Three New Studies Explore LLM Applications in Edge Computing, Battery Systems, and Human Values

Three New Studies Explore LLM Applications in Edge Computing, Battery Systems, and Human Values

Three new preprints on arXiv explore diverse applications of large language models across different domains.

TOGGLE: Edge Device Compression

According to arXiv paper 2512.16855v1, researchers have developed TOGGLE, a “Temporal Logic-Guided Large Language Model Compression” method designed for edge devices. The paper notes that while LLMs “deliver exceptional performance across natural language tasks,” they “demand substantial computational resources, limiting their deployment on resource-constrained edge devices.”

TimeSeries2Report: Battery Management

A second paper (arXiv:2512.16453v1) introduces TimeSeries2Report, a prompting approach for using LLMs to manage lithium-ion batteries. The authors state that “large language models (LLMs) offer promising capabilities for interpreting multivariate time-series data,” though they note “their application to real-world battery energy storage system (BESS) operation and maintenance remains largely unexplored.”

Value Lens: Human Value Alignment

The third study (arXiv:2512.15722v1) presents Value Lens, a framework for using LLMs to understand human values in autonomous decision-making. According to the abstract, the research addresses how “autonomous decision-making process, which is increasingly applied to computer systems, requires that the choices made by these systems align with human values.”

All three papers represent ongoing research and have not yet undergone peer review.