esmaeil-abedi-dev/verifier-ce-qwen2.5-0.5b
The esmaeil-abedi-dev/verifier-ce-qwen2.5-0.5b is a 0.5 billion parameter Qwen2.5 model fine-tuned for authorization verification in AI-agent actions, utilizing a 32768 token context length. It determines if an action is AUTHORIZED/UNAUTHORIZED based on a natural-language description of authority and delegation chains. This model is uniquely trained using a deterministic authorization verifier as the sole cross-entropy training signal, making it specialized for formal authorization logic tasks. It achieves high accuracy on synthetic benchmarks, approximating a ground-truth verifier for research into learnable authorization.
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Model Overview
The esmaeil-abedi-dev/verifier-ce-qwen2.5-0.5b is a 0.5 billion parameter Qwen2.5 model specifically fine-tuned for authorization verification in AI-agent contexts. Its core function is to determine if a proposed AI-agent action is AUTHORIZED or UNAUTHORIZED by evaluating a natural-language description of root authority, delegation chains, and the pending action.
Key Capabilities
- Deterministic Authorization Verification: Trained exclusively using a deterministic authorization verifier's verdict as its cross-entropy training signal, rather than human-assigned labels.
- Delegation Chain Reasoning: Capable of processing complex delegation chains, including narrowing authority, issue/expiry times, and revocations.
- High In-Distribution Accuracy: Achieves up to 0.988 accuracy on a committed held-out test set, with the released weights (seed 9) showing 0.975 accuracy. This performance is comparable to or exceeds larger models like Claude-Sonnet-4.5 (0.975) and Deepseek-R1 (0.95) on this specific task.
- Synthetic Data Training: Trained on synthetic execution traces across 5 domains and 9 scenario classes, generated and labeled by the verifier.
Intended Use and Limitations
This model is primarily a research artifact for exploring learnable authorization, not a production system. Its generalization to real-world agent logs or authorization models outside the attenuated-delegation formalism is not yet established, as it was trained and evaluated on a synthetic benchmark. Users should consult the associated GitHub repository for detailed experiment logs and benchmark limitations.