A perspective-conditioned RAG architecture is proposed for AI-assisted LCA interpretation, using scenario anchors, micro-queries, and neutral synthesis, demonstrated on a hydrogen diesel reduction case in Italian apple production.
In: High Performance Extreme Computing Conference (HPEC)
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Empirical study of agentic LLM generation of parallel Julia code finds reliable execution only at small scales with recurring failures in task dependencies and scheduling at larger scales.
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LCAi: Life Cycle Assessment with big data fusion and retrieval-augmented generation-assisted interpretation
A perspective-conditioned RAG architecture is proposed for AI-assisted LCA interpretation, using scenario anchors, micro-queries, and neutral synthesis, demonstrated on a hydrogen diesel reduction case in Italian apple production.
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Generated, Parallel, Scalable? A Study of Agentic AI-Generated Julia Code on Supercomputers
Empirical study of agentic LLM generation of parallel Julia code finds reliable execution only at small scales with recurring failures in task dependencies and scheduling at larger scales.