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COMPANY · ENTITY #402

Large Language Models (LLMs)

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RESEARCH · 1 SOURCE · arXiv cs.AI

Carbon-Aware Routing for Function Calling in Edge-Cloud LLM Systems

An arXiv paper proposes a carbon-aware routing framework that distributes function-calling queries across a three-tier edge-cloud architecture using edge and cloud LLMs. A lightweight k-NN predictor in a unified semantic-lexical embedding space estimates per-query accuracy, delay, and power, and combines these with real-time grid carbon intensity to route queries to the lowest-emission tier; evaluations report matching cloud-level accuracy while cutting operational carbon emissions by about 4× on average.

7.0

RESEARCH · 1 SOURCE · Apple Machine Learning Research

LLMs Are Not (Consistently) Bayesian — paper quantifies deviations from Bayes updates

The paper treats LLMs as information-processing rules and introduces the 'information processing gap' metric to quantify deviations from Bayesian updates when LLMs update probabilistic beliefs in light of new evidence. The authors run extensive experiments to evaluate internal (in)consistencies of LLMs' belief-updating behavior, with relevance to high-stakes domains like medicine, science and law.

7.0