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This project presents a lightweight and interpretable routing system that selects among multiple reasoning strategies—Zero-Shot, Chain-of-Thought (CoT), Program-Aided Language (PAL), ReAct, and Few-Shot—based on features of the user query. It supports the vision of expert orchestration by treating large language model (LLM) prompting strategies as modular, agent-like components.

We frame the problem as a reinforcement learning task using a custom Gym environment, training a PPO agent on a small synthetic dataset with handcrafted, human-interpretable features. The router achieves ~23% accuracy (above random baseline of 20%), showing early signal that reasoning strategy can be learned and predicted from query structure.

Our prototype leverages the CrewAI framework, allowing seamless generalization to multi-agent setups and agent routing, making it production-aligned. The system supports full agent traceability for debugging and interpretability.

Though built on just 39 examples, this demo shows potential for scaling up with semantic features, local LLMs, and more realistic workloads. Future work includes routing across nested agents, evaluation on local models, and applying mechanistic interpretability to policy analysis.

Cite this work:

@misc {

title={

},

author={

Henry Luan

},

date={

6/2/25

},

organization={Apart Research},

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

howpublished={https://apartresearch.com}

}

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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.