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Jun 2, 2025

Guardian-Loop: Mechanistically Interpretable Micro-Judges with Adversarial Self-Improvement

Efstathios Siatras, Man Kit Chan

Details

Details

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Guardian-Loop is a mechanistically interpretable judge system designed to enhance the Expert Orchestration Architecture through transparent and efficient safety evaluation. Targeting Track 1 (Judge Model Development), we train lightweight classifiers that pre-filter prompts for safety using a Llama 3.1 8B model, fine-tuning only the upper layers to directly output True or False responses. This avoids probe-head architectures, enabling native token-level interpretability and calibrated scoring. Achieving 85.0% accuracy and 94.6% AUC-ROC on a hold-out test set with low latency using the safety judge, the system is deployable on consumer hardware. Guardian-Loop integrates deep interpretability techniques, including token attribution, attention analysis, and circuit tracing, to expose the model’s internal decision-making; We also demonstrate the extensibility of our framework by applying it to adjacent judgment tasks, such as feasibility prediction. An open-ended adversarial framework based on MAP-Elites quality diversity optimization was proposed, designed to populate a 10×10 grid spanning risk types and evasion strategies. While not yet deployed, this framework could support continuous self-improvement and vulnerability discovery. Guardian-Loop illustrates how small-sized LLMs can be repurposed as efficient, transparent filters, supporting scalable and trustworthy AI deployments.

Cite this work:

@misc {

title={

},

author={

Efstathios Siatras, Man Kit Chan

},

date={

6/2/25

},

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

Reviewer's Comments

Reviewer's Comments

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Apr 14, 2025

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Jan 24, 2025

Safe ai

The rapid adoption of AI in critical industries like healthcare and legal services has highlighted the urgent need for robust risk mitigation mechanisms. While domain-specific AI agents offer efficiency, they often lack transparency and accountability, raising concerns about safety, reliability, and compliance. The stakes are high, as AI failures in these sectors can lead to catastrophic outcomes, including loss of life, legal repercussions, and significant financial and reputational damage. Current solutions, such as regulatory frameworks and quality assurance protocols, provide only partial protection against the multifaceted risks associated with AI deployment. This situation underscores the necessity for an innovative approach that combines comprehensive risk assessment with financial safeguards to ensure the responsible and secure implementation of AI technologies across high-stakes industries.

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Jan 24, 2025

CoTEP: A Multi-Modal Chain of Thought Evaluation Platform for the Next Generation of SOTA AI Models

As advanced state-of-the-art models like OpenAI's o-1 series, the upcoming o-3 family, Gemini 2.0 Flash Thinking and DeepSeek display increasingly sophisticated chain-of-thought (CoT) capabilities, our safety evaluations have not yet caught up. We propose building a platform that allows us to gather systematic evaluations of AI reasoning processes to create comprehensive safety benchmarks. Our Chain of Thought Evaluation Platform (CoTEP) will help establish standards for assessing AI reasoning and ensure development of more robust, trustworthy AI systems through industry and government collaboration.

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This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.