#!/usr/bin/env python3
"""文本探针的模型内复测 vs 跨模型区分度（纯离线）。

关键问题：s_joke 跨模型 Jaccard 0.06 是"风格指纹"还是"纯随机"？
若同一模型两次独立采样（glm_53 verify vs rerun）的 Jaccard 同样趋近 0，
则区分度被随机性淹没（模型内不稳定 → 无法建参考 → 打分端无法消费）。
对比组：I 层身份题（temp 0.2，预期模型内近乎逐字稳定）。
"""
import itertools
import json
import re

BFD = "/tmp/bfd"
DIRS = ["deepseek_v4_flash", "deepseek_v4_flash_0731", "deepseek_v4_pro",
        "glm_51", "glm_52", "glm_53", "kimi_k2_6", "kimi_k2_7code", "kimi_k3"]


def toks(t):
    return set(re.findall(r"\w+", (t or "").lower()))


def jac(a, b):
    return len(a & b) / len(a | b) if (a or b) else 1.0


def load(path):
    out = {}
    for r in map(json.loads, open(path)):
        if not r.get("error"):
            out[r["id"]] = r.get("response") or ""
    return out


r1 = {d: load(f"{BFD}/{d}/raw_answers.jsonl") for d in DIRS}
r2 = load(f"{BFD}/glm_53/rerun/raw_answers.jsonl")

PROBES = ["s_joke", "s_list", "s_simple", "s_what", "s_len1a",
          "i_zh_direct", "i_direct_en1", "i_meta1", "k_cutoff1", "k_params"]

print(f"{'探针':18s} {'模型内复测(g53两跑)':>20s} {'逐字相同':>8s} {'跨模型均J':>10s}  判读")
for p in PROBES:
    within = jac(toks(r1["glm_53"].get(p, "")), toks(r2.get(p, "")))
    verbatim = r1["glm_53"].get(p, "") == r2.get(p, "")
    ts = [toks(r1[d].get(p)) for d in DIRS if r1[d].get(p)]
    cross = sum(jac(a, b) for a, b in itertools.combinations(ts, 2)) / \
        max(len(list(itertools.combinations(ts, 2))), 1)
    if within > 0.6 and cross < 0.5:
        verdict = "真指纹: 模型内稳+模型间异"
    elif within < 0.3:
        verdict = "纯随机: 模型内也不稳→不可建参考"
    else:
        verdict = "部分信号"
    print(f"{p:18s} {within:20.2f} {str(verbatim):>8s} {cross:10.2f}  {verdict}")