The paper introduces Battery-Sim-Agent, an LLM-based framework that reframes the difficult inverse problem of battery parameter estimation as a reasoning task, significantly outperforming traditional…

This paper introduces a method for adapting pretrained battery prediction models in Electric Vehicles using on-device learning, achieving significant improvements in forecasting performance.

This paper studies the autarky problem of scheduling energy-consuming jobs with time windows using a battery and an energy forecast, and shows NP-hardness, polynomial-time solvability, and fixed-param…

The paper introduces 'Matter to Mechanism,' a novel benchmark designed to rigorously evaluate AI co-scientists' ability to generate plausible, mechanism-grounded solution hypotheses for complex materi…

This paper presents MemoGuard, a lightweight adaptive runtime that validates episodic memories against topology, resource, and outcome contracts before reuse in communication-limited robots, reducing…

This paper presents LightFARM, a predictive lighting control framework for energy-efficient indoor farming that combines finite-horizon predictive control with compact models of photosynthesis, therma…

The paper introduces VEHBench, an engineering-native diagnostic benchmark for LLM-assisted VEH design, featuring 763 literature-grounded tasks.

This paper introduces BigPower, a hierarchical source-level surrogate model for fine-grained module-level power estimation during CPU design using large language models and architectural hierarchy.

The paper establishes bijections between tree records, girth of connected endofunctions, and genesis sequences, deriving generating functions for tree and forest record numbers using Cayley's tree fun…

The paper introduces a quantitative, cell-level circuit framework to model DRAM vulnerability by linking physical charge leakage and disturbance pathways to system-level security properties like volat…

This paper develops FGDSE, a feature-governed dynamic stacking ensemble for climate-resilient charging-asset management in electric vehicles, which predicts daily fault risk over a multi-week horizon…

The paper proposes a Cycle-Space Detector (CSD) that uses network topology constraints to effectively detect stealthy, data-driven False Data Injection Attacks (FDIA) that exploit the null space of me…

The paper introduces Pocket-Dentist, an efficiency-aware benchmark and model that demonstrates that compact, smaller Vision-Language Models (VLMs) can outperform larger models in accuracy while drasti…

This paper proposes a thermodynamic computing stack for machine learning using stochastic analog processes and energy-based models.

A lightweight sensor-driven Lévy walk controller is presented for efficient autonomous exploration of minimal-sensing, resource-constrained nano-UAVs.

This paper presents a method for compressing matrices using a RePair straight-line program (SLP), allowing matrix-vector products with time and space proportional to the compressed size, and demonstra…