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"""Stage G: checkpoints, validation and the per-patient manifest.
assign one checkpoint visit per question -> checkpoints.jsonl
audit every (question, checkpoint) pair, with the memory depth -> checkpoint_audit.json
validate oracle and no-memory probes judged by an LLM, and the dialogue checks of abstention and axis-1
never-again questions -> validation.jsonl (+ _validation in the banks)
count-errors number of validation rows whose probes or judge calls failed
manifest MANIFEST.json: counts, models and the file inventory
"""
from __future__ import annotations
import argparse
import glob
import json
import os
import re
from collections import Counter
from concurrent.futures import ThreadPoolExecutor, as_completed
from pathlib import Path
from pipeline.common import BASE, Progress, has_text, lists_options, model, no_memory_exempt_axes, patient_id
from pipeline.llm import ChatClient
PATIENT = patient_id()
ENGINE = model("generation")
def assign() -> None:
"""Assign one checkpoint visit to every question.
The checkpoint is the visit at which a question is posed; the method has seen the dialogues up to and
including it. It must come after the evidence and fall inside the question's appearance window. The
latest visit in the window is chosen. Axis-8 questions keep their computed checkpoint; one that has to be
moved, since its gold was computed for it, is reported. Questions with no valid checkpoint are reported.
"""
chain = [json.loads(l) for l in open(BASE / "event_chain.jsonl") if l.strip()]
by_v = {r["visit_number"]: r for r in chain}
vs = sorted(by_v)
date_of = {v: by_v[v]["date"] for v in vs}
def last_visit_on_or_before(d):
cand = [v for v in vs if date_of[v] <= d]
return cand[-1] if cand else None
def first_visit_on_or_after(d):
cand = [v for v in vs if date_of[v] >= d]
return cand[0] if cand else None
checkpoints, problems = [], []
for f in sorted(glob.glob(str(BASE / "questions_axis*.jsonl"))):
ax = Path(f).stem.split("axis")[1]
for i, l in enumerate(open(f)):
q = json.loads(l)
qid = q.get("qid", f"{PATIENT}/axis{ax}/{i:03d}")
gt = q.get("gt_visit")
win = q.get("question_appearance_window") or {}
wf, wt = win.get("from"), win.get("to")
ck = (q.get("axis_specific") or {}).get("checkpoint_visit")
computed = ck
if ck not in by_v:
ck = None
if ck is None and wt:
ck = last_visit_on_or_before(wt)
if ck is None and wf:
ck = first_visit_on_or_after(wf)
if ck is None:
ck = vs[-1]
if isinstance(gt, int) and ck is not None and ck <= gt:
after = [v for v in vs if v > gt and (not wt or date_of[v] <= wt)]
if after:
ck = after[-1]
else:
after_any = [v for v in vs if v > gt]
ck = after_any[0] if after_any else ck
problems.append(f"{qid}: no in-window visit after gt=v{gt}; placed ckpt=v{ck} "
f"(out of window)")
if computed is not None and ck != computed:
problems.append(f"{qid}: computed checkpoint v{computed} moved to v{ck}; the gold was computed "
f"for v{computed}")
ck_date = date_of.get(ck)
in_window = bool(wf and wt and ck_date and wf <= ck_date <= wt)
ckpt_gt_ok = not (isinstance(gt, int)) or (ck is not None and ck > gt)
if not ckpt_gt_ok:
problems.append(f"{qid}: ckpt=v{ck} <= gt=v{gt} (unanswerable at ask time)")
checkpoints.append({
"qid": qid, "axis": q.get("axis", f"{ax}_"), "ckpt_visit": ck, "ckpt_date": ck_date,
"window_from": wf, "window_to": wt, "in_window": in_window,
"gt_visit": gt, "ckpt_gt_ok": ckpt_gt_ok,
"grounding": q.get("grounding")})
out = BASE / "checkpoints.jsonl"
with open(out, "w", encoding="utf-8") as fh:
for c in checkpoints:
fh.write(json.dumps(c, ensure_ascii=False) + "\n")
n = len(checkpoints)
inw = sum(1 for c in checkpoints if c["in_window"])
gtok = sum(1 for c in checkpoints if c["ckpt_gt_ok"])
print(f"assigned {n} checkpoints -> {out}")
print(f" ckpt > gt (answerable) : {gtok}/{n}")
print(f" in appearance window : {inw}/{n}")
print(f" by axis: {dict(Counter(c['axis'].split('_')[0] for c in checkpoints))}")
if problems:
print(f"\n {len(problems)} placement problem(s):")
for p in problems[:12]:
print(f" {p}")
else:
print(" no placement problems; every checkpoint is after its evidence.")
def toks(s):
return {w for w in re.sub(r"[^a-z0-9 ]", " ", str(s).lower()).split() if len(w) >= 4}
def audit() -> None:
"""Audit every (question, checkpoint) pair.
Checks that the checkpoint follows the evidence, lies in the appearance window, and that the evidence is
present in the dialogues up to the checkpoint. Reports the memory depth: visits from the latest first
statement of an evidence fact to the checkpoint.
"""
chain = [json.loads(l) for l in open(BASE / "event_chain.jsonl") if l.strip()]
by_v = {r["visit_number"]: r for r in chain}
vs = sorted(by_v)
ck = {c["qid"]: c for c in (json.loads(l) for l in open(BASE / "checkpoints.jsonl") if l.strip())}
dia = {r["visit_number"]: r for r in
(json.loads(l) for l in open(BASE / "dialogues.jsonl") if l.strip())}
spoken = {v: json.dumps(dia[v].get("turns"), ensure_ascii=False).lower() for v in dia}
def said_by(atom, ckpt_visit):
"""Whether `atom` is recoverable from the dialogues up to and including ckpt_visit (at least 60% of its
content tokens appear)."""
at = toks(atom)
if not at:
return True, None
for v in [x for x in vs if x <= ckpt_visit and x in spoken]:
if len(at & toks(spoken[v])) / len(at) >= 0.6:
return True, v
return False, None
rows, fails = [], []
for f in sorted(glob.glob(str(BASE / "questions_axis*.jsonl"))):
for l in open(f):
q = json.loads(l)
qid = q["qid"]
c = ck.get(qid)
if not c:
fails.append((qid, "no checkpoint"))
continue
cv, gt = c.get("ckpt_visit"), c.get("gt_visit")
after_evidence = not isinstance(gt, int) or (cv is not None and cv > gt)
in_window = c.get("in_window", False)
ev = (q.get("derivation") or {}).get("evidence") or []
ev_atoms = [e.get("atom") for e in ev if e.get("atom")]
if not ev_atoms and gt in by_v:
ev_atoms = [q.get("answer")]
answerable, last_said = True, None
for a in ev_atoms:
ok, v = said_by(a, cv if isinstance(cv, int) else vs[-1])
if not ok:
answerable = False
elif v is not None:
last_said = v if last_said is None else max(last_said, v)
depth = (vs.index(cv) - vs.index(last_said)) if (isinstance(cv, int) and last_said
and cv in vs and last_said in vs) else None
rows.append({"qid": qid, "ckpt_after_evidence": after_evidence, "in_window": in_window,
"answerable": answerable, "depth": depth})
if not (after_evidence and answerable):
fails.append((qid, f"ckpt_after_evidence={after_evidence} answerable={answerable}"))
(BASE / "checkpoint_audit.json").write_text(json.dumps(rows, ensure_ascii=False, indent=1))
n = len(rows)
n_after = sum(1 for r in rows if r["ckpt_after_evidence"])
n_window = sum(1 for r in rows if r["in_window"])
n_answerable = sum(1 for r in rows if r["answerable"])
depths = [r["depth"] for r in rows if r["depth"] is not None]
print(f"audited {n} questions")
print(f" checkpoint after evidence : {n_after}/{n}")
print(f" checkpoint in window : {n_window}/{n}")
print(f" answerable from dialogues : {n_answerable}/{n}")
if depths:
depths.sort()
print(f" depth (first statement -> checkpoint): min {depths[0]}, median {depths[len(depths)//2]}, "
f"max {depths[-1]}")
if fails:
print(f"\n {len(fails)} question(s) fail the audit:")
for qid, why in fails[:15]:
print(f" {qid}: {why}")
else:
print(" all questions pass the audit.")
print(f"\n-> {BASE/'checkpoint_audit.json'}")
def client():
return ChatClient(model=ENGINE, reasoning_effort="medium", max_completion_tokens=1500)
def qtext(q):
"""Render the question, folding MC options in so the oracle sees the choices, unless the question text
already lists them."""
s = q.get("question") or ""
if q.get("options") and not lists_options(s, q["options"]):
s += "\n" + "\n".join(f" {o.get('label')}. {o.get('content')}" for o in q["options"])
return s
def answer_probe(cli, q, context, as_of=None):
ans_type = q.get("answer_type", "open_exact")
hint = {"multiple_choice": "Answer with one letter (A, B, C or D) and the option text.",
"abstain": "If the record does not contain the answer, say exactly 'not documented'.",
"state": "State the current status and why."}.get(ans_type, "Answer concisely.")
today = f"\nToday's date, as of which you answer: {as_of}.\n" if as_of else ""
question = qtext(q)
if context:
sysm = ("You answer a question about one patient using only the clinical dialogue provided. Do not use\n"
"outside knowledge of what is typical; answer only from the record. " + hint)
usr = f"Clinical dialogue, the patient's record:\n{context}\n{today}\nQuestion:\n{question}\n\nAnswer:"
else:
sysm = ("You answer a question about a patient whose record you have not seen. Use general clinical\n"
"knowledge and give your single best guess; never say you do not know. " + hint)
usr = (f"A question about a patient whose record you have not been given:\n{question}\n\n"
"Answer from the question alone and give your best guess; do not answer 'unknown'.")
try:
r = cli.complete_json(sysm, usr + '\n\nReturn JSON in this form: {"answer": "<your answer>"}')
if r.get("answer") is None or not str(r["answer"]).strip():
raise ValueError(f"reply has no answer: {str(r)[:60]}")
return str(r["answer"]).strip()
except Exception as e:
return f"__ERROR__ {str(e)[:60]}"
def judge(cli, q, candidate):
gold = q.get("answer") or (q.get("derivation") or {}).get("gold")
sysm = (
"You judge whether a candidate answer matches the gold answer in meaning, for a specific patient.\n"
"Ignore wording, formatting and extra detail, and match on the core clinical fact. For a\n"
"multiple-choice question, the same option, by letter or by its text, is a match.\n\n"
"A non-answer is never a match:\n"
"- 'unknown', 'I don't know', 'no record was provided' and 'cannot determine without the record'\n"
" are non-answers. They never match, not even a gold answer that says 'not documented'. Saying\n"
" that you have not seen the record is different from stating, after reviewing it, that\n"
" something is absent.\n"
"- A gold answer of 'not documented', 'never recorded' or 'no' is a checked negative. Only an\n"
" explicit statement of absence matches it, such as 'not documented', 'never', 'no such record'\n"
" or 'it did not recur'. An admission of ignorance such as 'unknown' does not.\n\n"
"For a detailed gold answer with many values, the candidate matches if it captures the key fact\n"
"the question asks about, even if it leaves out secondary numbers. Return only JSON.")
question_text = qtext(q)
usr = (f"Question: {question_text}\nGold answer: {gold}\nCandidate answer: {candidate}\n\n"
'Return JSON in this form: {"match": true|false, "is_non_answer": true|false}')
r = cli.complete_json(sysm, usr)
if not (isinstance(r.get("match"), bool) and isinstance(r.get("is_non_answer"), bool)):
raise ValueError(f"{q.get('qid')}: judge reply lacks boolean match / is_non_answer: {str(r)[:80]}")
return r["match"] and not r["is_non_answer"]
def mentioned_in_dialogues(q, corpus):
"""Whether the entity that an abstention question calls undocumented is mentioned in the dialogues."""
rx = ((q.get("derivation") or {}).get("operands") or {}).get("absent_regex")
return bool(rx and re.search(rx, corpus, re.I))
def validate(a) -> None:
"""Question validation: answerability and memory requirement, judged by an LLM.
Oracle probe: the model answers from the dialogues of the evidence visits only. No-memory probe: the
model answers from the question alone. A separate judge call compares each answer with the gold. A
question is sound iff the oracle answer is correct, the no-memory answer is not (not required on the axes
of MEMEBENCH_NO_MEMORY_EXEMPT_AXES), for an abstention question the entity it calls undocumented is not
mentioned in any dialogue, and for an axis-1 never-again question the entity is not named in a dialogue after
its last documented date, up to the checkpoint. A question whose probes or judge calls fail gets a row with
`error` and is not sound.
"""
dia = {r["visit_number"]: r for r in
(json.loads(l) for l in open(BASE / "dialogues.jsonl") if l.strip())}
corpus = " ".join(t["text"] for v in sorted(dia) for t in dia[v]["turns"])
ck = {c["qid"]: c for c in (json.loads(l) for l in open(BASE / "checkpoints.jsonl") if l.strip())}
exempt = no_memory_exempt_axes()
questions = []
for f in sorted(glob.glob(str(BASE / "questions_axis*.jsonl"))):
questions += [json.loads(l) for l in open(f) if l.strip()]
def dialogues_text(vis, ckpt):
parts = []
for v in vis:
if v in dia and (not isinstance(ckpt, int) or v <= ckpt):
turns = dia[v].get("turns") or []
body = " ".join(f'{t.get("speaker","")}: {t.get("text","")}' for t in turns)
parts.append(f"[visit {v}, {dia[v].get('date')}] {body}")
return "\n\n".join(parts)[:16000]
def later_mention(q, ckpt):
"""For an axis-1 never-again question: the first later visit (after gt_date, up to the checkpoint) whose
dialogue names every content word of the entity, or None."""
if not str(q.get("axis", "")).startswith("1") or q.get("form") != "never_again":
return None
entity = str((q.get("axis_specific") or {}).get("entity") or "")
words = set(re.findall(r"[a-z][a-z0-9]{3,}", entity.lower()))
if not words:
return None
for v in sorted(dia):
if (dia[v].get("date") or "") <= str(q.get("gt_date") or "") or (isinstance(ckpt, int) and v > ckpt):
continue
text = " ".join(t.get("text", "") for t in dia[v].get("turns") or []).lower()
if all(w in text for w in words):
return v
return None
def one(q):
if not has_text(q):
return {"qid": q.get("qid"), "axis": q.get("axis"), "grounding": q.get("grounding"),
"oracle_ok": False, "nomem_ok": False, "absent_entity_mentioned": False,
"never_again_mentioned_later": None, "sound": False,
"oracle_ans": "", "nomem_ans": "", "problem": "no question text"}
cli = client()
cinfo = ck.get(q["qid"], {})
cv = cinfo.get("ckpt_visit")
as_of = cinfo.get("ckpt_date")
ev = q.get("evidence_visits") or ([q.get("gt_visit")] if q.get("gt_visit") else [])
ctx = dialogues_text(ev, cv)
oracle_ans = answer_probe(cli, q, ctx, as_of=as_of)
nomem_ans = answer_probe(cli, q, "")
for ans in (oracle_ans, nomem_ans):
if ans.startswith("__ERROR__"):
raise RuntimeError(f"{q['qid']}: answer probe failed ({ans[10:]})")
later = later_mention(q, cv)
oracle_ok = judge(cli, q, oracle_ans)
nomem_ok = judge(cli, q, nomem_ans)
mentioned = mentioned_in_dialogues(q, corpus)
needs_memory = int(str(q.get("axis")).split("_")[0]) in exempt or not nomem_ok
return {"qid": q["qid"], "axis": q.get("axis"), "grounding": q.get("grounding"),
"oracle_ok": oracle_ok, "nomem_ok": nomem_ok, "absent_entity_mentioned": mentioned,
"never_again_mentioned_later": later,
"sound": bool(oracle_ok and needs_memory and not mentioned and not later),
"oracle_ans": oracle_ans[:200], "nomem_ans": nomem_ans[:200]}
def checked(q):
"""one(q), or a row with the error when a probe or judge call fails."""
try:
return one(q)
except Exception as e:
return {"qid": q.get("qid"), "axis": q.get("axis"), "grounding": q.get("grounding"),
"oracle_ok": False, "nomem_ok": False, "absent_entity_mentioned": False,
"never_again_mentioned_later": None, "sound": False,
"oracle_ans": "", "nomem_ans": "", "error": f"{type(e).__name__}: {str(e)[:200]}"}
print(f"validating {len(questions)} questions, engine={ENGINE}, {a.workers} workers", flush=True)
res = []
with Progress("validate", total=len(questions), out=str(BASE / "progress_validate.json")) as pg:
with ThreadPoolExecutor(max_workers=a.workers) as ex:
futs = [ex.submit(checked, q) for q in questions]
for f in as_completed(futs):
r = f.result()
res.append(r)
pg.tick(ok=not r.get("error"))
(BASE / "validation.jsonl").write_text(
"\n".join(json.dumps(r, ensure_ascii=False) for r in res) + "\n")
verdict = {r["qid"]: {"sound": r["sound"], "oracle_ok": r["oracle_ok"], "nomem_ok": r["nomem_ok"],
**({"error": r["error"]} if r.get("error") else {})}
for r in res}
for bf in sorted(glob.glob(str(BASE / "questions_axis*.jsonl"))):
rows = [json.loads(l) for l in open(bf)]
hit = False
for q in rows:
if q.get("qid") in verdict:
q["_validation"] = verdict[q["qid"]]
hit = True
if hit:
with open(bf, "w", encoding="utf-8") as fh:
for q in rows:
fh.write(json.dumps(q, ensure_ascii=False) + "\n")
print(f" embedded _validation into banks ({len(verdict)} verdicts)")
n = len(res)
sound = sum(1 for r in res if r["sound"])
unans = [r for r in res if not r["oracle_ok"] and not r.get("error")]
guess = [r for r in res if r["nomem_ok"]]
mentioned = [r for r in res if r["absent_entity_mentioned"]]
later = [r for r in res if r.get("never_again_mentioned_later")]
errors = [r for r in res if r.get("error")]
print(f"\n SOUND: {sound}/{n}")
print(f" errors (probe or judge failed): {len(errors)}")
print(f" unanswerable (oracle could not): {len(unans)}")
print(f" guessable (no-memory answered): {len(guess)}")
print(f" abstention entity mentioned in the dialogues: {len(mentioned)}")
print(f" never-again entity named in a later dialogue: {len(later)}")
print(f" sound by axis: {dict(Counter(r['axis'].split('_')[0] for r in res if r['sound']))}")
if unans:
print("\n unanswerable:")
for r in unans[:8]:
print(f" {r['qid']}: oracle said {r['oracle_ans'][:60]!r}")
if guess:
print("\n guessable:")
for r in guess[:8]:
print(f" {r['qid']}: no-mem said {r['nomem_ans'][:60]!r}")
if errors:
print("\n errors:")
for r in errors[:8]:
print(f" {r['qid']}: {r['error'][:100]}")
print(f"\n-> {BASE/'validation.jsonl'}")
def count_errors() -> None:
"""Print the number of validation rows whose probes or judge calls failed (rows with `error`)."""
print(sum(1 for l in open(BASE / "validation.jsonl") if l.strip() and json.loads(l).get("error")))
def engines() -> dict:
"""The model of every LLM stage, as configured in build_patient.sh."""
x, g, d = model("extraction"), model("generation"), model("dialogue")
return {"extract": x, "evidence": g, "tags": x, "axes1-2_generate": g,
"axes3-8_discover": g, "synthetic_questions": g, "persona_gate": g, "dialogues": d, "validation": g}
def nlines(p):
return sum(1 for _ in open(p)) if os.path.exists(p) else 0
def manifest() -> None:
"""Write MANIFEST.json for a finished patient: counts, models and the file inventory."""
D = {}
for name in ["event_chain.jsonl", "evidence_chain.jsonl", "tags.jsonl", "vitals.json",
"dialogues.jsonl", "checkpoints.jsonl", "checkpoint_audit.json",
"validation.jsonl", "injection_registry.json", "injection_plan.json"]:
p = BASE / name
if p.exists():
D[name] = {"rows": nlines(p) if name.endswith(".jsonl") else None,
"bytes": p.stat().st_size}
q_files = sorted(glob.glob(str(BASE / "questions_axis*.jsonl")))
per_axis, sound_axis, synth = {}, {}, 0
total = sound = 0
for f in q_files:
ax = Path(f).stem.split("axis")[1]
rows = [json.loads(l) for l in open(f)]
per_axis[ax] = len(rows)
sa = sum(1 for q in rows if (q.get("_validation") or {}).get("sound"))
sound_axis[ax] = sa
synth += sum(1 for q in rows if "synthetic" in str(q.get("grounding", "")))
total += len(rows)
sound += sa
D[os.path.basename(f)] = {"rows": len(rows), "sound": sa}
depths = []
ap = BASE / "checkpoint_audit.json"
if ap.exists():
depths = sorted(r["depth"] for r in json.loads(ap.read_text()) if r.get("depth") is not None)
manifest = {
"patient_id": PATIENT,
"engines": engines(),
"questions": {"total": total, "sound": sound, "synthetic": synth,
"record": total - synth, "per_axis": per_axis, "sound_per_axis": sound_axis},
"corpus": {"dialogues": nlines(BASE / "dialogues.jsonl"),
"event_chain_visits": nlines(BASE / "event_chain.jsonl"),
"evidence_timelines": nlines(BASE / "evidence_chain.jsonl")},
"checkpoints": {"n": nlines(BASE / "checkpoints.jsonl"),
"depth_min": depths[0] if depths else None,
"depth_median": depths[len(depths) // 2] if depths else None,
"depth_max": depths[-1] if depths else None},
"no_memory_baseline": {
"note": "questions a no-record model answered correctly = do not require memory",
"guessable": sum(1 for l in open(BASE / "validation.jsonl")
if json.loads(l).get("nomem_ok")) if (BASE / "validation.jsonl").exists() else None},
"deliverables": D,
}
(BASE / "MANIFEST.json").write_text(json.dumps(manifest, ensure_ascii=False, indent=1))
print(f"MANIFEST, patient {PATIENT}")
print(f" questions: {total} ({total-synth} record + {synth} synthetic), SOUND {sound}")
print(f" corpus: {manifest['corpus']['dialogues']} dialogues, {manifest['corpus']['event_chain_visits']} visits")
print(f" checkpoints: {manifest['checkpoints']['n']}, "
f"depth median {manifest['checkpoints']['depth_median']} visits")
print(f" no-memory baseline: {manifest['no_memory_baseline']['guessable']} guessable (rest require memory)")
print(f" deliverables: {len(D)} files")
print(f"-> {BASE/'MANIFEST.json'}")
def main():
ap = argparse.ArgumentParser(description="Stage G: checkpoints, validation, manifest.")
sp = ap.add_subparsers(dest="cmd", required=True)
for name in ("assign", "audit", "count-errors", "manifest"):
sp.add_parser(name)
sp.add_parser("validate").add_argument("--workers", type=int, default=8)
a = ap.parse_args()
{"assign": assign, "audit": audit, "validate": lambda: validate(a), "count-errors": count_errors,
"manifest": manifest}[a.cmd]()
if __name__ == "__main__":
main()
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