PFAgent automates interactive power-flow analysis by combining intent parsing, tool execution, verification-driven self-evolution, and an evaluation framework, with demonstrations on IEEE benchmark systems.
Carbon footprint accounting driven by large language models and retrieval-augmented generation
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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GaiaFlow combines semantic-guided diffusion tuning with early-exit and quantization methods to lower carbon emissions in neural information retrieval while maintaining competitive effectiveness.
citing papers explorer
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PFAgent: A Tractable and Self-Evolving Power-Flow Agent for Interactive Grid Analysis
PFAgent automates interactive power-flow analysis by combining intent parsing, tool execution, verification-driven self-evolution, and an evaluation framework, with demonstrations on IEEE benchmark systems.
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GaiaFlow: Semantic-Guided Diffusion Tuning for Carbon-Frugal Search
GaiaFlow combines semantic-guided diffusion tuning with early-exit and quantization methods to lower carbon emissions in neural information retrieval while maintaining competitive effectiveness.