Add Liquid d1 benchmark results

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 |
@@ -89,6 +96,8 @@ _Sorted by AP. Bold marks the best value in each quality column; lower FP and FN
 
 ## Perch Lint Bench
 
+![Public Perch Lint Bench average precision, with Jev and Liquid d1 in their brand colors](assets/perch-lint-bench-public.png)
+
 [Public pairs](https://huggingface.co/datasets/perchscan/perch-lint-bench-public)
 
 ### Public — 214 pairs
@@ -96,6 +105,7 @@ _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 +117,8 @@ _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 +131,9 @@ 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 +144,7 @@ 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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