Add Liquid d1 benchmark results

Scores Liquid d1 on the public and private bug, security, and semantic lint tracks using the frozen Perch question pack. Includes aggregate metrics, public predictions, and a runnable adapter.

README.md

@@ -19,6 +19,7 @@ Pairs come from open-source projects in Rust, JavaScript, TypeScript, Python, Go
 | [SemiF](https://github.com/TheoLeeCJ/SemIf) | [Beam endpoint](https://app.beam.cloud/v1/models/jev/semif/invoke) |
 | [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | Runpod H100 80 GB pod (US-NE-1) |
 | [Span-01](https://www.respan.ai/docs/documentation/span-01/concept) | [Respan scoring API](https://api.respan.ai/api/v1/scores) (`span-01-pro`) |
+| [Liquid d1](https://docs.liquid.ai/lfm/models/decision-models) | [Liquid decision API](https://api.liquid.ai/decisions/v1/systemone) (`d1:free`) |
 
 
 ## Reading the results
@@ -33,12 +34,15 @@ Pairs come from open-source projects in Rust, JavaScript, TypeScript, Python, Go
 
 [Public pairs](https://huggingface.co/datasets/perchscan/bug-pairs-public)
 
+**Liquid d1 at the configured bug floors:** 26 of 4,440 public methods raised alerts, finding 13 of 1,861 labelled bugs.
+
 ### Public — 2,220 pairs
 
 | Model | AP | Precision | Recall | F1 | TP | FP | FN | Pair order | Inference cost |
 | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
 | [DiffusionGemma Jev](https://github.com/razorback16/openjev) | **46.8%** | 47.1% | 33.4% | 39.1% | 621 | 698 | 1,240 | **52.4%** | $0.239 |
-| [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | 46.2% | **55.6%** | 5.9% | 10.6% | 109 | **87** | 1,752 | 42.1% | $0.620 |
+| [Liquid d1](https://docs.liquid.ai/lfm/models/decision-models) | **46.8%** | 50.0% | 0.7% | 1.4% | 13 | **13** | 1,848 | 49.2% | Free tier |
+| [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | 46.2% | **55.6%** | 5.9% | 10.6% | 109 | 87 | 1,752 | 42.1% | $0.620 |
 | [SemiF](https://github.com/TheoLeeCJ/SemIf) | 44.6% | 43.9% | 43.0% | 43.4% | 800 | 1,023 | 1,061 | 44.9% | $0.239 |
 | [Span-01](https://www.respan.ai/docs/documentation/span-01/concept) | 43.8% | 44.7% | 25.3% | 32.3% | 471 | 583 | 1,390 | 45.0% | $0.182 |
 | [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | 42.1% | 42.2% | **81.9%** | **55.7%** | **1,525** | 2,087 | **336** | 45.3% | ~$0.210 |
@@ -50,7 +54,8 @@ _Sorted by AP. Bold marks the best value in each quality column; lower FP and FN
 | Model | AP | Precision | Recall | F1 | TP | FP | FN | Pair order | Inference cost |
 | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
 | [DiffusionGemma Jev](https://github.com/razorback16/openjev) | **47.9%** | 49.4% | 35.8% | 41.5% | 152 | 156 | 272 | **53.1%** | $0.054 |
-| [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | 47.0% | **51.9%** | 6.6% | 11.7% | 28 | **26** | 396 | 48.1% | $0.138 |
+| [Liquid d1](https://docs.liquid.ai/lfm/models/decision-models) | 47.2% | **75.0%** | 1.4% | 2.8% | 6 | **2** | 418 | 48.1% | Free tier |
+| [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | 47.0% | 51.9% | 6.6% | 11.7% | 28 | 26 | 396 | 48.1% | $0.138 |
 | [Span-01](https://www.respan.ai/docs/documentation/span-01/concept) | 45.5% | 46.5% | 28.5% | 35.4% | 121 | 139 | 303 | 44.6% | $0.040 |
 | [SemiF](https://github.com/TheoLeeCJ/SemIf) | 44.5% | 44.0% | 44.3% | 44.2% | 188 | 239 | 236 | 42.4% | $0.054 |
 | [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | 42.7% | 42.4% | **79.5%** | **55.3%** | **337** | 458 | **87** | 47.9% | ~$0.047 |
@@ -68,10 +73,11 @@ _Sorted by AP. Bold marks the best value in each quality column; lower FP and FN
 | Model | AP | Precision | Recall | F1 | TP | FP | FN | Pair order | Inference cost |
 | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
 | [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | **51.5%** | **53.1%** | 34.6% | 41.9% | 395 | 349 | 747 | 52.6% | $0.507 |
+| [Liquid d1](https://docs.liquid.ai/lfm/models/decision-models) | 49.4% | 47.8% | 55.6% | 51.4% | 635 | 693 | 507 | 52.2% | Free tier |
 | [DiffusionGemma Jev](https://github.com/razorback16/openjev) | 48.2% | 49.7% | 33.1% | 39.7% | 378 | 382 | 764 | **53.7%** | $0.208 |
 | [Span-01](https://www.respan.ai/docs/documentation/span-01/concept) | 47.6% | 48.6% | 21.0% | 29.3% | 240 | **254** | 902 | 43.7% | $0.251 |
 | [SemiF](https://github.com/TheoLeeCJ/SemIf) | 43.9% | 43.6% | **100.0%** | **60.7%** | **1,142** | 1,479 | **0** | 46.2% | $0.208 |
-| [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | 42.9% | 43.6% | **100.0%** | 60.7% | **1,142** | 1,480 | **0** | 45.9% | ~$1.432 |
+| [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | 42.9% | 43.6% | **100.0%** | **60.7%** | **1,142** | 1,480 | **0** | 45.9% | ~$1.432 |
 
 _Sorted by AP. Bold marks the best value in each quality column; lower FP and FN are better._
 
@@ -81,6 +87,7 @@ _Sorted by AP. Bold marks the best value in each quality column; lower FP and FN
 | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
 | [DiffusionGemma Jev](https://github.com/razorback16/openjev) | **49.2%** | 49.4% | 21.9% | 30.3% | 87 | 89 | 311 | **51.5%** | $0.074 |
 | [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | 48.9% | **50.6%** | 20.6% | 29.3% | 82 | 80 | 316 | 40.2% | $0.182 |
+| [Liquid d1](https://docs.liquid.ai/lfm/models/decision-models) | 47.8% | 49.5% | 34.2% | 40.4% | 136 | 139 | 262 | 45.5% | Free tier |
 | [Span-01](https://www.respan.ai/docs/documentation/span-01/concept) | 47.8% | 45.1% | 8.0% | 13.6% | 32 | **39** | 366 | 50.5% | $0.091 |
 | [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | 46.9% | 45.3% | **100.0%** | **62.4%** | **398** | 480 | **0** | 51.3% | ~$0.528 |
 | [SemiF](https://github.com/TheoLeeCJ/SemIf) | 46.8% | 45.3% | **100.0%** | **62.4%** | **398** | 480 | **0** | 50.0% | $0.074 |
@@ -96,6 +103,7 @@ _Sorted by AP. Bold marks the best value in each quality column; lower FP and FN
 | Model | AP | Precision | Recall | F1 | TP | FP | FN | Pair order | Inference cost |
 | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
 | [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | **95.0%** | **99.1%** | 50.0% | 66.5% | 107 | **1** | 107 | **96.3%** | $0.048 |
+| [Liquid d1](https://docs.liquid.ai/lfm/models/decision-models) | 92.5% | 98.1% | 49.1% | 65.4% | 105 | 2 | 109 | 93.5% | Free tier |
 | [DiffusionGemma Jev](https://github.com/razorback16/openjev) | 87.0% | 91.1% | **52.8%** | **66.9%** | **113** | 11 | **101** | 86.4% | $0.018 |
 | [Span-01](https://www.respan.ai/docs/documentation/span-01/concept) | 79.6% | 81.7% | 50.0% | 62.0% | 107 | 24 | 107 | 88.3% | $0.014 |
 | [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | 50.5% | 50.9% | 38.8% | 44.0% | 83 | 80 | 131 | 51.4% | ~$0.010 |
@@ -107,7 +115,8 @@ _Sorted by AP. Bold marks the best value in each quality column; lower FP and FN
 
 | Model | AP | Precision | Recall | F1 | TP | FP | FN | Pair order | Inference cost |
 | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | **94.3%** | **97.4%** | **53.5%** | **69.1%** | **38** | **1** | **33** | **94.4%** | $0.016 |
+| [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | **94.3%** | **97.4%** | **53.5%** | **69.1%** | **38** | **1** | **33** | 94.4% | $0.016 |
+| [Liquid d1](https://docs.liquid.ai/lfm/models/decision-models) | 92.4% | 97.2% | 49.3% | 65.4% | 35 | **1** | 36 | **97.2%** | Free tier |
 | [Span-01](https://www.respan.ai/docs/documentation/span-01/concept) | 83.2% | 84.2% | 45.1% | 58.7% | 32 | 6 | 39 | 91.5% | $0.005 |
 | [DiffusionGemma Jev](https://github.com/razorback16/openjev) | 83.0% | 86.4% | **53.5%** | 66.1% | **38** | 6 | **33** | 85.9% | $0.006 |
 | [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | 51.4% | 50.7% | 50.7% | 50.7% | 36 | 35 | 35 | 56.3% | ~$0.003 |
@@ -120,7 +129,9 @@ The bug and security tables use Perch's configured question floors. Semantic lin
 
 Span-01 uses its [behavior-scoring API](https://www.respan.ai/docs/documentation/span-01/quickstart): the target method is the output, its saved Perch context is the input, and all applicable checks are scored as behaviors in one call. Issue scores use `p_present`; semantic lint uses `p_absent` for the rule being satisfied. `p_not_observable` is saved separately. This is a different model interface from Jev's questions.
 
-Inference costs cover scoring the method sides in each table. Jev costs are estimated from API-reported input tokens at [TypeSafe's published rate](https://typesafe.ai/blog/introducing-system-one-models-and-jev); for semantic lint, input-token usage was measured by replaying the same 570 requests with `jev-1.13.0`. Span-01 costs use API-reported input tokens at [Respan's published rate](https://www.respan.ai/docs/documentation/span-01/concept). Beam costs are API-reported. CLM cost is an estimated share of a 38.4-minute H100 run costing $2.23 total, allocated by embedding count. Costs are not ranked.
+Liquid d1 received the same Perch method state and all applicable questions in one API call per method. Semantic lint used the same compiled `ensure` rule as Jev.
+
+Inference costs cover scoring the method sides in each table. Jev costs are estimated from API-reported input tokens at [TypeSafe's published rate](https://typesafe.ai/blog/introducing-system-one-models-and-jev); for semantic lint, input-token usage was measured by replaying the same 570 requests with `jev-1.13.0`. Span-01 costs use API-reported input tokens at [Respan's published rate](https://www.respan.ai/docs/documentation/span-01/concept). Beam costs are API-reported. CLM cost is an estimated share of a 38.4-minute H100 run costing $2.23 total, allocated by embedding count. Costs are not ranked. Liquid d1 was tested on the `d1:free` API tier; a paid per-token price was not published.
 
 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.
 
@@ -131,6 +142,7 @@ JevLike (192-byte input), Laya (512-token input), and Kev (packed-branch limit)
 - [`predictions/current/`](https://huggingface.co/datasets/perchscan/benchmark-results/tree/main/predictions/current): public method probabilities from this run. Private predictions are not published.
 - [`benchmark.py`](https://huggingface.co/datasets/perchscan/benchmark-results/blob/main/benchmark.py) and [`question-pack.json`](https://huggingface.co/datasets/perchscan/benchmark-results/blob/main/question-pack.json): reproducible current-pack scorer and frozen questions.
 - [`span01.py`](https://huggingface.co/datasets/perchscan/benchmark-results/blob/main/span01.py): calls Span-01 with all applicable behaviors in one request per method, saves resumable predictions, and scores the completed dataset.
+- [`liquid.py`](https://huggingface.co/datasets/perchscan/benchmark-results/blob/main/liquid.py): calls Liquid d1 with the frozen Perch question pack, saves resumable predictions, and scores each track.
 - [`perch-lint-bench-public`](https://huggingface.co/datasets/perchscan/perch-lint-bench-public): paired rules and its runnable scorer.
 
 To run Jev with the current pack, set `TYPESAFE_API_KEY` or `PERCH_API_KEY` in your environment:

liquid.py

@@ -0,0 +1,198 @@
+#!/usr/bin/env python3
+"""Run Liquid d1 on Perch bug, security, and semantic lint method pairs."""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import importlib.util
+import json
+import math
+import sys
+import time
+from pathlib import Path
+from urllib.error import HTTPError, URLError
+from urllib.request import Request, urlopen
+
+
+BASE = Path(__file__).resolve().parent
+API = 'https://api.liquid.ai/decisions/v1/systemone'
+MODEL = 'd1:free'
+PROTOCOL = 'liquid-d1-perch-scan-pack-v1'
+LINT_PROTOCOL = 'liquid-d1-perch-semantic-ensure-v1'
+LINT_URL = 'https://huggingface.co/datasets/perchscan/perch-lint-bench-public/resolve/main/benchmark.jsonl'
+
+
+def module(path: Path, name: str):
+    spec = importlib.util.spec_from_file_location(name, path)
+    result = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(result)
+    return result
+
+
+core = module(BASE / 'benchmark.py', 'perch_benchmark')
+
+
+def semantic_question(ensure: str) -> dict:
+    text = ' '.join(ensure.split())
+    return {'type': 'noul', 'instructions': 'Is `rule` true of the code below?',
+            'criteria': {'true': text, 'false': f'Not so: {text}'}}
+
+
+def load_data(domain: str, dataset: Path | None):
+    if domain != 'lint':
+        return core.load_pairs(domain, dataset)
+    raw = dataset.read_bytes() if dataset else urlopen(LINT_URL, timeout=120).read()
+    pairs = [json.loads(line) for line in raw.splitlines() if line]
+    if not pairs or len({pair['pair_id'] for pair in pairs}) != len(pairs):
+        raise ValueError('Empty or duplicate lint pairs')
+    return pairs, hashlib.sha256(raw).hexdigest()
+
+
+def ask(key: str, state: dict, questions: dict, pair_id: str, role: str):
+    payload = json.dumps({'model': MODEL, 'state': state, 'questions': questions}, ensure_ascii=False).encode()
+    for attempt in range(8):
+        request = Request(API, data=payload, method='POST',
+                          headers={'Authorization': 'Bearer ' + key,
+                                   'Content-Type': 'application/json'})
+        try:
+            with urlopen(request, timeout=120) as response:
+                result = json.load(response)
+            break
+        except HTTPError as exc:
+            if exc.code not in (429, 500, 502, 503, 504) or attempt == 7:
+                detail = exc.read(400).decode(errors='replace')
+                raise RuntimeError(f'Liquid HTTP {exc.code} for {pair_id}:{role}: {detail}') from None
+            delay = exc.headers.get('Retry-After')
+            try:
+                seconds = float(delay)
+            except (TypeError, ValueError):
+                seconds = min(60, 2 ** attempt)
+            time.sleep(min(120, max(1, seconds)))
+        except URLError:
+            if attempt == 7:
+                raise RuntimeError(f'Liquid network failure for {pair_id}:{role}') from None
+            time.sleep(min(60, 2 ** attempt))
+    if result.get('model') != MODEL or set(result.get('answers', {})) != set(questions):
+        raise ValueError(f'Liquid returned another model or incomplete answers for {pair_id}:{role}')
+    return result['answers'], result.get('usage') or {}
+
+
+def predict(pair: dict, role: str, domain: str, pack: dict, key: str):
+    if domain == 'lint':
+        questions = {'semantic': semantic_question(pair['rule']['ensure'])}
+        selected = None
+    else:
+        selected = core.scan_questions(pack, domain, pair['language'])
+        questions = {q['name']: q['question'] for q in selected}
+    answers, usage = ask(key, pair[role]['state'], questions, pair['pair_id'], role)
+    if domain == 'lint':
+        true_probability = core.probability(answers['semantic']['noul'])
+        score = 1 - true_probability
+        row = {'score': score, 'rule_true_probability': true_probability}
+        protocol = LINT_PROTOCOL
+    else:
+        relevant = core.detection_questions(selected, domain)
+        question_scores = {q['name']: core.raised_score(q, answers[q['name']]['noul'])
+                           for q in relevant}
+        score, alert = core.scan_result(question_scores, selected, domain, .5)
+        row = {'score': score, 'alert': alert, 'question_scores': question_scores,
+               'pack_sha256': pack['pack_sha256']}
+        protocol = PROTOCOL
+    row.update({'domain': domain, 'pair_id': pair['pair_id'], 'role': role,
+                'model_revision': MODEL, 'protocol_id': protocol, 'usage': usage})
+    return row
+
+
+def score_lint(pairs: list[dict], predictions: dict, digest: str):
+    items = [(pair[role]['labels'][pair['domain']], predictions[pair['pair_id'], role]['score'])
+             for pair in pairs for role in ('before', 'after')]
+    tp = sum(y == 1 and s > .7 for y, s in items)
+    fp = sum(y == 0 and s > .7 for y, s in items)
+    fn = sum(y == 1 and s <= .7 for y, s in items)
+    tn = sum(y == 0 and s <= .7 for y, s in items)
+    eligible = [(predictions[p['pair_id'], 'before']['score'],
+                 predictions[p['pair_id'], 'after']['score']) for p in pairs
+                if p['before']['labels'][p['domain']] == 1 and
+                   p['after']['labels'][p['domain']] == 0]
+    return {'domain': 'lint', 'model_revision': MODEL, 'protocol_id': LINT_PROTOCOL,
+            'threshold': .7, 'alert_rule': 'rule violation score > 0.7',
+            'benchmark_sha256': digest, 'benchmark_pairs': len(pairs),
+            'benchmark_methods': len(items), 'average_precision': core.average_precision(items),
+            'precision': tp / (tp + fp) if tp + fp else 0,
+            'recall': tp / (tp + fn) if tp + fn else 0,
+            'f1': 2 * tp / (2 * tp + fp + fn) if 2 * tp + fp + fn else 0,
+            'confusion': {'tp': tp, 'fp': fp, 'tn': tn, 'fn': fn},
+            'vulnerable_or_buggy_before_above_fixed_after':
+                {'correct': sum(a > b for a, b in eligible),
+                 'ties': sum(a == b for a, b in eligible), 'eligible': len(eligible)}}
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument('--domain', choices=('bug', 'security', 'lint'), required=True)
+    parser.add_argument('--dataset', type=Path)
+    parser.add_argument('--question-pack', type=Path, default=BASE / 'question-pack.json')
+    parser.add_argument('--predictions', type=Path, required=True)
+    parser.add_argument('--report', type=Path, required=True)
+    parser.add_argument('--limit', type=int, default=0)
+    parser.add_argument('--score-saved', action='store_true')
+    parser.add_argument('--key-stdin', action='store_true')
+    args = parser.parse_args()
+    if args.limit < 0:
+        parser.error('--limit must be nonnegative')
+    pack = core.load_pack(args.question_pack)
+    pairs, digest = load_data(args.domain, args.dataset)
+    expected = {(p['pair_id'], role) for p in pairs for role in ('before', 'after')}
+    predictions = {}
+    if args.predictions.exists():
+        for line in args.predictions.read_text().splitlines():
+            if not line:
+                continue
+            row = json.loads(line)
+            identity = row['pair_id'], row['role']
+            if identity not in expected or identity in predictions or row['domain'] != args.domain or row['model_revision'] != MODEL:
+                raise ValueError(f'Unknown, duplicate, or wrong-model prediction: {identity}')
+            predictions[identity] = row
+    pending = [(p, role) for p in pairs for role in ('before', 'after')
+               if (p['pair_id'], role) not in predictions]
+    if args.score_saved and pending:
+        raise ValueError(f'Incomplete: {len(pending)} method sides missing')
+    if args.limit:
+        pending = pending[:args.limit]
+    if pending and not args.score_saved:
+        if not args.key_stdin:
+            raise ValueError('Use --key-stdin to provide the Liquid API key through standard input')
+        key = sys.stdin.readline().strip()
+        if not key:
+            raise ValueError('Missing Liquid API key on standard input')
+        args.predictions.parent.mkdir(parents=True, exist_ok=True)
+        with args.predictions.open('a') as output:
+            for index, (pair, role) in enumerate(pending, 1):
+                row = predict(pair, role, args.domain, pack, key)
+                predictions[pair['pair_id'], role] = row
+                output.write(json.dumps(row, sort_keys=True) + '\n')
+                output.flush()
+                if index % 50 == 0 or index == len(pending):
+                    print(f'{args.domain}: {len(predictions)}/{len(expected)} method sides', file=sys.stderr, flush=True)
+    if predictions.keys() != expected:
+        print(f'{args.domain}: incomplete {len(predictions)}/{len(expected)}; rerun to resume', file=sys.stderr)
+        return
+    if args.domain == 'lint':
+        report = score_lint(pairs, predictions, digest)
+    else:
+        report = core.report(pairs, predictions, args.domain, digest, MODEL,
+                             pack['protocol_id'], .5, pack)
+        report['protocol_id'] = PROTOCOL
+    report['input_tokens'] = sum((row.get('usage') or {}).get('input_tokens', 0)
+                                 for row in predictions.values())
+    report['output_tokens'] = sum((row.get('usage') or {}).get('output_tokens', 0)
+                                  for row in predictions.values())
+    report['api_calls'] = len(predictions)
+    args.report.parent.mkdir(parents=True, exist_ok=True)
+    args.report.write_text(json.dumps(report, indent=2, sort_keys=True) + '\n')
+    print(json.dumps(report, sort_keys=True))
+
+
+if __name__ == '__main__':
+    main()

results-current.jsonl

@@ -28,3 +28,9 @@
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results.jsonl

@@ -70,3 +70,9 @@
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