constitutional-bioguard-v4

Constitutional BioGuard v4

A 184M-parameter domain-specialist classifier for biological dual-use content. It fine-tunes microsoft/deberta-v3-base on a 56-rule biosafety constitution plus corrective augmentation designed to diagnose and reduce shortcut learning.

Recommended checkpoint: v4 response-diverse.

Not released: v5 PairCFR. v5 fixed one artificial hybrid-response Goodhart case but lost too much specialist bio recall, so it is documented as a negative result rather than shipped as the release model.

Intended Scope

BioGuard v4 is a bio-specialist classifier for query/response pairs. It is not a general-purpose safety model. For cyber, harassment, self-harm, misinformation, or other non-bio categories, use a general safety classifier and route bio-like content to BioGuard as a second stage.

general safety model -> if bio-like / needs biosafety review -> BioGuard v4

Audited Results

The public headline uses clean held-out gates after the Goodhart/leakage audit.

What Changed From Earlier Reporting

The earlier OR-Bench-Health result (v3 98.5% FPR -> v4 1.22% FPR) is now treated as training-distribution evidence because the v4 audit found 100% overlap with the v4 augmentation block. The clean replacement headline is OR-Bench-Hard-1K FPR = 2.1%.

HarmBench/AdvBench bio "held-out" recall from earlier v3-era reporting is also restated as training-distribution recall after overlap analysis. The strongest clean transfer evidence is XSTest, WildGuard native labels, BioThreat-Eval, SaladBench/ALERT CBRN selectivity, and the BioThreat-based CRT probes.

v5 Non-Release Decision

v5 PairCFR solved the artificial refusal+compliance hybrid FPR but failed the specialist bio recall gate. v4 remains the recommended release checkpoint.

Usage

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_id = "jang1563/constitutional-bioguard-v4"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id).eval()


def classify(query: str, response: str = "") -> dict:
    inputs = tokenizer(
        query,
        response,
        return_tensors="pt",
        truncation=True,
        max_length=512,
    )
    with torch.no_grad():
        logits = model(**inputs).logits
    prob_unsafe = float(torch.softmax(logits, dim=-1)[0, 1])
    return {
        "label": "UNSAFE" if prob_unsafe >= 0.5 else "SAFE",
        "prob_unsafe": prob_unsafe,
    }


print(classify(
    query="What is the role of polymerase in DNA replication?",
    response="DNA polymerase synthesizes new DNA strands by adding nucleotides.",
))

Training Summary

• Base model: microsoft/deberta-v3-base
• Architecture: binary text-pair classifier, (query, response) -> SAFE/UNSAFE
• Constitution: 56 rules across 7 NSABB categories
• v4 augmentation: response-diverse blocks that decouple compliance/refusal style from the unsafe label
• Class weighting: UNSAFE = 1.5
• Max sequence length: 512
• Seed: 42
• Hardware: single A100 GPU on Weill Cornell Cayuga HPC

Limitations

• Research prototype, not a production safeguard.
• English-centric; multilingual behavior is not broadly validated.
• Synthetic and curated data dominate training; real-world distribution shift remains under-characterized.
• v4 over-flags an artificial refusal+compliance hybrid response pattern.
• It is intentionally bio-specialist and can miss non-bio harmful content.
• Use with upstream normalization and broader safety filters; do not rely on this model alone for high-stakes decisions.

Responsible Use

This model is intended to detect biological dual-use risk signals and support safety research. Do not use it to develop or optimize evasion strategies against biosafety systems. Do not paste operational harmful biology content into public issues or examples.

For the full safety scope, see SAFETY.md in the repository: https://github.com/jang1563/constitutional-bioguard

Citation

@software{kim2026bioguard,
  author = {Kim, JangKeun},
  title = {Constitutional BioGuard: A Biosafety Content Classifier},
  year = {2026},
  url = {https://github.com/jang1563/constitutional-bioguard},
  version = {v0.2.0},
}

License

MIT.