Proposes a rebound-informed framework with five tests (metric, boundary, reinvestment, burden shifting, governance) showing that AI datacenter sustainability claims often rely on relative efficiency gains without proving absolute reductions in energy, water, and other burdens.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
DLR augments low-rank factorization with a fixed structured residual during training that is absorbed post-training, improving C4 perplexity for LLaMA models from 60M to 7B while preserving exact low-rank inference cost.
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Plateau That Never Comes: When Efficiency Claims in Datacenters and AI Become Greenwashing
Proposes a rebound-informed framework with five tests (metric, boundary, reinvestment, burden shifting, governance) showing that AI datacenter sustainability claims often rely on relative efficiency gains without proving absolute reductions in energy, water, and other burdens.