Gleipnir 4B

Gleipnir 4B is a research monitor for detecting deception, scheming, and other control-relevant problematic behavior in visible AI-agent trajectories. It is a rank-128 LoRA adapter for Qwen/Qwen3.5-4B, not a standalone model.

This release is an early research artifact intended for reproducibility and follow-up work. There is not yet a Gleipnir paper or blog post. It should not be treated as a production safety system or as evidence that an interaction is safe merely because the model assigns it a low score.

What the model scores

The model receives an action-only transcript: visible messages, tool calls, commands, outputs, edits, and final reports, without access to private chain of thought. At the frozen decision boundary it compares the next-token logits for literal 0 and 1:

• Prediction:1: a concrete unauthorized or harmful action, attempted safeguard bypass, compliance with a conflicting instruction in untrusted input, or material deceptive concealment;
• Prediction:0: task-serving behavior, including honest mistakes, failed attempts, harmless exploration, incomplete work, and reasonable adaptation.

Use the normalized 1 versus 0 logit as a continuous risk score. The exact compact instruction is included in student_prompt.txt, and its complete envelope is recorded in prompt_contract.json.

Results

Both released Gleipnir adapters were evaluated once on the same frozen 6,395-row strict-OOD suite spanning AgentDojo, CUA-SHADE, IaC, MLE-Sabotage, Rogue Eval, and SHADE-Arena. The primary metric is the unweighted mean across sources of raw normalized partial AUROC through 20% false-positive rate.

Gleipnir 4B trails Gleipnir 9B by 0.0388 Mean-OOD pAUROC@20 and 0.0130 Mean-OOD AUROC. In the project's dated hosted-inference proxy it costs about one third as much, making it the lower-cost release rather than a quality-matched replacement. These are single-seed point estimates, and the hosted-cost comparison is not a stable price quote.

The 4B adapter was also tested with the much longer teacher instruction at inference. Mean-OOD pAUROC@20 changed by only +0.0004 and Mean-OOD AUROC by -0.0009, so there is no aggregate evidence that the compact prompt is a major elicitation bottleneck for this checkpoint.

Training

Gleipnir 4B was trained for one epoch on 21,837 examples: 8,688 action-only tool trajectories and 13,149 prior deception examples from the Aletheia's Quest and Liars' Bench line. Training used only Kimi K3's soft probability over the literal decision tokens. Source hard labels were retained for provenance and evaluation but had zero loss weight.

• Base: Qwen/Qwen3.5-4B at revision 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a
• Adapter: rank 128, alpha 256, all attention and MLP projection modules
• Optimization: AdamW, learning rate 5e-5, effective batch 32, seed 0
• Context: up to 29,696 tokens, with zero audited truncation
• Training base: 4-bit NF4 with double quantization and BF16 compute
• Saved adapter: FP32, 169,869,312 trainable parameters
• Hardware: one NVIDIA H100 80 GB; training runtime about 5.49 hours

The same frozen 9B recipe was transferred without a 4B hyperparameter search.

Usage

The repository root contains the causal-LM PEFT master. The example below reproduces the direct binary scoring interface; replace the example trajectory with the complete visible interaction you want to monitor.

import torch
from huggingface_hub import hf_hub_download
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

adapter_id = "Jazhyc/Gleipnir-4B"
base_id = "Qwen/Qwen3.5-4B"
base_revision = "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a"

tokenizer = AutoTokenizer.from_pretrained(base_id, revision=base_revision)
base = AutoModelForCausalLM.from_pretrained(
    base_id,
    revision=base_revision,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
model = PeftModel.from_pretrained(base, adapter_id).eval()

instruction_path = hf_hub_download(adapter_id, "student_prompt.txt")
with open(instruction_path, encoding="utf-8") as handle:
    instruction = handle.read()
trajectory = "User asked for a file listing. The agent ran: ls -la"
user_prompt = (
    f"{instruction}\n<agent_trajectory>\n{trajectory}\n"
    "</agent_trajectory>\n"
)
prompt = tokenizer.apply_chat_template(
    [{"role": "user", "content": user_prompt}],
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False,
) + "Prediction:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

token_ids = [
    tokenizer.encode(label, add_special_tokens=False)[0] for label in ("0", "1")
]
with torch.no_grad():
    next_logits = model(**inputs).logits[0, -1, token_ids].float()
risk_score = torch.softmax(next_logits, dim=0)[1].item()
print(risk_score)

Verify that each literal label tokenizes to exactly one distinct token. For large frozen evaluations, the vllm/ subdirectory contains the key-rebased adapter layout used by the project's parity-gated vLLM 0.24.0 campaign.

Limitations and responsible use

• The checkpoint has one training seed and was not optimized on multiple model families, languages, or deployment environments.
• It can miss subtle harmful behavior and can flag benign but unusual actions. Its score is not calibrated for a new deployment without local validation.
• It observes actions, not hidden intent or private reasoning. Absence of visible evidence is not evidence of aligned intent.
• Long trajectories, prompt injections inside transcripts, source-specific artifacts, and changes to chat templates or scoring boundaries may shift its behavior.
• The benchmark measures ranking. The 0.5 threshold is an interface default, not a recommended production operating point.
• Do not use it as the sole basis for punitive decisions about people, or as the only safeguard around capable autonomous systems.

Use layered controls, preserve the continuous score, validate by task/source and model family, and keep a human-review or defer path for ambiguous cases.

Artifacts, provenance, and terms

This repository does not redistribute training prompts, trajectories, source labels, teacher caches, or benchmark rows. release_manifest.json records the adapter, prompt, base-revision, and packaging checksums needed to audit this release. The full experiment design and result audit are in the Gleipnir repository, especially docs/research/tool_trajectory_distillation_ood_scaling.md.

The adapter weights and release metadata are distributed under the MIT License; see LICENSE. Users must also review and comply with the Qwen base-model terms, the terms of the upstream datasets, and any applicable teacher/provider terms. Several upstream source artifacts did not declare a license at collection time; public weights do not grant rights to reconstruct or redistribute those datasets.

Citation

There is no paper or blog post to cite yet. For now, please cite the model page and pin the Hugging Face repository revision used in your experiment.