15 : 12 : 34 : 31

15 : 12 : 34 : 31

15 : 12 : 34 : 31

15 : 12 : 34 : 31

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Jun 2, 2025

Routing LLMs using Distilled Predictors and Confidence Thresholding

Gideon Daniel Giftson T

Details

Details

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This project explores confidence-based routing using sparsified transformer models as an intelligent alternative to monolithic AI systems. Focusing on Track 2: Intelligent Router Systems, we implemented a confidence-threshold router for a pruned DistilBERT model deployed via DeepSparse on the SST-2 sentiment classification task. We investigated how routing confidence correlates with prediction accuracy and how routing fewer, more confident samples can enable fallback to larger models while retaining accuracy.

Our system enables cost-efficient, interpretable decision-making with routing justified by softmax confidence thresholds. We show that routing 70% of samples at a confidence threshold of 0.8 retains 97% of original accuracy while reducing inference costs by over 50%. These results advance the Expert Orchestration Architecture by demonstrating real-world savings and interpretable routing without compromising safety or performance.

Cite this work:

@misc {

title={

},

author={

Gideon Daniel Giftson T

},

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.