code/bench/report.py · nandukmelath/nirnay at main

Nirnay v3.1 is the new default: request routing 0.40 -> 0.88, SMS spam, emotion, toxicity, RAG up; previous default moved to v2/

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"""Merge benchmark results into REPORT.md (Nirnay models vs Laya measured here, Jev as published).
  python bench\\report.py
  REPORT_NIR="nirnay-v3,nirnay-v2,nirnay-v1" python bench\\report.py      (first model = the headline column)
Models without a results file are left out. The multilingual section appears once
results/suites_multi__*.json exist (MASSIVE, 51 languages; SIB-200, 205 languages).
"""
import json
import os
import statistics
import time

HERE = os.path.dirname(os.path.abspath(__file__))
ROOT = os.path.dirname(HERE)
RES = os.path.join(HERE, "results")
OUT = os.path.join(ROOT, os.environ.get("REPORT_OUT", "REPORT.md"))

# Published Jev figures (TypeSafe's closed API; not runnable here). Source: Laya repo
# research/scripts/bench_apps.py, which cites AbdelStark/jev-benchmarks v0.1.0 (n=100 each)
# and nibzard/decision-model-benchmark.
JEV = {"jev.ag_news": 0.910, "jev.banking77_full": 0.870, "jev.emotion": 0.480, "extra.sms_spam": 0.930}
JEV_NOTE = {"jev.banking77_full": "72 labels, n=100; independent run 0.763",
            "extra.sms_spam": "independent run, different sample"}
LAYA = [("laya", "laya (en)"), ("laya-multilingual", "laya-ml"), ("laya-typed-decisions", "laya-td")]
MODELS = {   # key -> (column label, result-file tag, description)
    "nirnay-v3": ("Nirnay v3", "v3", "bge-base-en-v1.5, deep fusion; 324k questions: v2's mix plus request routing, short-message "
                                     "spam, request safety (NVIDIA Aegis 2.0), urgency and more phrasings of every question "
                                     "template; warm-started from v2 for 1.25 more epochs on both CPU sockets."),
    "nirnay-multilingual": ("Nirnay multilingual", "ml", "multilingual-e5-base (100 languages), deep fusion; ~380k questions: "
                                                         "v3's mix plus MTOP intents (6 languages), PAWS-X, NLI in 26 "
                                                         "languages, toxicity in 14, language identification, multilingual "
                                                         "reviews and tweets; 1.2 epochs."),
    "nirnay-v31": ("Nirnay v3.1", "v31", "v3 with content-only safety wording (v3's AI-assistant / policy phrasings made it "
                                        "call harmful prompts jailbreaks); same recipe: warm-started from v2, 1.25 epochs."),
    "nirnay-v31-gpu": ("Nirnay v3.1-GPU", "v31gpu", "v3.1's data and architecture, warm-started from v2 for 2 epochs on a "
                                                    "free Kaggle T4 GPU (train/train_gpu.py; frozen lower layers computed live)."),
    "nirnay-v2": ("Nirnay v2", "v2", "bge-base-en-v1.5, deep fusion (top 4 encoder layers trained), 294k questions, 1.6 epochs "
                                     "(overnight, both CPU sockets)."),
    "nirnay-v1": ("Nirnay v1 (fast)", "v1", "bge-small-en-v1.5, same architecture, 221k questions, 1 epoch."),
}
ORDER = ["jev.ag_news", "jev.emotion", "jev.banking77_full", "app.support_triage", "app.email_spam", "app.phishing",
         "app.guardrails_jailbreak", "app.moderation_toxicity", "app.rag_relevance", "app.model_routing_domain",
         "extra.sst5", "extra.sms_spam"]
TIE = 0.005


def jload(name):
    p = os.path.join(RES, name)
    return json.load(open(p, encoding="utf-8")) if os.path.exists(p) else None


def fmt(x, nd=3):
    return "—" if x is None else ("%.*f" % (nd, x))


def acc(d, s):
    return ((d or {}).get("suites", {}).get(s) or {}).get("accuracy")


def tally(mine, theirs):
    w = t = l = 0
    for a, b in zip(mine, theirs):
        if a is None or b is None:
            continue
        if a > b + TIE:
            w += 1
        elif a < b - TIE:
            l += 1
        else:
            t += 1
    return w, t, l


def multilingual_section(keys):
    """MASSIVE + SIB-200 per model; per-language MASSIVE table; per-language wins vs Laya-multilingual."""
    res = {k: jload("suites_multi__nirnay__%s.json" % k) for k in keys}
    res = {k: v for k, v in res.items() if v}
    laya = jload("suites_multi__laya__laya-multilingual.json")
    if not res:   # nothing of ours measured yet: no section (a Laya-only table would compare nothing)
        return []
    cols = [(k, MODELS[k][0]) for k in res] + ([("laya-ml", "Laya multilingual")] if laya else [])
    data = dict(res, **({"laya-ml": laya} if laya else {}))

    def per_lang(d, prefix):
        return {s.split(".", 1)[1]: m["accuracy"] for s, m in (d or {}).get("suites", {}).items() if s.startswith(prefix + ".")}

    L = ["## Multilingual (held out of training for every model)", "",
         "* **MASSIVE** intent detection (mteb/amazon_massive_intent, test split): 51 languages, all 60 intents as options, "
         "80 requests per language, the question in English.",
         "* **SIB-200** topic classification (Davlan/sib200, test split): 205 languages and scripts, 7 topics, the same 24 "
         "FLORES sentences in every language.", "",
         "| benchmark | " + " | ".join(l for _, l in cols) + " |", "|---|" + "---|" * len(cols)]
    for prefix, name in (("massive", "MASSIVE, mean of 51 languages"), ("sib200", "SIB-200, mean of 205 languages")):
        row = []
        for k, _ in cols:
            xs = list(per_lang(data[k], prefix).values())
            row.append(fmt(statistics.mean(xs)) if xs else "—")
        L.append("| %s | %s |" % (name, " | ".join(row)))
    for prefix, name in (("massive", "MASSIVE, worst language"), ("sib200", "SIB-200, worst language")):
        row = []
        for k, _ in cols:
            pl = per_lang(data[k], prefix)
            row.append(("%s (%s)" % (fmt(min(pl.values())), min(pl, key=pl.get))) if pl else "—")
        L.append("| %s | %s |" % (name, " | ".join(row)))
    L.append("")
    if laya and "nirnay-multilingual" in res:
        for prefix, name in (("massive", "MASSIVE"), ("sib200", "SIB-200")):
            a, b = per_lang(res["nirnay-multilingual"], prefix), per_lang(laya, prefix)
            common = sorted(set(a) & set(b))
            w, t, l = tally([a[x] for x in common], [b[x] for x in common])
            L.append("%s, per language, Nirnay multilingual vs Laya multilingual: **%d wins, %d ties, %d losses** "
                     "(of %d languages)." % (name, w, t, l, len(common)))
        L.append("")
    langs = sorted(set().union(*[per_lang(data[k], "massive") for k, _ in cols]))
    if langs:
        L += ["### MASSIVE by language", "", "| language | " + " | ".join(l for _, l in cols) + " |",
              "|---|" + "---|" * len(cols)]
        for lg in langs:
            L.append("| %s | %s |" % (lg, " | ".join(fmt(per_lang(data[k], "massive").get(lg)) for k, _ in cols)))
        L.append("")
    return L


def main():
    want = [k.strip() for k in os.environ.get("REPORT_NIR", "nirnay-v31,nirnay-v3,nirnay-multilingual,nirnay-v2,nirnay-v1").split(",")
            if k.strip()]
    nir = {k: jload("suites__nirnay__%s.json" % k) for k in want}
    NIR = [(k, MODELS[k][0]) for k in want if nir.get(k)]
    head = NIR[0][0]
    lv = ((jload("laya_latency.json") or {}).get("meta") or {}).get("laya", "?")
    lay = {k: jload("suites__laya__%s.json" % k) for k, _ in LAYA}
    L = ["# Nirnay vs Laya vs Jev — measured on this machine", "",
         "Generated %s. Hardware: HP Z840, 2x Xeon E5-2630 v3 (16 cores / 32 threads, AVX2, no AVX-512), "
         "128 GB DDR4, CPU only (Quadro K2200 unused)." % time.strftime("%Y-%m-%d %H:%M"), "",
         "Laya was installed from PyPI (`laya` %s) with its three published checkpoints and run through its own API "
         "with its shipped calibration. Jev is a closed, paid cloud API: it cannot run on this machine, so its column "
         "is the third-party published figure where one exists." % lv, ""]
    L += ["* **%s** (`models\\%s`): %s" % (MODELS[k][0], k, MODELS[k][2]) for k, _ in NIR] + [""]

    # ------------------------------------------------------------- accuracy
    L += ["## Accuracy (same 400 cases per task, same questions, seed 13)", "",
          "| task | " + " | ".join(l for _, l in NIR) + " | " + " | ".join(l for _, l in LAYA) +
          " | best Laya | Jev (published) | Laya trained on it? |",
          "|---|" + "---|" * (len(NIR) + len(LAYA) + 3)]
    heads, bests, ens = [], [], []
    for s in ORDER:
        ls = [acc(lay[k], s) for k, _ in LAYA]
        best = max([x for x in ls if x is not None], default=None)
        meta = ((nir[head] or {}).get("suites", {}).get(s) or {}).get("meta") or {}
        heads.append(acc(nir[head], s))
        bests.append(best)
        ens.append(ls[0])
        jt = JEV.get(s)
        cells = ["**%s**" % fmt(acc(nir[k], s)) if k == head else fmt(acc(nir[k], s)) for k, _ in NIR]
        L.append("| %s | %s | %s | %s | %s | %s |" % (
            s, " | ".join(cells), " | ".join(fmt(x) for x in ls), fmt(best),
            (fmt(jt) + (" (%s)" % JEV_NOTE[s] if s in JEV_NOTE else "")) if jt else "—",
            "yes" if meta.get("laya_in_training") else "no (held out)"))
    hl = MODELS[head][0]
    w, t, l = tally(heads, bests)
    w2, t2, l2 = tally(heads, ens)
    L += ["", "%s vs the best of Laya's three checkpoints (picked per task, after the fact): "
          "**%d wins, %d ties (±%.3f), %d losses**." % (hl, w, t, TIE, l),
          "%s vs Laya's default English checkpoint: **%d wins, %d ties, %d losses**." % (hl, w2, t2, l2)]
    for k, lab in NIR[1:]:
        w3, t3, l3 = tally(heads, [acc(nir[k], s) for s in ORDER])
        L.append("%s vs %s: %d better, %d same, %d worse." % (hl, lab, w3, t3, l3))
    L.append("")

    # ------------------------------------------------------------- calibration
    L += ["## Calibration (ECE, lower is better)", "",
          "| task | " + " | ".join(l for _, l in NIR) + " | best Laya |", "|---|" + "---|" * (len(NIR) + 1)]
    for s in ORDER:
        le = [((lay[k] or {}).get("suites", {}).get(s) or {}).get("ece") for k, _ in LAYA]
        le = [x for x in le if x is not None]
        L.append("| %s | %s | %s |" % (s, " | ".join(fmt(((nir[k] or {}).get("suites", {}).get(s) or {}).get("ece")) for k, _ in NIR),
                                     fmt(min(le) if le else None)))
    L.append("")

    # ------------------------------------------------------------- robustness
    flip = lambda d: [v.get("order_flip_rate") for v in (d or {}).get("suites", {}).values() if v.get("order_flip_rate") is not None]
    avg = lambda xs: (sum(xs) / len(xs)) if xs else None
    cols = [l for _, l in NIR] + [l for _, l in LAYA]
    nm = " | ".join("not measured" for _ in LAYA)
    L += ["## Robustness and determinism", "", "| | " + " | ".join(cols) + " |", "|---|" + "---|" * len(cols)]
    L.append("| option-order flip rate (choice tasks, mean) | %s | %s |" % (
        " | ".join(fmt(avg(flip(nir[k]))) for k, _ in NIR), " | ".join(fmt(avg(flip(lay[k]))) for k, _ in LAYA)))
    L.append("| identical answers on repeat call | %s | %s |" % (
        " | ".join(fmt((nir[k] or {}).get("determinism_repeat_identical")) for k, _ in NIR),
        " | ".join(fmt((lay[k] or {}).get("determinism_repeat_identical")) for k, _ in LAYA)))
    dets = [jload("nirnay_determinism_%s.json" % MODELS[k][1]) for k, _ in NIR]
    L.append("| byte-identical answers across 1/4/8/16 threads | %s | %s |" % (
        " | ".join(fmt((d or {}).get("identical_rate")) for d in dets), nm))
    L.append("| decisions changed across 1/4/8/16 threads | %s | %s |" % (
        " | ".join(("%s (max prob. diff %s)" % (d.get("answer_changes"), d.get("max_probability_diff"))) if d else "—"
                   for d in dets), nm))
    cs = jload("cold_start.json") or {}
    L.append("| cold start: new process -> first answer (s) | %s | %s |" % (
        " | ".join(fmt((cs.get("nirnay:" + k) or {}).get("first_answer_s"), 1) for k, _ in NIR),
        " | ".join(fmt((cs.get("laya:" + k) or {}).get("first_answer_s"), 1) for k, _ in LAYA)))
    L.append("")

    # ------------------------------------------------------------- latency
    ll, lo = jload("laya_latency.json"), jload("laya_latency_onnx.json")
    if ll and lo:   # Laya's best of eager torch (8 / 16 threads) and its own ONNX Runtime backend
        for mname, ent in lo.get("models", {}).items():
            ll["models"].setdefault(mname, {"by_threads": {}})["by_threads"]["onnx"] = ent["by_threads"].get("0", {})
    nl = {k: jload("nirnay_latency_%s.json" % MODELS[k][1]) for k, _ in NIR}
    L += ["## Latency on this CPU (Laya's own workload: a support ticket, mixed choice / yes-no questions)", "",
          "p50 ms, warm, best setting for each model (Laya: eager torch at 8/16 threads or its ONNX Runtime backend).", "",
          "| questions per call | " + " | ".join(l for _, l in NIR) + " | " + " | ".join(l for _, l in LAYA) +
          " | " + " | ".join("%s speed-up vs fastest Laya" % l for _, l in NIR) + " |",
          "|---|" + "---|" * (2 * len(NIR) + len(LAYA))]
    for n in (1, 5, 10, 50):
        key = "%d_questions" % n

        def best_l(m):
            bt = ((ll or {}).get("models", {}).get(m) or {}).get("by_threads", {})
            xs = [v[key]["p50_ms"] for v in bt.values() if key in v]
            return min(xs) if xs else None

        def best_n(d):
            bt = (d or {}).get("by_threads", {})
            xs = [v[key]["warm"]["p50_ms"] for v in bt.values() if key in v]
            return min(xs) if xs else None
        ls = [best_l(m) for m, _ in LAYA]
        fastest = min([x for x in ls if x], default=None)
        ns = [best_n(nl[k]) for k, _ in NIR]
        L.append("| %d | %s | %s | %s |" % (
            n, " | ".join(("**%s**" % fmt(x, 1)) if k == head else fmt(x, 1) for x, (k, _) in zip(ns, NIR)),
            " | ".join(fmt(x, 1) for x in ls),
            " | ".join(("%.1fx" % (fastest / x)) if (fastest and x) else "—" for x in ns)))
    L.append("")
    L.append("Batch throughput (10 questions per state): " + ", ".join(
        "%s %s decisions/s" % (lab, (nl[k] or {}).get("throughput_10q", {}).get("decisions_per_s")) for k, lab in NIR) + ".")
    L.append("")
    disk = lambda name: round(sum(os.path.getsize(os.path.join(ROOT, "models", name, f))
                                  for f in os.listdir(os.path.join(ROOT, "models", name))) / 2**20) \
        if os.path.isdir(os.path.join(ROOT, "models", name)) else None
    L += ["## Footprint", "", "| | " + " | ".join(cols) + " |", "|---|" + "---|" * len(cols),
          "| weights on disk (MB) | %s | 804 | 614 | 804 |" % " | ".join(str(disk(k)) for k, _ in NIR),
          "| RAM added by loading (MB) | %s | %s |" % (
              " | ".join(fmt((nl[k] or {}).get("rss_delta_mb"), 0) for k, _ in NIR),
              " | ".join(fmt(((ll or {}).get("models", {}).get(m) or {}).get("rss_delta_mb"), 0) for m, _ in LAYA)), ""]

    # ------------------------------------------------------------- few-shot
    L += ["## Few-shot adaptation (`learn()`, no training, CPU)", "",
          "Labelled examples per label from the TRAIN split; evaluated on the same 400 TEST cases. Laya cannot do this "
          "without RL fine-tuning on a GPU; its zero-shot score is the reference.", "",
          "| model / task | k = 0 | 2 | 4 | 8 | 16 | learn() time at 16 | Laya (best ckpt) | Jev (published) |",
          "|---|---|---|---|---|---|---|---|---|"]
    for k, lab in NIR:
        fs = jload("fewshot_%s.json" % MODELS[k][1]) or {}
        for task, suite in (("banking77", "jev.banking77_full"), ("emotion", "jev.emotion")):
            r = fs.get(task) or {}
            if not r:
                continue
            ls = [acc(lay[x], suite) for x, _ in LAYA]
            L.append("| %s %s | %s | %s s | %s | %s |" % (
                lab, task, " | ".join(fmt((r.get(str(q)) or {}).get("accuracy")) for q in (0, 2, 4, 8, 16)),
                fmt((r.get("16") or {}).get("learn_seconds"), 0), fmt(max([x for x in ls if x is not None], default=None)),
                fmt(JEV.get(suite))))
    L.append("")

    # ------------------------------------------------------------- multilingual
    L += multilingual_section([k for k in want if k in MODELS])

    # ------------------------------------------------------------- ablations
    ab = [("nirnay-bges-b2", "v1-small: frozen encoder + 2 fusion blocks"),
          ("nirnay-v1", "v1: deep fusion (bge-small)"),
          ("nirnay-bges-deep-debias", "v1 + opt-in debias"),
          ("nirnay-v2", "v2: deep fusion (bge-base, more data)"),
          ("nirnay-v3", "v3: v2 + routing, short messages, safety, urgency, phrasings"),
          ("nirnay-v31", "v3.1: v3 with content-only safety wording")]
    abd = [(lbl, jload("suites__nirnay__%s.json" % n)) for n, lbl in ab]
    abd = [(lbl, d) for lbl, d in abd if d]
    L += ["## Ablations (same suites)", "", "| task | " + " | ".join(l for l, _ in abd) + " |", "|---|" + "---|" * len(abd)]
    for s in ORDER:
        L.append("| %s | %s |" % (s, " | ".join(fmt(acc(d, s)) for _, d in abd)))
    L.append("")
    open(OUT, "w", encoding="utf-8").write("\n".join(L) + "\n")
    print("wrote", OUT)


if __name__ == "__main__":
    main()

	"""Merge benchmark results into REPORT.md (Nirnay models vs Laya measured here, Jev as published).

	python bench\\report.py
	REPORT_NIR="nirnay-v3,nirnay-v2,nirnay-v1" python bench\\report.py (first model = the headline column)

	Models without a results file are left out. The multilingual section appears once
	results/suites_multi__*.json exist (MASSIVE, 51 languages; SIB-200, 205 languages).
	"""
	import json
	import os
	import statistics
	import time

	HERE = os.path.dirname(os.path.abspath(__file__))
	ROOT = os.path.dirname(HERE)
	RES = os.path.join(HERE, "results")
	OUT = os.path.join(ROOT, os.environ.get("REPORT_OUT", "REPORT.md"))

	# Published Jev figures (TypeSafe's closed API; not runnable here). Source: Laya repo
	# research/scripts/bench_apps.py, which cites AbdelStark/jev-benchmarks v0.1.0 (n=100 each)
	# and nibzard/decision-model-benchmark.
	JEV = {"jev.ag_news": 0.910, "jev.banking77_full": 0.870, "jev.emotion": 0.480, "extra.sms_spam": 0.930}
	JEV_NOTE = {"jev.banking77_full": "72 labels, n=100; independent run 0.763",
	"extra.sms_spam": "independent run, different sample"}
	LAYA = [("laya", "laya (en)"), ("laya-multilingual", "laya-ml"), ("laya-typed-decisions", "laya-td")]
	MODELS = { # key -> (column label, result-file tag, description)
	"nirnay-v3": ("Nirnay v3", "v3", "bge-base-en-v1.5, deep fusion; 324k questions: v2's mix plus request routing, short-message "
	"spam, request safety (NVIDIA Aegis 2.0), urgency and more phrasings of every question "
	"template; warm-started from v2 for 1.25 more epochs on both CPU sockets."),
	"nirnay-multilingual": ("Nirnay multilingual", "ml", "multilingual-e5-base (100 languages), deep fusion; ~380k questions: "
	"v3's mix plus MTOP intents (6 languages), PAWS-X, NLI in 26 "
	"languages, toxicity in 14, language identification, multilingual "
	"reviews and tweets; 1.2 epochs."),
	"nirnay-v31": ("Nirnay v3.1", "v31", "v3 with content-only safety wording (v3's AI-assistant / policy phrasings made it "
	"call harmful prompts jailbreaks); same recipe: warm-started from v2, 1.25 epochs."),
	"nirnay-v31-gpu": ("Nirnay v3.1-GPU", "v31gpu", "v3.1's data and architecture, warm-started from v2 for 2 epochs on a "
	"free Kaggle T4 GPU (train/train_gpu.py; frozen lower layers computed live)."),
	"nirnay-v2": ("Nirnay v2", "v2", "bge-base-en-v1.5, deep fusion (top 4 encoder layers trained), 294k questions, 1.6 epochs "
	"(overnight, both CPU sockets)."),
	"nirnay-v1": ("Nirnay v1 (fast)", "v1", "bge-small-en-v1.5, same architecture, 221k questions, 1 epoch."),
	}
	ORDER = ["jev.ag_news", "jev.emotion", "jev.banking77_full", "app.support_triage", "app.email_spam", "app.phishing",
	"app.guardrails_jailbreak", "app.moderation_toxicity", "app.rag_relevance", "app.model_routing_domain",
	"extra.sst5", "extra.sms_spam"]
	TIE = 0.005


	def jload(name):
	p = os.path.join(RES, name)
	return json.load(open(p, encoding="utf-8")) if os.path.exists(p) else None


	def fmt(x, nd=3):
	return "—" if x is None else ("%.*f" % (nd, x))


	def acc(d, s):
	return ((d or {}).get("suites", {}).get(s) or {}).get("accuracy")


	def tally(mine, theirs):
	w = t = l = 0
	for a, b in zip(mine, theirs):
	if a is None or b is None:
	continue
	if a > b + TIE:
	w += 1
	elif a < b - TIE:
	l += 1
	else:
	t += 1
	return w, t, l


	def multilingual_section(keys):
	"""MASSIVE + SIB-200 per model; per-language MASSIVE table; per-language wins vs Laya-multilingual."""
	res = {k: jload("suites_multi__nirnay__%s.json" % k) for k in keys}
	res = {k: v for k, v in res.items() if v}
	laya = jload("suites_multi__laya__laya-multilingual.json")
	if not res: # nothing of ours measured yet: no section (a Laya-only table would compare nothing)
	return []
	cols = [(k, MODELS[k][0]) for k in res] + ([("laya-ml", "Laya multilingual")] if laya else [])
	data = dict(res, **({"laya-ml": laya} if laya else {}))

	def per_lang(d, prefix):
	return {s.split(".", 1)[1]: m["accuracy"] for s, m in (d or {}).get("suites", {}).items() if s.startswith(prefix + ".")}

	L = ["## Multilingual (held out of training for every model)", "",
	"* MASSIVE intent detection (mteb/amazon_massive_intent, test split): 51 languages, all 60 intents as options, "
	"80 requests per language, the question in English.",
	"* SIB-200 topic classification (Davlan/sib200, test split): 205 languages and scripts, 7 topics, the same 24 "
	"FLORES sentences in every language.", "",
	"\| benchmark \| " + " \| ".join(l for _, l in cols) + " \|", "\|---\|" + "---\|" * len(cols)]
	for prefix, name in (("massive", "MASSIVE, mean of 51 languages"), ("sib200", "SIB-200, mean of 205 languages")):
	row = []
	for k, _ in cols:
	xs = list(per_lang(data[k], prefix).values())
	row.append(fmt(statistics.mean(xs)) if xs else "—")
	L.append("\| %s \| %s \|" % (name, " \| ".join(row)))
	for prefix, name in (("massive", "MASSIVE, worst language"), ("sib200", "SIB-200, worst language")):
	row = []
	for k, _ in cols:
	pl = per_lang(data[k], prefix)
	row.append(("%s (%s)" % (fmt(min(pl.values())), min(pl, key=pl.get))) if pl else "—")
	L.append("\| %s \| %s \|" % (name, " \| ".join(row)))
	L.append("")
	if laya and "nirnay-multilingual" in res:
	for prefix, name in (("massive", "MASSIVE"), ("sib200", "SIB-200")):
	a, b = per_lang(res["nirnay-multilingual"], prefix), per_lang(laya, prefix)
	common = sorted(set(a) & set(b))
	w, t, l = tally([a[x] for x in common], [b[x] for x in common])
	L.append("%s, per language, Nirnay multilingual vs Laya multilingual: %d wins, %d ties, %d losses "
	"(of %d languages)." % (name, w, t, l, len(common)))
	L.append("")
	langs = sorted(set().union(*[per_lang(data[k], "massive") for k, _ in cols]))
	if langs:
	L += ["### MASSIVE by language", "", "\| language \| " + " \| ".join(l for _, l in cols) + " \|",
	"\|---\|" + "---\|" * len(cols)]
	for lg in langs:
	L.append("\| %s \| %s \|" % (lg, " \| ".join(fmt(per_lang(data[k], "massive").get(lg)) for k, _ in cols)))
	L.append("")
	return L


	def main():
	want = [k.strip() for k in os.environ.get("REPORT_NIR", "nirnay-v31,nirnay-v3,nirnay-multilingual,nirnay-v2,nirnay-v1").split(",")
	if k.strip()]
	nir = {k: jload("suites__nirnay__%s.json" % k) for k in want}
	NIR = [(k, MODELS[k][0]) for k in want if nir.get(k)]
	head = NIR[0][0]
	lv = ((jload("laya_latency.json") or {}).get("meta") or {}).get("laya", "?")
	lay = {k: jload("suites__laya__%s.json" % k) for k, _ in LAYA}
	L = ["# Nirnay vs Laya vs Jev — measured on this machine", "",
	"Generated %s. Hardware: HP Z840, 2x Xeon E5-2630 v3 (16 cores / 32 threads, AVX2, no AVX-512), "
	"128 GB DDR4, CPU only (Quadro K2200 unused)." % time.strftime("%Y-%m-%d %H:%M"), "",
	"Laya was installed from PyPI (`laya` %s) with its three published checkpoints and run through its own API "
	"with its shipped calibration. Jev is a closed, paid cloud API: it cannot run on this machine, so its column "
	"is the third-party published figure where one exists." % lv, ""]
	L += ["* %s (`models\\%s`): %s" % (MODELS[k][0], k, MODELS[k][2]) for k, _ in NIR] + [""]

	# ------------------------------------------------------------- accuracy
	L += ["## Accuracy (same 400 cases per task, same questions, seed 13)", "",
	"\| task \| " + " \| ".join(l for _, l in NIR) + " \| " + " \| ".join(l for _, l in LAYA) +
	" \| best Laya \| Jev (published) \| Laya trained on it? \|",
	"\|---\|" + "---\|" * (len(NIR) + len(LAYA) + 3)]
	heads, bests, ens = [], [], []
	for s in ORDER:
	ls = [acc(lay[k], s) for k, _ in LAYA]
	best = max([x for x in ls if x is not None], default=None)
	meta = ((nir[head] or {}).get("suites", {}).get(s) or {}).get("meta") or {}
	heads.append(acc(nir[head], s))
	bests.append(best)
	ens.append(ls[0])
	jt = JEV.get(s)
	cells = ["%s" % fmt(acc(nir[k], s)) if k == head else fmt(acc(nir[k], s)) for k, _ in NIR]
	L.append("\| %s \| %s \| %s \| %s \| %s \| %s \|" % (
	s, " \| ".join(cells), " \| ".join(fmt(x) for x in ls), fmt(best),
	(fmt(jt) + (" (%s)" % JEV_NOTE[s] if s in JEV_NOTE else "")) if jt else "—",
	"yes" if meta.get("laya_in_training") else "no (held out)"))
	hl = MODELS[head][0]
	w, t, l = tally(heads, bests)
	w2, t2, l2 = tally(heads, ens)
	L += ["", "%s vs the best of Laya's three checkpoints (picked per task, after the fact): "
	"%d wins, %d ties (±%.3f), %d losses." % (hl, w, t, TIE, l),
	"%s vs Laya's default English checkpoint: %d wins, %d ties, %d losses." % (hl, w2, t2, l2)]
	for k, lab in NIR[1:]:
	w3, t3, l3 = tally(heads, [acc(nir[k], s) for s in ORDER])
	L.append("%s vs %s: %d better, %d same, %d worse." % (hl, lab, w3, t3, l3))
	L.append("")

	# ------------------------------------------------------------- calibration
	L += ["## Calibration (ECE, lower is better)", "",
	"\| task \| " + " \| ".join(l for _, l in NIR) + " \| best Laya \|", "\|---\|" + "---\|" * (len(NIR) + 1)]
	for s in ORDER:
	le = [((lay[k] or {}).get("suites", {}).get(s) or {}).get("ece") for k, _ in LAYA]
	le = [x for x in le if x is not None]
	L.append("\| %s \| %s \| %s \|" % (s, " \| ".join(fmt(((nir[k] or {}).get("suites", {}).get(s) or {}).get("ece")) for k, _ in NIR),
	fmt(min(le) if le else None)))
	L.append("")

	# ------------------------------------------------------------- robustness
	flip = lambda d: [v.get("order_flip_rate") for v in (d or {}).get("suites", {}).values() if v.get("order_flip_rate") is not None]
	avg = lambda xs: (sum(xs) / len(xs)) if xs else None
	cols = [l for _, l in NIR] + [l for _, l in LAYA]
	nm = " \| ".join("not measured" for _ in LAYA)
	L += ["## Robustness and determinism", "", "\| \| " + " \| ".join(cols) + " \|", "\|---\|" + "---\|" * len(cols)]
	L.append("\| option-order flip rate (choice tasks, mean) \| %s \| %s \|" % (
	" \| ".join(fmt(avg(flip(nir[k]))) for k, _ in NIR), " \| ".join(fmt(avg(flip(lay[k]))) for k, _ in LAYA)))
	L.append("\| identical answers on repeat call \| %s \| %s \|" % (
	" \| ".join(fmt((nir[k] or {}).get("determinism_repeat_identical")) for k, _ in NIR),
	" \| ".join(fmt((lay[k] or {}).get("determinism_repeat_identical")) for k, _ in LAYA)))
	dets = [jload("nirnay_determinism_%s.json" % MODELS[k][1]) for k, _ in NIR]
	L.append("\| byte-identical answers across 1/4/8/16 threads \| %s \| %s \|" % (
	" \| ".join(fmt((d or {}).get("identical_rate")) for d in dets), nm))
	L.append("\| decisions changed across 1/4/8/16 threads \| %s \| %s \|" % (
	" \| ".join(("%s (max prob. diff %s)" % (d.get("answer_changes"), d.get("max_probability_diff"))) if d else "—"
	for d in dets), nm))
	cs = jload("cold_start.json") or {}
	L.append("\| cold start: new process -> first answer (s) \| %s \| %s \|" % (
	" \| ".join(fmt((cs.get("nirnay:" + k) or {}).get("first_answer_s"), 1) for k, _ in NIR),
	" \| ".join(fmt((cs.get("laya:" + k) or {}).get("first_answer_s"), 1) for k, _ in LAYA)))
	L.append("")

	# ------------------------------------------------------------- latency
	ll, lo = jload("laya_latency.json"), jload("laya_latency_onnx.json")
	if ll and lo: # Laya's best of eager torch (8 / 16 threads) and its own ONNX Runtime backend
	for mname, ent in lo.get("models", {}).items():
	ll["models"].setdefault(mname, {"by_threads": {}})["by_threads"]["onnx"] = ent["by_threads"].get("0", {})
	nl = {k: jload("nirnay_latency_%s.json" % MODELS[k][1]) for k, _ in NIR}
	L += ["## Latency on this CPU (Laya's own workload: a support ticket, mixed choice / yes-no questions)", "",
	"p50 ms, warm, best setting for each model (Laya: eager torch at 8/16 threads or its ONNX Runtime backend).", "",
	"\| questions per call \| " + " \| ".join(l for _, l in NIR) + " \| " + " \| ".join(l for _, l in LAYA) +
	" \| " + " \| ".join("%s speed-up vs fastest Laya" % l for _, l in NIR) + " \|",
	"\|---\|" + "---\|" * (2 * len(NIR) + len(LAYA))]
	for n in (1, 5, 10, 50):
	key = "%d_questions" % n

	def best_l(m):
	bt = ((ll or {}).get("models", {}).get(m) or {}).get("by_threads", {})
	xs = [v[key]["p50_ms"] for v in bt.values() if key in v]
	return min(xs) if xs else None

	def best_n(d):
	bt = (d or {}).get("by_threads", {})
	xs = [v[key]["warm"]["p50_ms"] for v in bt.values() if key in v]
	return min(xs) if xs else None
	ls = [best_l(m) for m, _ in LAYA]
	fastest = min([x for x in ls if x], default=None)
	ns = [best_n(nl[k]) for k, _ in NIR]
	L.append("\| %d \| %s \| %s \| %s \|" % (
	n, " \| ".join(("%s" % fmt(x, 1)) if k == head else fmt(x, 1) for x, (k, _) in zip(ns, NIR)),
	" \| ".join(fmt(x, 1) for x in ls),
	" \| ".join(("%.1fx" % (fastest / x)) if (fastest and x) else "—" for x in ns)))
	L.append("")
	L.append("Batch throughput (10 questions per state): " + ", ".join(
	"%s %s decisions/s" % (lab, (nl[k] or {}).get("throughput_10q", {}).get("decisions_per_s")) for k, lab in NIR) + ".")
	L.append("")
	disk = lambda name: round(sum(os.path.getsize(os.path.join(ROOT, "models", name, f))
	for f in os.listdir(os.path.join(ROOT, "models", name))) / 2**20) \
	if os.path.isdir(os.path.join(ROOT, "models", name)) else None
	L += ["## Footprint", "", "\| \| " + " \| ".join(cols) + " \|", "\|---\|" + "---\|" * len(cols),
	"\| weights on disk (MB) \| %s \| 804 \| 614 \| 804 \|" % " \| ".join(str(disk(k)) for k, _ in NIR),
	"\| RAM added by loading (MB) \| %s \| %s \|" % (
	" \| ".join(fmt((nl[k] or {}).get("rss_delta_mb"), 0) for k, _ in NIR),
	" \| ".join(fmt(((ll or {}).get("models", {}).get(m) or {}).get("rss_delta_mb"), 0) for m, _ in LAYA)), ""]

	# ------------------------------------------------------------- few-shot
	L += ["## Few-shot adaptation (`learn()`, no training, CPU)", "",
	"Labelled examples per label from the TRAIN split; evaluated on the same 400 TEST cases. Laya cannot do this "
	"without RL fine-tuning on a GPU; its zero-shot score is the reference.", "",
	"\| model / task \| k = 0 \| 2 \| 4 \| 8 \| 16 \| learn() time at 16 \| Laya (best ckpt) \| Jev (published) \|",
	"\|---\|---\|---\|---\|---\|---\|---\|---\|---\|"]
	for k, lab in NIR:
	fs = jload("fewshot_%s.json" % MODELS[k][1]) or {}
	for task, suite in (("banking77", "jev.banking77_full"), ("emotion", "jev.emotion")):
	r = fs.get(task) or {}
	if not r:
	continue
	ls = [acc(lay[x], suite) for x, _ in LAYA]
	L.append("\| %s %s \| %s \| %s s \| %s \| %s \|" % (
	lab, task, " \| ".join(fmt((r.get(str(q)) or {}).get("accuracy")) for q in (0, 2, 4, 8, 16)),
	fmt((r.get("16") or {}).get("learn_seconds"), 0), fmt(max([x for x in ls if x is not None], default=None)),
	fmt(JEV.get(suite))))
	L.append("")

	# ------------------------------------------------------------- multilingual
	L += multilingual_section([k for k in want if k in MODELS])

	# ------------------------------------------------------------- ablations
	ab = [("nirnay-bges-b2", "v1-small: frozen encoder + 2 fusion blocks"),
	("nirnay-v1", "v1: deep fusion (bge-small)"),
	("nirnay-bges-deep-debias", "v1 + opt-in debias"),
	("nirnay-v2", "v2: deep fusion (bge-base, more data)"),
	("nirnay-v3", "v3: v2 + routing, short messages, safety, urgency, phrasings"),
	("nirnay-v31", "v3.1: v3 with content-only safety wording")]
	abd = [(lbl, jload("suites__nirnay__%s.json" % n)) for n, lbl in ab]
	abd = [(lbl, d) for lbl, d in abd if d]
	L += ["## Ablations (same suites)", "", "\| task \| " + " \| ".join(l for l, _ in abd) + " \|", "\|---\|" + "---\|" * len(abd)]
	for s in ORDER:
	L.append("\| %s \| %s \|" % (s, " \| ".join(fmt(acc(d, s)) for _, d in abd)))
	L.append("")
	open(OUT, "w", encoding="utf-8").write("\n".join(L) + "\n")
	print("wrote", OUT)


	if __name__ == "__main__":
	main()