Update GPT-6 Luna benchmark results

README.md

@@ -29,7 +29,7 @@ Pairs come from open-source projects in Rust, JavaScript, TypeScript, Python, Go
 
 ## Reading the results
 
-Each line shows how many issues a model finds and what percentage of clean methods it wrongly flags as its score cutoff changes. Higher and farther left is better.
+Each line shows recall minus the false-positive rate as the score cutoff changes. Higher is better; farther left means fewer clean methods flagged. The dashed zero line means issues and clean methods are flagged at equal rates.
 
 The tables use each run's recorded alert settings. A value of 20% under ‘Clean methods wrongly flagged’ means one in five clean methods received an alert. Models are ranked by recall minus that percentage. Bold marks the best full-set value in each column; cost compares priced runs captured here.
 
@@ -39,14 +39,14 @@ Failed requests count as incorrect decisions: a failed issue is a miss; a failed
 
 [Public pairs](https://huggingface.co/datasets/perchscan/bug-pairs-public)
 
-![Bugs recall versus false alerts](assets/bug-tradeoff.svg)
+![Bugs: recall minus false-positive rate across cutoffs](assets/bug-tradeoff.svg)
 
 ### Public — 2,220 pairs
 
 | Model | Recall | Clean methods wrongly flagged | Recall − false-positive rate | Failed requests | Inference cost |
 | --- | ---: | ---: | ---: | ---: | ---: |
-| [DiffusionGemma Jev](https://github.com/razorback16/openjev) | 33.4% | 27.1% | **6.3%** | **0** | $0.239 |
-| [GPT-6 Luna Decisions](https://developers.openai.com/api/docs/guides/decisions) | 24.7% | 19.0% | 5.7% | 98 | $1.146 |
+| [GPT-6 Luna Decisions](https://developers.openai.com/api/docs/guides/decisions) | 48.7% | 38.7% | **10.0%** | **0** | $1.102 |
+| [DiffusionGemma Jev](https://github.com/razorback16/openjev) | 33.4% | 27.1% | 6.3% | **0** | $0.239 |
 | [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | 17.7% | 12.7% | 5.0% | **0** | $0.551 |
 | [SemiF](https://github.com/TheoLeeCJ/SemIf) | 43.0% | 39.7% | 3.3% | **0** | $0.239 |
 | [TypeLLM](https://typellm.ai/docs) | 8.3% | 5.5% | 2.7% | **0** | $0.576 |
@@ -62,7 +62,7 @@ Failed requests count as incorrect decisions: a failed issue is a miss; a failed
 
 | Model | Recall | Clean methods wrongly flagged | Recall − false-positive rate | Failed requests | Inference cost |
 | --- | ---: | ---: | ---: | ---: | ---: |
-| [GPT-6 Luna Decisions](https://developers.openai.com/api/docs/guides/decisions) | 29.5% | 20.3% | **9.2%** | 18 | $0.258 |
+| [GPT-6 Luna Decisions](https://developers.openai.com/api/docs/guides/decisions) | 52.4% | 41.3% | **11.1%** | **0** | $0.247 |
 | [DiffusionGemma Jev](https://github.com/razorback16/openjev) | 35.8% | 27.3% | 8.6% | **0** | $0.054 |
 | [TypeLLM](https://typellm.ai/docs) | 11.3% | 5.8% | 5.6% | **0** | $0.129 |
 | [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | 20.0% | 15.6% | 4.5% | **0** | $0.123 |
@@ -76,7 +76,7 @@ Failed requests count as incorrect decisions: a failed issue is a miss; a failed
 
 [Public pairs](https://huggingface.co/datasets/perchscan/security-pairs-public)
 
-![Security recall versus false alerts](assets/security-tradeoff.svg)
+![Security: recall minus false-positive rate across cutoffs](assets/security-tradeoff.svg)
 
 ### Public — 1,311 pairs
 
@@ -84,9 +84,9 @@ Failed requests count as incorrect decisions: a failed issue is a miss; a failed
 | --- | ---: | ---: | ---: | ---: | ---: |
 | [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | 34.6% | 23.6% | **11.0%** | **0** | $0.507 |
 | [TypeLLM](https://typellm.ai/docs) | 27.8% | 17.4% | 10.5% | **0** | $0.566 |
+| [GPT-6 Luna Decisions](https://developers.openai.com/api/docs/guides/decisions) | 54.1% | 44.5% | 9.7% | **0** | $1.469 |
 | [Liquid d1](https://docs.liquid.ai/lfm/models/decision-models) | 55.6% | 46.8% | 8.8% | **0** | Free tier |
 | [DiffusionGemma Jev](https://github.com/razorback16/openjev) | 33.1% | 25.8% | 7.3% | **0** | **$0.208** |
-| [GPT-6 Luna Decisions](https://developers.openai.com/api/docs/guides/decisions) | 22.6% | 15.9% | 6.6% | 50 | $1.775 |
 | [Span-01](https://www.respan.ai/docs/documentation/span-01/concept) | 21.0% | 17.2% | 3.9% | **0** | $0.251 |
 | [CodeQL](https://codeql.github.com/)* | 4.8% | 1.5% | 3.3% | — | — |
 | [GLiNER-2.5-Decide](https://huggingface.co/fastino/GLiNER2.5-Decide) | 2.1% | **1.6%** | 0.5% | 6 | $0.434 |
@@ -99,8 +99,8 @@ Failed requests count as incorrect decisions: a failed issue is a miss; a failed
 
 | Model | Recall | Clean methods wrongly flagged | Recall − false-positive rate | Failed requests | Inference cost |
 | --- | ---: | ---: | ---: | ---: | ---: |
-| [Liquid d1](https://docs.liquid.ai/lfm/models/decision-models) | 34.2% | 29.0% | **5.2%** | **0** | Free tier |
-| [GPT-6 Luna Decisions](https://developers.openai.com/api/docs/guides/decisions) | 11.1% | 6.5% | 4.6% | 11 | $0.643 |
+| [GPT-6 Luna Decisions](https://developers.openai.com/api/docs/guides/decisions) | 40.5% | 31.5% | **9.0%** | **0** | $0.530 |
+| [Liquid d1](https://docs.liquid.ai/lfm/models/decision-models) | 34.2% | 29.0% | 5.2% | **0** | Free tier |
 | [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | 20.6% | 16.7% | 3.9% | **0** | $0.182 |
 | [DiffusionGemma Jev](https://github.com/razorback16/openjev) | 21.9% | 18.5% | 3.3% | **0** | **$0.074** |
 | [TypeLLM](https://typellm.ai/docs) | 8.8% | 6.2% | 2.5% | **0** | $0.203 |
@@ -113,14 +113,14 @@ Failed requests count as incorrect decisions: a failed issue is a miss; a failed
 
 [Public pairs](https://huggingface.co/datasets/perchscan/perch-lint-bench-public)
 
-![Perch Lint Bench recall versus false alerts](assets/lint-tradeoff.svg)
+![Perch Lint Bench: recall minus false-positive rate across cutoffs](assets/lint-tradeoff.svg)
 
 ### Public — 214 pairs
 
 | Model | Recall | Clean methods wrongly flagged | Recall − false-positive rate | Failed requests | Inference cost |
 | --- | ---: | ---: | ---: | ---: | ---: |
-| [TypeLLM](https://typellm.ai/docs) | **61.7%** | 2.8% | **58.9%** | **0** | $0.050 |
-| [GPT-6 Luna Decisions](https://developers.openai.com/api/docs/guides/decisions) | 51.4% | 0.5% | 50.9% | **0** | $0.095 |
+| [GPT-6 Luna Decisions](https://developers.openai.com/api/docs/guides/decisions) | **76.6%** | 4.7% | **72.0%** | **0** | $0.093 |
+| [TypeLLM](https://typellm.ai/docs) | 61.7% | 2.8% | 58.9% | **0** | $0.050 |
 | [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | 50.0% | 0.5% | 49.5% | **0** | $0.048 |
 | [Liquid d1](https://docs.liquid.ai/lfm/models/decision-models) | 49.1% | 0.9% | 48.1% | **0** | Free tier |
 | [DiffusionGemma Jev](https://github.com/razorback16/openjev) | 52.8% | 5.1% | 47.7% | **0** | $0.018 |
@@ -133,10 +133,10 @@ Failed requests count as incorrect decisions: a failed issue is a miss; a failed
 
 | Model | Recall | Clean methods wrongly flagged | Recall − false-positive rate | Failed requests | Inference cost |
 | --- | ---: | ---: | ---: | ---: | ---: |
-| [TypeLLM](https://typellm.ai/docs) | **59.2%** | 5.6% | **53.5%** | **0** | $0.016 |
+| [GPT-6 Luna Decisions](https://developers.openai.com/api/docs/guides/decisions) | **84.5%** | 7.0% | **77.5%** | **0** | $0.030 |
+| [TypeLLM](https://typellm.ai/docs) | 59.2% | 5.6% | 53.5% | **0** | $0.016 |
 | [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | 53.5% | 1.4% | 52.1% | **0** | $0.016 |
 | [Liquid d1](https://docs.liquid.ai/lfm/models/decision-models) | 49.3% | 1.4% | 47.9% | **0** | Free tier |
-| [GPT-6 Luna Decisions](https://developers.openai.com/api/docs/guides/decisions) | 50.7% | 2.8% | 47.9% | **0** | $0.030 |
 | [DiffusionGemma Jev](https://github.com/razorback16/openjev) | 53.5% | 8.5% | 45.1% | **0** | $0.006 |
 | [Span-01](https://www.respan.ai/docs/documentation/span-01/concept) | 45.1% | 8.5% | 36.6% | **0** | $0.005 |
 | [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | 50.7% | 49.3% | 1.4% | **0** | **~$0.003** |
@@ -157,7 +157,7 @@ Inference costs cover scoring the method sides in each table. Jev costs are esti
 
 JevLike (192-byte input), Laya (512-token input), and Kev (packed-branch limit) could not score every full-context method. NanoJev was unavailable on Beam. They are excluded rather than ranked on partial results.
 
-GPT-6 Luna Decisions receives each method's saved context with all applicable checks as predicate questions in one request. True and false criteria are retained. Semantic lint scores invert the probability that the rule is satisfied. Costs use API-reported input tokens at [$0.10 per million](https://developers.openai.com/api/docs/guides/decisions#pricing-and-availability); output and cache operations carry no extra charge.
+GPT-6 Luna Decisions receives each method's saved context with all applicable checks as two-option choice questions in one request. True and false criteria define the options. Semantic lint scores invert the probability that the rule is satisfied. Costs use API-reported input tokens at [$0.10 per million](https://developers.openai.com/api/docs/guides/decisions#pricing-and-availability); output and cache operations carry no extra charge.
 
 ## Files and reproduction
 
@@ -203,7 +203,7 @@ To run OpenAI Decisions, download each public `benchmark.jsonl` into
 `public/bug/`, `public/security/` and `public/lint/`, then set `OPENAI_API_KEY`:
 
 ```sh
-python3 openai_decisions.py --manifest openai-decisions-manifest.json --out decisions-results
+python3 openai_decisions.py --protocol choice --manifest openai-decisions-manifest.json --out decisions-results
 ```
 
 The runner preserves the configured question floors and 70% semantic-lint

openai_decisions.py

@@ -2,7 +2,7 @@
 """Run OpenAI Decisions on the Perch bug, security and semantic lint benchmarks.
 
 Each method is independent. Applicable checks share its complete saved context.
-Predicates retain the frozen question and answer criteria. No repair is shown.
+Predicates and choices retain the frozen question and answer criteria. No repair is shown.
 Failed requests are saved once and counted as wrong decisions, without scores.
 """
 from __future__ import annotations
@@ -27,6 +27,7 @@ MODEL_ID = 'openai-decisions'
 API = 'https://api.openai.com/v1/decisions'
 PRICE = .10
 PROTOCOL = 'openai-decisions-perch-predicate-v1'
+CHOICE_PROTOCOL = 'openai-decisions-perch-choice-v1'
 spec = importlib.util.spec_from_file_location('perch_benchmark', ROOT / 'benchmark.py')
 core = importlib.util.module_from_spec(spec)
 spec.loader.exec_module(core)
@@ -74,47 +75,83 @@ def questions_for(pair, domain, pack):
 
 def predicate(check):
     question = check['question']
+    if not question.get('criteria'):
+        return {'type': 'predicate', 'name': check['name'],
+                'instructions': question['instructions']}
     return {'type': 'predicate', 'name': check['name'],
             'instructions': question['instructions'].strip() + '\nTrue: ' +
                 question['criteria']['true'].strip() + '\nFalse: ' +
                 question['criteria']['false'].strip()}
 
 
-def payload_for(pair, role, domain, pack):
+def decision_question(check, protocol=PROTOCOL):
+    question = check['question']
+    criteria = question.get('criteria') or {}
+    if protocol == CHOICE_PROTOCOL and 'true' in criteria and 'false' in criteria:
+        return {'type': 'choice', 'name': check['name'],
+                'instructions': question['instructions'],
+                'choices': [{'value': value, 'description': criteria[value]}
+                            for value in ('true', 'false')]}
+    return predicate(check)
+
+
+def payload_for(pair, role, domain, pack, protocol=PROTOCOL):
     checks = questions_for(pair, domain, pack)
     if not checks:
         raise ValueError('No applicable detection questions')
     context = 'Perch method context (JSON):\n' + json.dumps(
         pair[role]['state'], ensure_ascii=False, separators=(',', ':'))
-    return {'model': MODEL, 'input': context, 'questions': [predicate(c) for c in checks]}, checks
+    return {'model': MODEL, 'input': context,
+            'questions': [decision_question(c, protocol) for c in checks]}, checks
 
 
-def prediction(pair, role, domain, pack, response, elapsed, request_id):
+def prediction(pair, role, domain, pack, response, elapsed, request_id, protocol=PROTOCOL):
     checks = questions_for(pair, domain, pack)
     if response.get('model') != MODEL:
         raise ValueError('Decisions returned a different model')
     answers = response.get('answers', [])
     if len(answers) != len(checks) or {a['name'] for a in answers} != {c['name'] for c in checks}:
         raise ValueError('Decisions returned missing, duplicate or unexpected answers')
-    if any(a.get('type') != 'predicate' for a in answers):
-        types = ', '.join(a['name'] + ':' + str(a.get('type')) for a in answers if a.get('type') != 'predicate')
+    expected_types = {c['name']: decision_question(c, protocol)['type'] for c in checks}
+    if any(a.get('type') != expected_types[a['name']] for a in answers):
+        types = ', '.join(a['name'] + ':' + str(a.get('type')) for a in answers
+                          if a.get('type') != expected_types[a['name']])
         raise AnswerError('Decisions returned ' + types, response, request_id, elapsed)
-    probabilities = {a['name']: core.probability(a['probability']) for a in answers}
+    probabilities = {}
+    try:
+        for answer in answers:
+            if answer['type'] == 'choice':
+                options = answer['probabilities']
+                if len(options) != 2 or {p['value'] for p in options} != {'true', 'false'}:
+                    raise ValueError('Expected exactly true and false probabilities')
+                values = {p['value']: core.probability(p['probability']) for p in options}
+                if abs(sum(values.values()) - 1) > .001:
+                    raise ValueError('Choice probabilities do not sum to one')
+                probabilities[answer['name']] = values['true']
+            else:
+                probabilities[answer['name']] = core.probability(answer['probability'])
+    except (KeyError, TypeError, ValueError) as exc:
+        raise AnswerError('Invalid Decisions probabilities: ' + str(exc), response, request_id, elapsed) from exc
     scores = {c['name']: core.raised_score(c, probabilities[c['name']]) for c in checks}
     score, alert = (scores['semantic'], scores['semantic'] > .7) if domain == 'lint' else core.scan_result(scores, checks, domain, .5)
     usage = response.get('usage', {})
     if not isinstance(usage.get('input_tokens'), int) or usage['input_tokens'] < 0:
         raise ValueError('Decisions did not return billable input-token usage')
-    return {'pair_id': pair['pair_id'], 'role': role, 'domain': domain,
-            'model_id': MODEL_ID, 'model_revision': MODEL, 'protocol_id': PROTOCOL,
+    row = {'pair_id': pair['pair_id'], 'role': role, 'domain': domain,
+            'model_id': MODEL_ID if protocol == PROTOCOL else MODEL_ID + '-choice',
+            'model_revision': MODEL, 'protocol_id': protocol,
             'pack_sha256': pack['pack_sha256'] if domain != 'lint' else None,
             'score': score, 'alert': alert, 'question_scores': scores,
-            'predicate_probabilities': probabilities, 'usage': usage,
+            'usage': usage,
             'latency_seconds': elapsed, 'request_id': request_id}
+    row['predicate_probabilities' if protocol == PROTOCOL else 'true_probabilities'] = probabilities
+    if protocol == CHOICE_PROTOCOL:
+        row['answers'] = answers
+    return row
 
 
-def predict(key, pair, role, domain, pack, timeout, limiter):
-    payload, _ = payload_for(pair, role, domain, pack)
+def predict(key, pair, role, domain, pack, timeout, limiter, protocol=PROTOCOL):
+    payload, _ = payload_for(pair, role, domain, pack, protocol)
     body = json.dumps(payload, ensure_ascii=False).encode()
     estimated_tokens = max(1, len(body) // 3)
     started = time.monotonic()
@@ -126,7 +163,7 @@ def predict(key, pair, role, domain, pack, timeout, limiter):
             with urlopen(request, timeout=timeout) as result:
                 response = json.load(result)
                 request_id = result.headers.get('x-request-id')
-            return prediction(pair, role, domain, pack, response, time.monotonic() - started, request_id)
+            return prediction(pair, role, domain, pack, response, time.monotonic() - started, request_id, protocol)
         except HTTPError as exc:
             detail = json.loads(exc.read()).get('error', {})
             code = detail.get('code')
@@ -138,7 +175,7 @@ def predict(key, pair, role, domain, pack, timeout, limiter):
     raise AssertionError('unreachable')
 
 
-def report(group, pack):
+def report(group, pack, protocol=PROTOCOL):
     pairs, predictions, failures = group['pairs'], group['predictions'], group['failures']
     domain = group['domain']
     counts = dict(tp=0, fp=0, fn=0, tn=0)
@@ -164,13 +201,20 @@ def report(group, pack):
     tokens = sum(r.get('usage', {}).get('input_tokens', 0) for r in [*predictions.values(), *failures.values()])
     latencies = sorted(r['latency_seconds'] for r in predictions.values())
     ordering = {'correct': correct, 'ties': ties, 'eligible': eligible}
-    return {'domain': domain, 'track': group['split'], 'model_id': MODEL_ID,
-        'model_revision': MODEL, 'protocol_id': PROTOCOL, 'run_id': 'openai-decisions-20261006',
+    per_question = Counter(a['name'] for r in failures.values() for a in r.get('answers', [])
+                           if a.get('type') == 'refusal')
+    return {'domain': domain, 'track': group['split'],
+        'model_id': MODEL_ID if protocol == PROTOCOL else MODEL_ID + '-choice',
+        'model_revision': MODEL, 'protocol_id': protocol,
+        'run_id': 'openai-decisions-20261006' if protocol == PROTOCOL else 'openai-decisions-choice-20261006',
         'benchmark_sha256': group['digest'], 'benchmark_pairs': len(pairs), 'benchmark_methods': 2 * len(pairs),
         'answered_methods': len(predictions), 'failed_methods': len(failures),
         'complete': len(predictions) + len(failures) == 2 * len(pairs),
         'failure_policy': 'failed positive counts as FN; failed negative counts as FP; failed pair counts as incorrect ordering',
         'failure_counts': dict(Counter(r['error'] for r in failures.values())),
+        'failure_counts_per_question': dict(per_question),
+        'refused_methods': sum(any(a.get('type') == 'refusal' for a in r.get('answers', []))
+                               for r in failures.values()),
         'confusion': counts, 'precision': tp / (tp + fp) if tp + fp else 0,
         'recall': tp / (tp + fn) if tp + fn else 0, 'false_positive_rate': fp / (fp + tn) if fp + tn else 0,
         'f1': 2 * tp / (2 * tp + fp + fn) if 2 * tp + fp + fn else 0,
@@ -198,7 +242,9 @@ def main():
     parser.add_argument('--timeout', type=float, default=45)
     parser.add_argument('--budget', type=float, default=5)
     parser.add_argument('--score-saved', action='store_true')
+    parser.add_argument('--protocol', choices=('predicate', 'choice'), default='predicate')
     args = parser.parse_args()
+    protocol = PROTOCOL if args.protocol == 'predicate' else CHOICE_PROTOCOL
     pack = core.load_pack(args.question_pack)
     groups, pending_groups = [], []
     for dataset in json.loads(args.manifest.read_text())['datasets']:
@@ -218,7 +264,7 @@ def main():
                 identity = r['pair_id'], r['role']
                 if identity not in expected or identity in preds or identity in fails:
                     raise ValueError('Invalid or duplicate saved outcome')
-                if r['dataset_sha256'] != digest or r['protocol_id'] != PROTOCOL or r['model_revision'] != MODEL:
+                if r['dataset_sha256'] != digest or r['protocol_id'] != protocol or r['model_revision'] != MODEL:
                     raise ValueError('Saved outcome uses a different dataset, model or protocol')
                 if r.get('pack_sha256') != (pack['pack_sha256'] if dataset['domain'] != 'lint' else None):
                     raise ValueError('Saved outcome uses a different question pack')
@@ -230,7 +276,7 @@ def main():
                                if (pair['pair_id'], role) not in preds and (pair['pair_id'], role) not in fails])
     pending = [job for row in zip_longest(*pending_groups) for job in row if job is not None]
     def save_reports():
-        reports = [report(g, pack) for g in groups]
+        reports = [report(g, pack, protocol) for g in groups]
         for g, result in zip(groups, reports):
             (g['directory'] / 'report.json').write_text(json.dumps(result, indent=2) + '\n')
         status = {'model': MODEL, 'reports': reports,
@@ -256,7 +302,7 @@ def main():
             job = next(iterator, None)
             if job:
                 group, pair, role = job
-                active[pool.submit(predict, key, pair, role, group['domain'], pack, args.timeout, limiter)] = job
+                active[pool.submit(predict, key, pair, role, group['domain'], pack, args.timeout, limiter, protocol)] = job
         for _ in range(args.workers):
             submit()
         try:
@@ -273,7 +319,7 @@ def main():
                         raise
                     except Exception as exc:
                         row = {'pair_id': pair['pair_id'], 'role': role, 'domain': group['domain'],
-                               'model_revision': MODEL, 'protocol_id': PROTOCOL,
+                               'model_revision': MODEL, 'protocol_id': protocol,
                                'pack_sha256': pack['pack_sha256'] if group['domain'] != 'lint' else None,
                                'status': 'failed', 'error': str(exc),
                                'usage': getattr(exc, 'usage', {}), 'answers': getattr(exc, 'answers', []),

predictions/current/bug/openai-decisions.jsonl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a7801755a7ff36856ef8f18e7c0975f5c9bfc192c258ccb729b34bcda98f8a41
-size 3696789
+oid sha256:e8ed2c9159299b95911a2879284777ee04d29f3c6394c8c99cf59902b9eaf82e
+size 5317543

predictions/current/lint/openai-decisions.jsonl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9f9450d90e0ac177fb260e2abb2ef63d61fc1b84b6e53fb2f4bf1d778afd98e3
-size 318004
+oid sha256:f3c5817f3973524f258865ac1f972da2bf1a8b143215bec23bb2c0c52ebf40fa
+size 394083

predictions/current/security/openai-decisions.jsonl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5b945451a0ea46391c49bdc8b1fa628c95299cd7547a3ceee6cdcba0168eeaff
-size 3950511
+oid sha256:26ce3b6291e12a8be72af0b42a3f475a5a4324e79054ae019e91c82cc3f31e88
+size 14582673

results-current.jsonl

@@ -46,9 +46,9 @@
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-{"alert_rule": "any issue score > max(0.5, question min)", "answered_methods": 867, "average_precision_answered_methods": 0.5169176433965138, "benchmark_methods": 878, "benchmark_pairs": 439, "benchmark_sha256": "a4b81728e9e520cf1c6f342974bec369d72f5b7d8aabc1c9cf2aceba966f8051", "complete": true, "confusion": {"fn": 354, "fp": 31, "tn": 449, "tp": 44}, "domain": "security", "f1": 0.18604651162790697, "failed_methods": 11, "failure_counts": {"Decisions returned cwe_120:refusal": 6, "Decisions returned cwe_770:refusal": 2, "Decisions returned cwe_863:refusal": 2, "Decisions returned refusal or non-predicate answer (original response)": 1}, "failure_policy": "failed positive counts as FN; failed negative counts as FP; failed pair counts as incorrect ordering", "false_positive_rate": 0.06458333333333334, "inference_cost_basis": "Decisions API-reported input tokens at $0.10/M; no output or cache charges; transport failures without returned usage excluded", "inference_cost_usd": 0.6434506000000001, "inference_endpoint": "https://api.openai.com/v1/decisions", "inference_host": "OpenAI Decisions API", "input_tokens": 6434506, "latency_basis": "Per-method elapsed time, including client rate-limit waits and 429 backoff", "latency_median_seconds": 3.465080959023908, "latency_p95_seconds": 4.828431333997287, "model_id": "openai-decisions", "model_revision": "gpt-6-luna", "pair_order": 0.5402010050251256, "pair_order_correct": 215, "pair_order_eligible": 398, "precision": 0.5866666666666667, "protocol_id": "openai-decisions-perch-predicate-v1", "question_pack_sha256": "afe116cc8461552aaca996f8f0b639cdf5e42e9d8291c59995482309b5c330dc", "recall": 0.11055276381909548, "run_id": "openai-decisions-20261006", "threshold": 0.5, "track": "private", "vulnerable_or_buggy_before_above_fixed_after": {"correct": 215, "eligible": 398, "ties": 19}}
+{"alert_rule": "any issue score > max(0.5, question min)", "answered_methods": 4440, "average_precision_answered_methods": 0.4831610495664901, "benchmark_methods": 4440, "benchmark_pairs": 2220, "benchmark_sha256": "32bc68bc85d5535ad28e103adbfa0c09c73e09d0f53a53f7ca35ec219a96e6e2", "complete": true, "confusion": {"fn": 955, "fp": 997, "tn": 1582, "tp": 906}, "domain": "bug", "f1": 0.4814027630180659, "failed_methods": 0, "failure_counts": {}, "failure_counts_per_question": {}, "failure_policy": "failed positive counts as FN; failed negative counts as FP; failed pair counts as incorrect ordering", "false_positive_rate": 0.38658394726638234, "inference_cost_basis": "Decisions API-reported input tokens at $0.10/M; no output or cache charges; transport failures without returned usage excluded", "inference_cost_usd": 1.1018942, "inference_endpoint": "https://api.openai.com/v1/decisions", "inference_host": "OpenAI Decisions API", "input_tokens": 11018942, "latency_basis": "Per-method elapsed time, including client rate-limit waits and 429 backoff", "latency_median_seconds": 3.428778040979523, "latency_p95_seconds": 5.043582332989899, "model_id": "openai-decisions", "model_revision": "gpt-6-luna", "pair_order": 0.4797979797979798, "pair_order_correct": 855, "pair_order_eligible": 1782, "precision": 0.47609038360483447, "protocol_id": "openai-decisions-perch-choice-v1", "question_pack_sha256": "afe116cc8461552aaca996f8f0b639cdf5e42e9d8291c59995482309b5c330dc", "recall": 0.48683503492745833, "refused_methods": 0, "run_id": "openai-decisions-choice-20261006", "threshold": 0.5, "track": "public", "vulnerable_or_buggy_before_above_fixed_after": {"correct": 855, "eligible": 1782, "ties": 105}}
+{"alert_rule": "any issue score > max(0.5, question min)", "answered_methods": 2622, "average_precision_answered_methods": 0.5007223297716433, "benchmark_methods": 2622, "benchmark_pairs": 1311, "benchmark_sha256": "2407059caa3486371749884138b997d89043eec646e548ee8f44ba3ab08514b8", "complete": true, "confusion": {"fn": 524, "fp": 658, "tn": 822, "tp": 618}, "domain": "security", "f1": 0.511166253101737, "failed_methods": 0, "failure_counts": {}, "failure_counts_per_question": {}, "failure_policy": "failed positive counts as FN; failed negative counts as FP; failed pair counts as incorrect ordering", "false_positive_rate": 0.4445945945945946, "inference_cost_basis": "Decisions API-reported input tokens at $0.10/M; no output or cache charges; transport failures without returned usage excluded", "inference_cost_usd": 1.4690454000000002, "inference_endpoint": "https://api.openai.com/v1/decisions", "inference_host": "OpenAI Decisions API", "input_tokens": 14690454, "latency_basis": "Per-method elapsed time, including client rate-limit waits and 429 backoff", "latency_median_seconds": 3.770231959002558, "latency_p95_seconds": 5.241487499995856, "model_id": "openai-decisions", "model_revision": "gpt-6-luna", "pair_order": 0.48398576512455516, "pair_order_correct": 544, "pair_order_eligible": 1124, "precision": 0.4843260188087774, "protocol_id": "openai-decisions-perch-choice-v1", "question_pack_sha256": "afe116cc8461552aaca996f8f0b639cdf5e42e9d8291c59995482309b5c330dc", "recall": 0.5411558669001751, "refused_methods": 0, "run_id": "openai-decisions-choice-20261006", "threshold": 0.5, "track": "public", "vulnerable_or_buggy_before_above_fixed_after": {"correct": 544, "eligible": 1124, "ties": 117}}
+{"alert_rule": "rule violation probability > 0.7", "answered_methods": 428, "average_precision_answered_methods": 0.9607007525697188, "benchmark_methods": 428, "benchmark_pairs": 214, "benchmark_sha256": "534f6c2abddc507c40f4597607e97e07168c544011d617b83aa39e08abb35d75", "complete": true, "confusion": {"fn": 50, "fp": 10, "tn": 204, "tp": 164}, "domain": "lint", "f1": 0.845360824742268, "failed_methods": 0, "failure_counts": {}, "failure_counts_per_question": {}, "failure_policy": "failed positive counts as FN; failed negative counts as FP; failed pair counts as incorrect ordering", "false_positive_rate": 0.04672897196261682, "inference_cost_basis": "Decisions API-reported input tokens at $0.10/M; no output or cache charges; transport failures without returned usage excluded", "inference_cost_usd": 0.0929731, "inference_endpoint": "https://api.openai.com/v1/decisions", "inference_host": "OpenAI Decisions API", "input_tokens": 929731, "latency_basis": "Per-method elapsed time, including client rate-limit waits and 429 backoff", "latency_median_seconds": 3.5743396670150105, "latency_p95_seconds": 4.67788199998904, "model_id": "openai-decisions", "model_revision": "gpt-6-luna", "pair_order": 0.9626168224299065, "pair_order_correct": 206, "pair_order_eligible": 214, "precision": 0.9425287356321839, "protocol_id": "openai-decisions-perch-choice-v1", "question_pack_sha256": null, "recall": 0.7663551401869159, "refused_methods": 0, "run_id": "openai-decisions-choice-20261006", "threshold": 0.7, "track": "public", "vulnerable_or_buggy_before_above_fixed_after": {"correct": 206, "eligible": 214, "ties": 3}}
+{"alert_rule": "any issue score > max(0.5, question min)", "answered_methods": 996, "average_precision_answered_methods": 0.5038335285710763, "benchmark_methods": 996, "benchmark_pairs": 498, "benchmark_sha256": "3df4ade65fedd1128d30bc17c9c9869c8ab9bd526e666156d0e16685e78f8a73", "complete": true, "confusion": {"fn": 202, "fp": 236, "tn": 336, "tp": 222}, "domain": "bug", "f1": 0.5034013605442177, "failed_methods": 0, "failure_counts": {}, "failure_counts_per_question": {}, "failure_policy": "failed positive counts as FN; failed negative counts as FP; failed pair counts as incorrect ordering", "false_positive_rate": 0.4125874125874126, "inference_cost_basis": "Decisions API-reported input tokens at $0.10/M; no output or cache charges; transport failures without returned usage excluded", "inference_cost_usd": 0.2474223, "inference_endpoint": "https://api.openai.com/v1/decisions", "inference_host": "OpenAI Decisions API", "input_tokens": 2474223, "latency_basis": "Per-method elapsed time, including client rate-limit waits and 429 backoff", "latency_median_seconds": 3.7011702089803293, "latency_p95_seconds": 5.054382875008741, "model_id": "openai-decisions", "model_revision": "gpt-6-luna", "pair_order": 0.49875311720698257, "pair_order_correct": 200, "pair_order_eligible": 401, "precision": 0.4847161572052402, "protocol_id": "openai-decisions-perch-choice-v1", "question_pack_sha256": "afe116cc8461552aaca996f8f0b639cdf5e42e9d8291c59995482309b5c330dc", "recall": 0.5235849056603774, "refused_methods": 0, "run_id": "openai-decisions-choice-20261006", "threshold": 0.5, "track": "private", "vulnerable_or_buggy_before_above_fixed_after": {"correct": 200, "eligible": 401, "ties": 29}}
+{"alert_rule": "any issue score > max(0.5, question min)", "answered_methods": 878, "average_precision_answered_methods": 0.5143518871191308, "benchmark_methods": 878, "benchmark_pairs": 439, "benchmark_sha256": "a4b81728e9e520cf1c6f342974bec369d72f5b7d8aabc1c9cf2aceba966f8051", "complete": true, "confusion": {"fn": 237, "fp": 151, "tn": 329, "tp": 161}, "domain": "security", "f1": 0.4535211267605634, "failed_methods": 0, "failure_counts": {}, "failure_counts_per_question": {}, "failure_policy": "failed positive counts as FN; failed negative counts as FP; failed pair counts as incorrect ordering", "false_positive_rate": 0.3145833333333333, "inference_cost_basis": "Decisions API-reported input tokens at $0.10/M; no output or cache charges; transport failures without returned usage excluded", "inference_cost_usd": 0.5297818000000001, "inference_endpoint": "https://api.openai.com/v1/decisions", "inference_host": "OpenAI Decisions API", "input_tokens": 5297818, "latency_basis": "Per-method elapsed time, including client rate-limit waits and 429 backoff", "latency_median_seconds": 3.751356124994345, "latency_p95_seconds": 5.058746583003085, "model_id": "openai-decisions", "model_revision": "gpt-6-luna", "pair_order": 0.5301507537688442, "pair_order_correct": 211, "pair_order_eligible": 398, "precision": 0.5160256410256411, "protocol_id": "openai-decisions-perch-choice-v1", "question_pack_sha256": "afe116cc8461552aaca996f8f0b639cdf5e42e9d8291c59995482309b5c330dc", "recall": 0.4045226130653266, "refused_methods": 0, "run_id": "openai-decisions-choice-20261006", "threshold": 0.5, "track": "private", "vulnerable_or_buggy_before_above_fixed_after": {"correct": 211, "eligible": 398, "ties": 21}}
+{"alert_rule": "rule violation probability > 0.7", "answered_methods": 142, "average_precision_answered_methods": 0.9427382409373941, "benchmark_methods": 142, "benchmark_pairs": 71, "benchmark_sha256": "a6e9f2e4ee8ae77bdaf23af46920d97d1ba47375dc9ce03b950cdbe95c2777f9", "complete": true, "confusion": {"fn": 11, "fp": 5, "tn": 66, "tp": 60}, "domain": "lint", "f1": 0.8823529411764706, "failed_methods": 0, "failure_counts": {}, "failure_counts_per_question": {}, "failure_policy": "failed positive counts as FN; failed negative counts as FP; failed pair counts as incorrect ordering", "false_positive_rate": 0.07042253521126761, "inference_cost_basis": "Decisions API-reported input tokens at $0.10/M; no output or cache charges; transport failures without returned usage excluded", "inference_cost_usd": 0.0297267, "inference_endpoint": "https://api.openai.com/v1/decisions", "inference_host": "OpenAI Decisions API", "input_tokens": 297267, "latency_basis": "Per-method elapsed time, including client rate-limit waits and 429 backoff", "latency_median_seconds": 3.594590458989842, "latency_p95_seconds": 4.666571665991796, "model_id": "openai-decisions", "model_revision": "gpt-6-luna", "pair_order": 0.9295774647887324, "pair_order_correct": 66, "pair_order_eligible": 71, "precision": 0.9230769230769231, "protocol_id": "openai-decisions-perch-choice-v1", "question_pack_sha256": null, "recall": 0.8450704225352113, "refused_methods": 0, "run_id": "openai-decisions-choice-20261006", "threshold": 0.7, "track": "private", "vulnerable_or_buggy_before_above_fixed_after": {"correct": 66, "eligible": 71, "ties": 1}}

test_openai_decisions.py

@@ -1,4 +1,5 @@
 import unittest
+from unittest.mock import patch
 
 import openai_decisions as decisions
 
@@ -60,6 +61,67 @@ class DecisionsTests(unittest.TestCase):
         self.assertEqual(raised.exception.usage['input_tokens'], 123)
         self.assertEqual(raised.exception.answers, response['answers'])
 
+    def test_choice_payload_preserves_instructions_and_two_options(self):
+        payload, checks = decisions.payload_for(self.pair, 'before', 'bug', self.pack,
+                                               decisions.CHOICE_PROTOCOL)
+        for sent, check in zip(payload['questions'], checks):
+            self.assertEqual(sent['type'], 'choice')
+            self.assertEqual(sent['instructions'], check['question']['instructions'])
+            self.assertEqual(sent['choices'], [
+                {'value': v, 'description': check['question']['criteria'][v]}
+                for v in ('true', 'false')])
+        lint, _ = decisions.payload_for(self.pair, 'before', 'lint', self.pack,
+                                        decisions.CHOICE_PROTOCOL)
+        self.assertEqual(lint['questions'][0]['choices'][1]['description'],
+                         'Not so: ' + self.pair['rule']['ensure'])
+        check = {'name': 'plain', 'question': {'instructions': 'Is this safe?'}}
+        self.assertEqual(decisions.decision_question(check, decisions.CHOICE_PROTOCOL),
+                         {'type': 'predicate', 'name': 'plain', 'instructions': 'Is this safe?'})
+
+    def test_choice_reads_true_option_not_selected_choice_or_confidence(self):
+        response = {'model': decisions.MODEL, 'usage': {'input_tokens': 101},
+                    'answers': [{'type': 'choice', 'name': 'semantic', 'choice': 'false',
+                                 'confidence': .99, 'probabilities': [
+                                     {'value': 'false', 'probability': .9},
+                                     {'value': 'true', 'probability': .1}]}]}
+        row = decisions.prediction(self.pair, 'before', 'lint', self.pack, response,
+                                   .2, 'request', decisions.CHOICE_PROTOCOL)
+        self.assertAlmostEqual(row['score'], .9)
+        self.assertTrue(row['alert'])
+        self.assertEqual(row['true_probabilities'], {'semantic': .1})
+        self.assertEqual(row['protocol_id'], decisions.CHOICE_PROTOCOL)
+        response['answers'][0]['probabilities'][0]['value'] = 'true'
+        with self.assertRaises(decisions.AnswerError):
+            decisions.prediction(self.pair, 'before', 'lint', self.pack, response,
+                                 .2, 'request', decisions.CHOICE_PROTOCOL)
+
+    def test_mixed_choice_predicate_and_refusal_failure_counts(self):
+        checks = [
+            {'name': 'binary', 'question': {'instructions': 'Has a bug?',
+             'criteria': {'true': 'Bug', 'false': 'No bug'}}, 'type': 'noul', 'issue': {'on': True, 'type': 'defect'}},
+            {'name': 'plain', 'question': {'instructions': 'Has a bug?'}, 'type': 'noul', 'issue': {'on': True, 'type': 'defect'}}]
+        response = {'model': decisions.MODEL, 'usage': {'input_tokens': 111}, 'answers': [
+            {'type': 'predicate', 'name': 'plain', 'probability': .2},
+            {'type': 'choice', 'name': 'binary', 'probabilities': [
+                {'value': 'true', 'probability': .9}, {'value': 'false', 'probability': .1}]}]}
+        with patch.object(decisions, 'questions_for', return_value=checks):
+            row = decisions.prediction(self.pair, 'before', 'bug', self.pack, response,
+                                       .2, 'request', decisions.CHOICE_PROTOCOL)
+            self.assertTrue(row['alert'])
+            response['answers'][1] = {'name': 'binary', 'type': 'refusal'}
+            with self.assertRaises(decisions.AnswerError) as raised:
+                decisions.prediction(self.pair, 'after', 'bug', self.pack, response,
+                                     .2, 'request', decisions.CHOICE_PROTOCOL)
+        failure = {'error': str(raised.exception), 'answers': raised.exception.answers,
+                   'usage': raised.exception.usage}
+        group = {'pairs': [self.pair], 'predictions': {},
+                 'failures': {('repair', 'before'): failure, ('repair', 'after'): failure},
+                 'domain': 'bug', 'split': 'public', 'digest': 'test'}
+        result = decisions.report(group, self.pack, decisions.CHOICE_PROTOCOL)
+        self.assertEqual(result['confusion'], {'tp': 0, 'fp': 1, 'fn': 1, 'tn': 0})
+        self.assertEqual(result['failure_counts_per_question'], {'binary': 2})
+        self.assertEqual(result['refused_methods'], 2)
+
 
 if __name__ == '__main__':
     unittest.main()

test_tradeoff.py

@@ -0,0 +1,42 @@
+from pathlib import Path
+import tempfile
+import unittest
+import xml.etree.ElementTree as ET
+
+import tradeoff
+
+
+class GraphTests(unittest.TestCase):
+    def test_equal_alert_rates_have_zero_margin_at_both_ends(self):
+        # Unequal class sizes prevent subtracting raw counts by mistake.
+        curves = {model: ([(0, 0), (2, 7), (20, 10)], 10, 20) for model in tradeoff.ORDER}
+        with tempfile.TemporaryDirectory() as temp:
+            output = Path(temp) / 'graph.svg'
+            tradeoff.svg('bug', curves, output)
+            root = ET.fromstring(output.read_text())
+        paths = root.findall('{http://www.w3.org/2000/svg}polyline')
+        self.assertEqual(len(paths), len(tradeoff.ORDER))
+        points = [tuple(map(float, point.split(','))) for point in paths[0].attrib['points'].split()]
+        self.assertEqual(points[0][1], points[2][1])
+        self.assertLess(points[1][1], points[0][1])
+        self.assertGreater(points[2][0], points[1][0])
+        labels = [e.text for e in root.findall('{http://www.w3.org/2000/svg}text')]
+        self.assertIn('100%', labels)
+        self.assertIn('Zero means issues and clean methods are flagged at equal rates.',
+                      root.find('{http://www.w3.org/2000/svg}desc').text)
+        self.assertIn('Recall − false-positive rate (percentage points)', labels)
+
+    def test_negative_margins_remain_visible(self):
+        curves = {model: ([(0, 0), (10, 2), (20, 10)], 10, 20) for model in tradeoff.ORDER}
+        with tempfile.TemporaryDirectory() as temp:
+            output = Path(temp) / 'graph.svg'
+            tradeoff.svg('security', curves, output)
+            root = ET.fromstring(output.read_text())
+        path = root.find('{http://www.w3.org/2000/svg}polyline')
+        points = [tuple(map(float, point.split(','))) for point in path.attrib['points'].split()]
+        self.assertGreater(points[1][1], points[0][1])
+        self.assertLessEqual(points[1][1], 498)
+
+
+if __name__ == '__main__':
+    unittest.main()

tradeoff.py

@@ -11,7 +11,7 @@ import argparse
 from collections import defaultdict
 import html
 import json
-from math import isfinite
+from math import ceil, floor, isfinite
 from pathlib import Path
 from tempfile import TemporaryDirectory
 from urllib.request import urlopen
@@ -169,24 +169,29 @@ def svg(domain: str, curves: dict[str, tuple[list[tuple[int, int]], int, int]],
     width, height = 1120, 580
     left, right, top, bottom = 90, 40, 148, 82
     plot_w, plot_h = width - left - right, height - top - bottom
-    # All benchmarks use one complete plot with the same percentage axes.
+    # Subtract the diagonal so small ranking differences remain visible.
+    # Keep every cutoff and adapt only the vertical display range.
     x_max = 100
-    y_max = 1.0
+    step = 10 if domain == "lint" else 5
+    margins = [100 * (tp / positives - fp / negatives)
+               for points, positives, negatives in curves.values() for fp, tp in points]
+    y_min = min(-step, floor(min(margins) / step) * step)
+    y_max = max(step, ceil(max(margins) / step) * step)
 
     def x(fp: int, negatives: int) -> float:
         return left + (fp / negatives * 100) / x_max * plot_w
 
-    def y(tp: int, positives: int) -> float:
-        return top + (1 - (tp / positives) / y_max) * plot_h
+    def y(margin: float) -> float:
+        return top + (y_max - margin) / (y_max - y_min) * plot_h
 
     title = {"bug": "Bug detection", "security": "Security detection", "lint": "Perch Lint Bench"}[domain]
     out = [f'<svg xmlns="http://www.w3.org/2000/svg" width="{width}" height="{height}" viewBox="0 0 {width} {height}" role="img" aria-labelledby="title desc">',
-           f'<title id="title">{title}: recall versus false alerts</title>',
-           '<desc id="desc">Model score curves on the public benchmark. Higher and farther left is better.</desc>',
+           f'<title id="title">{title}: recall minus false-positive rate</title>',
+           '<desc id="desc">Public model curves across score cutoffs. The vertical axis subtracts the false-positive rate from recall. Zero means issues and clean methods are flagged at equal rates. Higher is better.</desc>',
            f'<rect width="{width}" height="{height}" rx="16" fill="#101318"/>',
            f'<defs><clipPath id="plot"><rect x="{left}" y="{top}" width="{plot_w}" height="{plot_h}"/></clipPath></defs>',
            f'<text x="{left}" y="43" fill="#f2f5fa" font-family="Arial,sans-serif" font-weight="700" font-size="28">{html.escape(title)}</text>',
-           f'<text x="{left}" y="70" fill="#aeb9c7" font-family="Arial,sans-serif" font-size="16">Public methods · recall versus false alerts</text>']
+           f'<text x="{left}" y="70" fill="#aeb9c7" font-family="Arial,sans-serif" font-size="16">Public methods · recall minus false-positive rate · higher is better</text>']
     for i, model in enumerate(ORDER):
         col = i % 4
         row = i // 4
@@ -199,19 +204,20 @@ def svg(domain: str, curves: dict[str, tuple[list[tuple[int, int]], int, int]],
         px = left + value / x_max * plot_w
         out += [f'<line x1="{px:.1f}" y1="{top}" x2="{px:.1f}" y2="{top + plot_h}" stroke="#29303a"/>',
                 f'<text x="{px:.1f}" y="{top + plot_h + 25}" text-anchor="middle" fill="#aeb9c7" font-family="Arial,sans-serif" font-size="14">{value}%</text>']
-    step = 20
-    for value in range(0, round(y_max * 100) + 1, step):
-        py = top + (1 - (value / 100) / y_max) * plot_h
+    for value in range(y_min, y_max + 1, step):
+        py = y(value)
         out += [f'<line x1="{left}" y1="{py:.1f}" x2="{left + plot_w}" y2="{py:.1f}" stroke="#29303a"/>',
-                f'<text x="{left - 12}" y="{py + 5:.1f}" text-anchor="end" fill="#aeb9c7" font-family="Arial,sans-serif" font-size="14">{value}%</text>']
+                f'<text x="{left - 12}" y="{py + 5:.1f}" text-anchor="end" fill="#aeb9c7" font-family="Arial,sans-serif" font-size="14">{value}</text>']
+    zero_y = y(0)
+    out.append(f'<line x1="{left}" y1="{zero_y:.1f}" x2="{left + plot_w}" y2="{zero_y:.1f}" stroke="#aeb9c7" stroke-width="1.5" stroke-dasharray="5 5"/>')
     for model in ORDER:
         points, positives, negatives = curves[model]
         visible = [(fp, tp) for fp, tp in points if fp / negatives * 100 <= x_max]
         if len(visible) > 1:
-            positions = " ".join(f"{x(fp, negatives):.2f},{y(tp, positives):.2f}" for fp, tp in visible)
+            positions = " ".join(f"{x(fp, negatives):.2f},{y(100 * (tp / positives - fp / negatives)):.2f}" for fp, tp in visible)
             out.append(f'<polyline points="{positions}" fill="none" stroke="{COLORS[model]}" stroke-width="2.5" stroke-linejoin="round" stroke-linecap="round" clip-path="url(#plot)"/>')
     out += [f'<text x="{left + plot_w / 2:.1f}" y="{height - 23}" text-anchor="middle" fill="#e1e7ef" font-family="Arial,sans-serif" font-size="17">Clean methods wrongly flagged →</text>',
-            f'<text transform="translate(23 {top + plot_h / 2:.1f}) rotate(-90)" text-anchor="middle" fill="#e1e7ef" font-family="Arial,sans-serif" font-size="17">Actual issues found (recall) →</text>',
+            f'<text transform="translate(23 {top + plot_h / 2:.1f}) rotate(-90)" text-anchor="middle" fill="#e1e7ef" font-family="Arial,sans-serif" font-size="14">Recall − false-positive rate (percentage points)</text>',
             '</svg>']
     output.parent.mkdir(parents=True, exist_ok=True)
     output.write_text("\n".join(out) + "\n")
@@ -220,7 +226,7 @@ def svg(domain: str, curves: dict[str, tuple[list[tuple[int, int]], int, int]],
 def render_sections(results: list[dict], curves_by_domain: dict[str, dict], output_dir: Path,
                     codeql_results: dict) -> str:
     sections = ["## Reading the results", "",
-                "Each line shows how many issues a model finds and what percentage of clean methods it wrongly flags as its score cutoff changes. Higher and farther left is better.", "",
+                "Each line shows recall minus the false-positive rate as the score cutoff changes. Higher is better; farther left means fewer clean methods flagged. The dashed zero line means issues and clean methods are flagged at equal rates.", "",
                 "The tables use each run's recorded alert settings. A value of 20% under ‘Clean methods wrongly flagged’ means one in five clean methods received an alert. Models are ranked by recall minus that percentage. Bold marks the best full-set value in each column; cost compares priced runs captured here.", ""]
     if any(r.get("failed_methods") for r in results):
         sections += ["Failed requests count as incorrect decisions: a failed issue is a miss; a failed clean method is a false positive. Failed cases receive no model score and remain incorrect across the plotted cutoffs.", ""]
@@ -243,7 +249,7 @@ def render_sections(results: list[dict], curves_by_domain: dict[str, dict], outp
         image = output_dir / f"{domain}-tradeoff.svg"
         svg(domain, curves_by_domain[domain], image)
         sections += [f"## {title}", "", f"[Public pairs](https://huggingface.co/datasets/perchscan/{link})", "",
-                     f"![{title} recall versus false alerts](assets/{domain}-tradeoff.svg)", "",
+                     f"![{title}: recall minus false-positive rate across cutoffs](assets/{domain}-tradeoff.svg)", "",
                      f"### Public — {public[0]['benchmark_pairs']:,} pairs", "",
                      results_table(public), ""]
         if domain in ("bug", "security"):