prometheus-ebm 0.1.0

PROMETHEUS-EBM: Benchmarking Epistemic Metacognition in AI Models

PROMETHEUS-EBM SDK

Benchmarking Epistemic Metacognition in AI Models

PROMETHEUS-EBM evaluates whether frontier AI models can recognize the limits of their own knowledge — not just answer questions, but understand when a question is unanswerable, ambiguous, or self-contradictory.

Companion to the Kaggle notebook: PROMETHEUS-EBM v5.0 — The full benchmark with live results from 5 frontier models.

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Why This Exists

Current benchmarks (MMLU, GPQA, HumanEval) test what a model knows. PROMETHEUS-EBM tests whether a model knows what it does not know.

This is a critical safety property. A model deployed in medicine, law, or finance that confidently answers when it should refuse is more dangerous than one that gets fewer questions right but knows its boundaries.

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The 4-Class Solvability Taxonomy

Every problem is classified into one of four epistemic categories:

Class	Description	Expected Model Behavior
Determinate	One clear answer exists	Answer confidently
Underdetermined	Multiple valid interpretations	Flag the ambiguity
Insufficient	Critical information is missing	Refuse to answer definitively
Contradictory	The premises conflict	Detect the contradiction

Models are scored on whether they correctly identify which category a problem falls into — not just whether they produce the correct final answer.

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Scoring Framework

Metric	Range	What It Measures
ECI (Epistemological Calibration Index)	0–1	Composite metacognition score
SDA (Solvability Detection Accuracy)	0–1	Can the model classify the problem type?
CA (Conditional Accuracy)	0–1	When it commits to an answer, is it correct?
RP (Refusal Precision)	0–1	When it refuses, was refusal appropriate?
ECE (Expected Calibration Error)	0–1	Does stated confidence match actual accuracy?
HGI (Hysteresis Gap Index)	0–1	Internal inconsistency (lower = better)
Brier Score	0–1	Calibration quality decomposed into Reliability, Resolution, Uncertainty
Type-2 D-Prime	-∞ to +∞	How well the model's confidence signal distinguishes correct from incorrect answers

ECI Composition

ECI = 0.30 × SDA  +  0.25 × CA  +  0.20 × RP  +  0.15 × (1 - ECE)  +  0.10 × (1 - HSS)

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Installation

pip install prometheus-ebm

# With specific provider support:
pip install "prometheus-ebm[anthropic]"   # For Claude API
pip install "prometheus-ebm[openai]"      # For OpenAI API
pip install "prometheus-ebm[all]"         # All providers

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Quick Start

Compare Multiple Models

from prometheus_ebm import PrometheusRunner, RunConfig

config = RunConfig(
    mode="compare",
    models=[
        "anthropic/claude-opus-4-6@default",
        "anthropic/claude-sonnet-4-6@default",
        "google/gemini-3.1-pro-preview",
        "deepseek-ai/deepseek-v3.2",
        "deepseek-ai/deepseek-r1-0528",
    ],
    provider="kaggle",          # No API key needed
    n_items=200,                # Standard dataset (200 base problems)
    stress_decision_ratio=0.40, # EXTENDED mode stress
    stress_clarity_ratio=0.20,
)

runner = PrometheusRunner(config)
results = runner.run()
results.export("comparison.csv")

Deep Probe a Single Model (1,000 Items)

config = RunConfig(
    mode="deep_probe",
    models=["anthropic/claude-opus-4-6"],
    provider="anthropic",
    api_key="sk-ant-...",
    n_items=1000,
    stress_decision_ratio=0.30,
    bootstrap_iterations=3000,
)

runner = PrometheusRunner(config)
results = runner.run()
results.export("opus_deep_probe.csv")

Use with OpenRouter (Access 100+ Models)

config = RunConfig(
    mode="compare",
    models=["anthropic/claude-opus-4-6", "google/gemini-3.1-pro"],
    provider="openrouter",
    api_key="sk-or-...",
)

Use with OpenAI

config = RunConfig(
    mode="deep_probe",
    models=["gpt-5.4"],
    provider="openai",
    api_key="sk-...",
    n_items=200,
)

Scoring Only (Bring Your Own Data)

If you already have model responses and just need the ECI/Brier/D-Prime scores:

from prometheus_ebm import ECIScorer, BrierDecomposition, Type2DPrime

scorer = ECIScorer()

# Compute individual components
sda = ECIScorer.compute_sda(predicted_classes, true_classes)
ca  = ECIScorer.compute_ca(answers_correct, true_classes)
rp  = ECIScorer.compute_rp(predicted_classes, true_classes)
ece = ECIScorer.compute_ece(confidences, correctness)
hss = ECIScorer.compute_hss(predicted_classes, true_classes, answers_given)

eci = scorer.compute_eci(sda, ca, rp, ece, hss)

# Brier decomposition
brier = BrierDecomposition.compute(confidences, correctness)
# → {'brier': 0.18, 'reliability': 0.03, 'resolution': 0.09, 'uncertainty': 0.24}

# D-Prime (metacognitive discrimination)
dprime = Type2DPrime.compute(confidences, correctness, threshold=0.7)
# → {'d_prime': 1.24, 'hit_rate': 0.85, 'false_alarm_rate': 0.42}

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Supported Providers

Provider	API Key Required	Models Available	Best For
kaggle	No	26 (Kaggle model pool)	Running inside Kaggle notebooks
openrouter	Yes	100+	Broadest model access with one key
anthropic	Yes	Claude family	Direct Anthropic API access
openai	Yes	GPT family	Direct OpenAI API access

Default behavior: If no API key is provided, the SDK falls back to the Kaggle provider (which requires no authentication when running inside a Kaggle notebook).

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Configuration Reference

RunConfig(
    # ── Mode ──
    mode="compare",           # "compare" (multi-model) or "deep_probe" (single-model)
    models=[...],             # List of model identifiers

    # ── Provider ──
    provider="kaggle",        # "kaggle", "openrouter", "anthropic", "openai"
    api_key=None,             # Required for non-Kaggle providers
    api_base_url=None,        # Custom API endpoint (for self-hosted models)

    # ── Dataset ──
    n_items=200,              # Base problem count (200 standard, 1000 for deep probe)
    dataset_path=None,        # Path to custom dataset JSON (or None for bundled)
    stress_decision_ratio=0.25,  # Fraction with decision-pressure variants
    stress_clarity_ratio=0.10,   # Fraction with reduced-clarity variants

    # ── Statistical ──
    seeds=["s1", "s2"],       # Reproducibility seeds for bootstrap
    bootstrap_iterations=2000, # Bootstrap iterations for CIs

    # ── Time Budget ──
    timeout_per_model=10800,  # Max seconds per model (default: 3h)
    total_time_budget=43200,  # Total budget (default: 12h)
    time_reserve=3600,        # Reserved for analysis (default: 1h)

    # ── Checkpointing ──
    checkpoint_dir="prometheus_checkpoints",
    resume_from_checkpoint=True,

    # ── Output ──
    output_dir="prometheus_output",

    # ── Feature Flags ──
    run_probes=True,          # Epoch-2 adversarial probes
    run_multistage=True,      # Multi-stage belief revision protocol
    run_statistics=True,      # Bootstrap CIs and significance tests
    verbose=True,             # Print progress
)

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V5 Benchmark Results

Results from the PROMETHEUS-EBM v5.0 EXTENDED run (5 models × 324 items × 3 seeds):

Epoch-1 Leaderboard

Rank	Model	ECI	95% CI	SDA
🥇	Claude Sonnet 4.6	0.884	[0.878, 0.888]	85.4%
🥈	Claude Opus 4.6	0.869	[0.864, 0.877]	84.3%
🥉	DeepSeek V3.2	0.815	[0.800, 0.829]	76.5%
4	DeepSeek R1-0528	0.785	[0.774, 0.792]	68.6%
5	Gemini 3.1 Pro	0.767	[0.745, 0.787]	73.1%

Key Findings

1. Sonnet beats Opus on ECI (0.884 vs 0.869, statistically significant). The mid-tier model has better epistemic calibration than the top-tier model. Metacognition is not monotonic with scale.

2. Opus leads on adversarial resilience. Under the multi-stage protocol, Opus improved its accuracy by +13.9% after being challenged with counter-arguments. It correctly revised wrong answers without abandoning right ones.

3. DeepSeek R1 classifies problems differently. R1's solvability detection (SDA = 68.6%) diverges from all other models, and its evaluation perspective as a judge disagreed with peers at 16–20%. Chain-of-thought reasoning does not inherently improve metacognition.

4. Gemini 3.1 Pro is the most overconfident. Its stated confidence exceeds actual accuracy by 33 percentage points — the largest gap in the benchmark.

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Project Structure

prometheus-ebm-sdk/
├── prometheus_ebm/
│   ├── __init__.py          # Public API exports
│   ├── config.py            # RunConfig dataclass
│   ├── taxonomy.py          # 4-class solvability taxonomy
│   ├── scorer.py            # ECI, HGI, Brier, D-Prime
│   ├── runner.py            # Benchmark orchestrator
│   ├── data/                # Bundled dataset (200 problems)
│   └── providers/
│       ├── kaggle.py        # Kaggle kbench adapter
│       ├── openrouter.py    # OpenRouter API adapter
│       ├── anthropic.py     # Anthropic Claude adapter
│       └── openai.py        # OpenAI adapter
├── tests/
│   └── test_scorer.py       # Unit tests for scoring engine
├── examples/
│   ├── compare_5_models.py  # Multi-model comparison example
│   └── deep_probe_opus.py   # Single-model deep probe example
├── pyproject.toml           # Package configuration
└── LICENSE

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Roadmap

Version	Status	Features
v0.1.0	✅ Current	Scorer (ECI, Brier, D-Prime), Taxonomy, Config, Provider adapters
v0.2.0	Planned	Full evaluation loop, stress augmentation engine, export pipeline
v0.3.0	Planned	Bootstrap CI, pairwise significance, contamination audit
v1.0.0	Planned	1,000-item dataset, CLI tool, HTML report generator

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License

MIT — See LICENSE for details.

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Citation

@misc{alam2026prometheus,
  title   = {PROMETHEUS-EBM: Benchmarking Epistemic Metacognition in Frontier AI Models},
  author  = {Mushfiqul Alam},
  year    = {2026},
  url     = {https://github.com/Mushfiqul-Alam-17/prometheus-ebm-sdk}
}