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alea/gen_jb3.py

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+"""gen_jb3: LONG-document reasoning — multi-section policies with nested
+exceptions, judge-style verdicts, multi-constraint chains.
+
+Hard tier needs 1-4k token docs where the answer depends on composing
+several sections in order (base rule -> endorsement override ->
+exception-to-endorsement -> global condition). These generators build
+that structure deterministically so the correct answer is exact.
+"""
+import argparse
+import json
+import random
+
+rng = random.Random(31337)
+
+
+def choice(state, instr, crit, correct_key, family):
+    keys = list(crit.keys())
+    tgt = [0.0] * len(keys)
+    tgt[keys.index(correct_key)] = 1.0
+    return {"state": state,
+            "questions": {"q0": {"type": "choice", "instructions": instr,
+                                 "criteria": crit}},
+            "targets": {"q0": tgt}, "family": family}
+
+
+def noul(state, instr, val, family):
+    return {"state": state,
+            "questions": {"q0": {"type": "noul", "instructions": instr}},
+            "targets": {"q0": float(val)}, "family": family}
+
+
+# ---------- long insurance/loan/policy docs ----------
+
+def gen_longpolicy(n):
+    """Multi-section policy with exception chains -> adjudication."""
+    out = []
+    for _ in range(n):
+        # --- document skeleton ---
+        cov_window = rng.choice([30, 60, 90])
+        late_days = rng.choice([30, 45])
+        ded = rng.choice([250, 500])
+        cap = rng.choice([2000, 5000, 10000])
+        region_ok = rng.choice([True, False])
+        secs = [
+            "MASTER SERVICES AND PROTECTION AGREEMENT",
+            "Document ref: MPA-%d" % rng.randint(1000, 9999),
+            "In force: %s through %s." % (
+                rng.choice(["01 Jan 2024", "01 Feb 2024"]),
+                rng.choice(["31 Dec 2025", "31 Dec 2026"])),
+            "",
+            "1. DEFINITIONS",
+            "  1.1 'Covered Event' means an event listed in Section 3.",
+            "  1.2 'Territory' means the coverage area in Schedule A.",
+            "  1.3 'Prompt Notice' means notice within %d days." % late_days,
+            "",
+            "2. SCOPE",
+            "  2.1 This agreement applies to claims arising during the "
+            "in-force period.",
+            "  2.2 Claims arising outside the Territory are excluded "
+            "absent a Territory Endorsement.",
+            "",
+            "3. COVERED EVENTS",
+            "  3.1 Accidental damage reported within %d days." % cov_window,
+            "  3.2 Theft with a filed police report.",
+            "  3.3 Mechanical failure not caused by misuse.",
+            "",
+            "4. EXCLUSIONS",
+            "  4.1 Cosmetic damage is excluded.",
+            "  4.2 Events arising from unauthorized modification are "
+            "excluded.",
+            "  4.3 Events occurring before the in-force date are excluded.",
+            "",
+            "5. CONDITIONS PRECEDENT",
+            "  5.1 Prompt Notice is a condition precedent to coverage.",
+            "  5.2 A deductible of $%d applies per claim." % ded,
+            "",
+            "6. ENDORSEMENTS",
+        ]
+        # endorsements create the exception chains
+        e_theft_no_police = rng.random() < 0.4
+        e_territory = rng.random() < 0.5
+        e_mod = rng.random() < 0.35
+        if e_theft_no_police:
+            secs.append("  E-T1: theft claims without a police report are "
+                        "covered up to $%d if reported with two forms of "
+                        "proof of ownership." % cap)
+        if e_territory:
+            secs.append("  E-TR: the Territory exclusion in 2.2 is waived "
+                        "for losses occurring within 50 km of the Territory "
+                        "boundary.")
+        if e_mod:
+            secs.append("  E-M1: Section 4.2 does not apply where the "
+                        "modification was performed by an authorized "
+                        "service center.")
+        secs += ["",
+                 "7. CLAIMS PROCEDURE",
+                 "  7.1 All claims must be filed via the portal.",
+                 "  7.2 The insurer responds within 15 business days.",
+                 "",
+                 "8. GENERAL",
+                 "  8.1 This agreement is governed by the laws of the "
+                 "issuing jurisdiction.",
+                 "  8.2 Nothing herein limits statutory rights."]
+
+        # --- the claim ---
+        event = rng.choice(["accident", "theft", "mechanical"])
+        reported = rng.choice(["prompt", "late"])
+        outside = rng.random() < 0.4
+        police = rng.random() < 0.6
+        modded = rng.random() < 0.35
+        auth_mod = modded and (rng.random() < 0.5)
+
+        facts = ["Loss date: %s 2025 (in-force period)." %
+                 rng.choice(["Apr 3", "Jul 19", "Oct 8"]),
+                 "Event type: %s." % event]
+        if event == "accident":
+            facts.append("Damage is structural, not cosmetic.")
+            facts.append("Reported %d days after the event." %
+                         (rng.randint(1, cov_window - 3) if reported == "prompt"
+                          else cov_window + rng.randint(3, 20)))
+        if event == "theft":
+            facts.append("Police report %s." %
+                         ("filed same day" if police else "was NOT filed"))
+            if e_theft_no_police and not police:
+                facts.append("Claimant supplied %s." %
+                             rng.choice(["two proofs of ownership",
+                                         "one proof of ownership"]))
+        if event == "mechanical":
+            facts.append("Unit %s." %
+                         ("was modified by an authorized service center"
+                          if auth_mod else
+                          "has an unauthorized third-party modification"
+                          if modded else "is unmodified"))
+            facts.append("Reported %d days after the event." %
+                         rng.randint(1, cov_window))
+        if outside:
+            facts.append("Loss occurred %d km outside the Territory "
+                         "boundary." % rng.choice([20, 80]))
+        if reported == "late" and event != "accident":
+            facts.append("Claim filed %d days after the loss." %
+                         (late_days + rng.randint(1, 15)))
+        elif event != "accident":
+            facts.append("Claim filed %d days after the loss." %
+                         rng.randint(1, late_days - 2))
+
+        # --- adjudicate ---
+        if event == "accident" and reported == "late":
+            correct = "deny_late"
+        elif event == "accident":
+            correct = "pay"
+        elif event == "theft":
+            if police:
+                correct = "pay"
+            elif e_theft_no_police:
+                two_proofs = any("two proofs" in f for f in facts)
+                correct = "pay_capped" if two_proofs else "deny_condition"
+            else:
+                correct = "deny_condition"
+        else:  # mechanical
+            if modded and auth_mod and e_mod:
+                correct = "pay"
+            elif modded and auth_mod and not e_mod:
+                correct = "deny_excluded"
+            elif modded and not auth_mod:
+                correct = "deny_excluded"
+            else:
+                correct = "pay"
+        if correct == "pay" and outside:
+            near = "20 km" in " ".join(facts)
+            correct = "pay" if (e_territory and near) else "deny_excluded"
+        elif outside and correct.startswith("pay"):
+            near = "20 km" in " ".join(facts)
+            correct = "pay" if (e_territory and near) else "deny_excluded"
+        if reported == "late" and event != "accident" and correct == "pay":
+            correct = "deny_late"
+
+        crit = {
+            "pay": "Pay the claim less the deductible",
+            "pay_capped": "Pay subject to the endorsement cap",
+            "deny_late": "Deny: notice condition not met",
+            "deny_excluded": "Deny: exclusion applies",
+            "deny_condition": "Deny: a coverage condition was not met",
+        }
+        st = "\n".join(secs) + "\n\nCLAIM:\n" + "\n".join(facts)
+        out.append(choice(st, "Adjudicate the claim. Apply sections in "
+                          "order; endorsements modify earlier sections; "
+                          "conditions precedent bind first.", crit,
+                          correct, "jb3_longpolicy"))
+    return out
+
+
+# ---------- judge: verify a verdict ----------
+
+def gen_judge(n):
+    """Doc + candidate verdict -> agree/disagree (judge_hard style)."""
+    out = []
+    for _ in range(n):
+        a = rng.randint(100, 900)
+        b = rng.randint(100, 900)
+        c = rng.randint(100, 900)
+        col = rng.choice(["revenue", "cost", "units"])
+        rec = {"revenue": a, "cost": b, "units": c}
+        rec["margin"] = round((a - b) / a, 3)
+        st = ("Record:\n  revenue: %d\n  cost: %d\n  units: %d\n  margin: "
+              "%.3f\n  region: %s\n  quarter: Q%d" %
+              (a, b, c, rec["margin"], rng.choice(["EU", "NA", "APAC"]),
+               rng.randint(1, 4)))
+        # candidate verdict — correct half the time
+        kind = rng.choice(["margin", "sum", "max"])
+        if kind == "margin":
+            true_v = rec["margin"]
+            wrong_v = round((a - b) / b, 3)
+            claim = "the margin is %.3f" % (true_v if rng.random() < 0.5
+                                            else wrong_v)
+            ok = ("%.3f" % true_v) in claim
+        elif kind == "sum":
+            true_v = a + b
+            wrong_v = a + b + rng.choice([-1, 1]) * rng.randint(5, 50)
+            claim = "revenue plus cost equals %d" % (true_v
+                     if rng.random() < 0.5 else wrong_v)
+            ok = str(true_v) in claim
+        else:
+            big = max(rec.values())
+            wrong = min(rec.values())
+            claim = "the largest field is %d" % (big if rng.random() < 0.5
+                                                 else wrong)
+            ok = str(big) in claim
+        st += "\n\nAnalyst verdict: \"%s\"." % claim
+        out.append(choice(st, "Judge the verdict: does it follow from the "
+                          "record?",
+                          {"correct": "Verdict is correct",
+                           "incorrect": "Verdict is incorrect"},
+                          "correct" if ok else "incorrect", "jb3_judge"))
+    return out
+
+
+# ---------- multi-hop temporal chains ----------
+
+def gen_temporal_chain(n):
+    """3-4 step date/shift chains — multi_hop style."""
+    import datetime as dt
+    out = []
+    for _ in range(n):
+        y = rng.choice([2024, 2025])
+        m, d = rng.randint(1, 12), rng.randint(1, 28)
+        try:
+            base = dt.date(y, m, d)
+        except ValueError:
+            continue
+        hops = [rng.randint(3, 14) for _ in range(3)]
+        wds = [h * rng.choice([1, 1]) for h in hops]
+        steps_txt = []
+        cur = base
+        for i, h in enumerate(hops):
+            steps_txt.append("Step %d: add %d days." % (i + 1, h))
+            cur += dt.timedelta(days=h)
+        # ask for final weekday or date
+        ask = rng.choice(["weekday", "date"])
+        st = ("Start date: %s.\n%s\n" % (base.strftime("%A, %B %d, %Y"),
+                                        "\n".join(steps_txt)))
+        if ask == "weekday":
+            opts = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday",
+                    "Saturday", "Sunday"]
+            rng.shuffle(opts)
+            crit = {o: "final day is %s" % o for o in opts}
+            out.append(choice(st, "Apply each step in order and choose the "
+                              "final weekday.", crit,
+                              cur.strftime("%A"), "jb3_temporal_chain"))
+        else:
+            opts = [cur.strftime("%B %d")]
+            for _ in range(3):
+                w = cur + dt.timedelta(days=rng.choice([-3, -2, -1, 1, 2, 3]))
+                opts.append(w.strftime("%B %d"))
+            order = list(range(4)); rng.shuffle(order)
+            shuffled = [opts[j] for j in order]
+            crit = {"d%d" % i: shuffled[i] for i in range(4)}
+            out.append(choice(st, "Apply each step in order and choose the "
+                              "final date.", crit,
+                              "d%d" % order.index(0), "jb3_temporal_chain"))
+    return out
+
+
+GENS = {"longpolicy": gen_longpolicy, "judge": gen_judge,
+        "temporal_chain": gen_temporal_chain}
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--out", default="data/jb3.jsonl")
+    ap.add_argument("--per", type=int, default=8000)
+    args = ap.parse_args()
+    rows = []
+    for name, fn in GENS.items():
+        got = fn(args.per)
+        print(name, len(got))
+        rows += got
+    rng.shuffle(rows)
+    with open(args.out, "w") as f:
+        for r in rows:
+            f.write(json.dumps(r, ensure_ascii=False) + "\n")
+    print("TOTAL", len(rows), "->", args.out)
+
+
+if __name__ == "__main__":
+    main()