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This paper proposes a carbon-aware re-ranking strategy for e-commerce recommendations, using a retrieval-augmented pipeline to estimate product carbon footprints and trading off predicted engagement against sustainability. Evaluated on Amazon Reviews data, substantial carbon reductions are achievable with minimal engagement loss.
The paper investigates whether the performance gains from rewriting retrieved passages in RAG QA pipelines are causally driven by the presence of the gold answer string in the rewritten context, using controlled intervention audits across multiple models and datasets.
GBrain now recommends ZeroEntropy as the default embedding and re-ranking option, replacing OpenAI and Voyage AI.