This paper introduces the Token Efficiency Index (TEI), a peer-benchmarked composite indicator that quantifies AI token spend efficiency into a single 0-100 score. It uses established methods like Benefit of the Doubt Data Envelopment Analysis with a robust order-m extension to provide an adaptive, outlier-resistant framework for optimizing AI costs.

This paper introduces a practical framework for benchmarking and optimizing AI models on ARM Cortex processors in embedded systems. It focuses on balancing energy efficiency, accuracy, and resource utilization, demonstrating how optimal processor and model selections depend on an application's inference cycle time.

This paper systematically investigates fairness in medical imaging AI benchmarking competitions, revealing significant biases in dataset composition and critical flaws in data accessibility, licensing, and documentation. The findings highlight a disconnect between leaderboard success and clinically meaningful AI, urging for improved transparency and reusability standards.

This paper introduces a benchmark for evaluating long context reasoning capabilities of WebAgents through sequentially dependent subtasks that require retrieval and application of information from extended interaction histories. It observes a dramatic performance degradation as context length increases and proposes an implicit RAG approach for modest improvements.