeatable; select base or thinking protocol families",
)
parser.add_argument("--output-dir", default=str(ROOT / "reports"))
parser.add_argument(
"--spatial-codes-dir",
default=None,
help="optional local root used to rebase stale recorded code paths",
)
for h in "abce":
parser.add_argument(f"--{h}-results-dir", default=None, help=argparse.SUPPRESS)
args = parser.parse_args()
cells = dict(parse_assignment(v, "--cell") for v in args.cell)
for h in "abce":
value = getattr(args, f"{h}_results_dir")
if value:
cells[h.upper()] = value
if not cells:
parser.error("provide at least one --cell LETTER=PATH")
profile_paths = dict(parse_assignment(v, "--profile") for v in args.profile)
profiles = {
letter: load_profile(letter, profile_paths.get(letter)) for letter in cells
}
pairs = []
for value in args.compare:
bits = [x.upper() for x in value.split(":")]
if len(bits) != 2 or any(x not in cells for x in bits):
parser.error(f"invalid --compare {value}")
pairs.append(tuple(bits))
per, combined = analyze_modular(
cells, profiles, args.protocol, pairs, args.spatial_codes_dir
)
for path in export_reports(per, combined, args.output_dir):
print(f"wrote {path}")
# Consolidated analysis helpers formerly split across stats/solvability/sufficiency/audits.
def _official_scores(records):
records = list(records)
try:
import importlib.util, os
path = os.environ.get(
"HARNESS_OFFICIAL_EVAL",
"/root/data/thinking-in-space/lmms_eval/tasks/vsibench/utils.py",
)
spec = importlib.util.spec_from_file_location(
"analysis_vsi_official_eval", path
)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
docs = [
{
"question_type": r["question_type"],
"ground_truth": r.get("answer_expected"),
r["metric"]: r["score"],
}
for r in records
]
return module.vsibench_aggregate_results(docs)
except (OSError, ImportError, AttributeError, TypeError):
scores = [
r.get("score") for r in records if isinstance(r.get("score"), (int, float))
]
return {
"overall": statistics.mean(scores) * 100 if scores else None,
"scoring_mode": "stored_per_question_mean_fallback",
}
def holm_bonferroni(p_values):
ordered = sorted(p_values.items(), key=lambda item: item[1])
total = len(ordered)
out = {}
running = 0.0
for rank, (name, p) in enumerate(ordered):
running = max(running, min(1.0, (total - rank) * p))
out[name] = running
return out
def solved_set_overlap(cells, threshold=1.0):
maps = {
name: {r["question_id"]: r.get("score") for r in records}
for name, records in cells.items()
}
common = set.intersection(*(set(m) for m in maps.values())) if maps else set()
solved = {
n: {q for q in common if v[q] is not None and v[q] >= threshold}
for n, v in maps.items()
}
pairs = {}
for a, b in combinations(sorted(solved), 2):
union = solved[a] | solved[b]
pairs[f"{a}|{b}"] = {
"jaccard": len(solved[a] & solved[b]) / len(union) if union else None,
"both": len(solved[a] & solved[b]),
f"only_{a}": len(solved[a] - solved[b]),
f"only_{b}": len(solved[b] - solved[a]),
}
return {
"questions": len(common),
"solved": {n: len(v) for n, v in solved.items()},
"pairs": pairs,
}
def sufficiency_decomposition(vlm_records, solver_records, threshold=1.0, exclude=()):
cert = {
r["question_id"]: r.get("score") is not None and r["score"] >= threshold
for r in solver_records
}
buckets = {"certified": [], "uncertified": []}
for r in vlm_records:
if r.get("question_type") in set(exclude) or r.get("question_id") not in cert:
continue
buckets["certified" if cert[r["question_id"]] else "uncertified"].append(
r.get("score")
)
def summary(vals):
valid = [v for v in vals if isinstance(v, (int, float))]
correct = sum(v >= threshold for v in valid)
return {
"count": len(vals),
"mean_score": statistics.mean(valid) if valid else None,
"vlm_correct": correct,
"vlm_wrong": len(vals) - correct,
}
return {name: summary(vals) for name, vals in buckets.items()}
def solver_depth_table(records):
try:
from symbolic import adapters, solver
except ImportError:
return {
"status": "unavailable",
"reason": "symbolic solver imports unavailable",
}
cache = {}
buckets = defaultdict(list)
for r in records:
path = r.get("spatial_code_path")
if not path:
continue
try:
if path not in cache:
cache[path] = adapters.adapt_spatial_code(
json.loads(Path(path).read_text())
)
solver.answer(
r["question_type"], r["question"], r.get("options"), cache[path]
)
depth = solver.LAST_ANSWER_OPS.get("total")
except (OSError, KeyError, ValueError):
continue
if depth is not None and isinstance(r.get("score"), (int, float)):
buckets[
(
"0-2"
if depth <= 2
else "3-8" if depth <= 8 else "9-20" if depth <= 20 else "21-inf"
)
].append((depth, r["score"]))
return {
k: {
"count": len(v),
"mean_depth": statistics.mean(x for x, _ in v),
"mean_score": statistics.mean(y for _, y in v),
}
for k, v in buckets.items()
}
_NUMBER_RE = __import__("re").compile(r"[-+]?\d+(?:\.\d+)?")
def deterministic_cot_audit(records, tolerance=0.01):
def nums(value):
return [float(x) for x in _NUMBER_RE.findall(str(value or ""))]
audits = []
cache = {}
for r in records:
reasoning = r.get("reasoning_text")
path = r.get("spatial_code_path")
if not reasoning or not path:
continue
try:
if path not in cache:
cache[path] = nums(Path(path).read_text())
except OSError:
continue
sources = (
cache[path]
+ nums(r.get("question"))
+ sum((nums(x) for x in r.get("options") or []), [])
)
cited = nums(reasoning)
fabricated = [
v
for v in cited
if not (abs(v) <= 12 and v.is_integer())
and not any(abs(v - x) <= tolerance * max(1, abs(x)) for x in sources)
]
audits.append(
{
"question_id": r["question_id"],
"score": r.get("score"),
"cited": len(cited),
"fabricated": len(fabricated),
}
)
wrong = [a for a in audits if a["score"] is not None and a["score"] < 1]
bad = [a for a in wrong if a["fabricated"]]
return {
"audited": len(audits),
"wrong": len(wrong),
"wrong_with_fabrication": len(bad),
"fabrication_share_of_wrong": len(bad) / len(wrong) if wrong else None,
}
def generate_letter(
letter,
results_dir,
protocols=(),
output_dir=None,
spatial_codes_dir=None,
profile_path=None,
):
letter = letter.upper()
profile = load_profile(letter, profile_path)
per, combined = analyze_modular(
{letter: Path(results_dir)}, {letter: profile}, protocols, (), spatial_codes_dir
)
paths = export_reports(per, combined, output_dir or ROOT / "reports")
return {"report": per[letter], "path": paths[0]}
def generate(
cells,
protocols=(),
comparisons=(),
output_dir=None,
profile_paths=None,
spatial_codes_dir=None,
):
normalized = {str(k).upper(): Path(v) for k, v in cells.items()}
profile_paths = {str(k).upper(): v for k, v in (profile_paths or {}).items()}
profiles = {l: load_profile(l, profile_paths.get(l)) for l in normalized}
pairs = []
for pair in comparisons:
pair = tuple(
x.upper() for x in (pair.split(":") if isinstance(pair, str) else pair)
)
if len(pair) != 2 or any(x not in normalized for x in pair):
raise ValueError(f"invalid comparison {pair}")
pairs.append(pair)
per, combined = analyze_modular(
normalized, profiles, protocols, pairs, spatial_codes_dir
)
paths = export_reports(per, combined, output_dir or ROOT / "reports")
return {"letter_reports": per, "combined_report": combined, "paths": paths}
def main():
parser = argparse.ArgumentParser(
description="Generate arbitrary mixed letter reports; D is removed."
)
parser.add_argument("--cell", action="append", required=True)
parser.add_argument("--profile", action="append", default=[])
parser.add_argument("--compare", action="append", default=[])
parser.add_argument("--protocol", action="append", default=[])
parser.add_argument("--output-dir", default=str(ROOT / "reports"))
parser.add_argument("--spatial-codes-dir", default=None)
args = parser.parse_args()
cells = dict(parse_assignment(v, "--cell") for v in args.cell)
profiles = dict(parse_assignment(v, "--profile") for v in args.profile)
try:
result = generate(
cells,
args.protocol,
args.compare,
args.output_dir,
profiles,
args.spatial_codes_dir,
)
except ValueError as exc:
parser.error(str(exc))
for path in result["paths"]:
print(f"wrote {path}")
if __name__ == "__main__":
main()
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