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AI shopping assistants increasingly influence which products consumers consider in retail markets. Retail assortment planning under AI mediation involves a retailer choosing a catalog and an AI intermediary forming a shortlist.
Source: arXiv
https://arxiv.org/abs/2610.12917
#MachineLearning
A study presents COFFEQA, a counterfactual-guided few-shot distillation method for LLM question answering. Experiments across benchmarks show it outperforms factual-only baselines.
Source: arXiv cs.CL
https://arxiv.org/abs/2610.12837
#MachineLearning
RAMP performs early recurrent refinement using low-precision weight-and-activation computation and restores high precision for terminal refinement, achieving up to 1.16x end-to-end first-prefill speedup on an NVIDIA H100.
Source: arXiv cs.CL
https://arxiv.org/abs/2610.13041
#MachineLearning #Nvidia
Researchers introduced ReTRAIL, a framework enabling large language model agents to rewind the environment to earlier states while preserving valid progress and carrying forward knowledge learned from abandoned exploration.
Source: arXiv
https://arxiv.org/abs/2610.13058
#MachineLearning
Researchers tested whether unsupervised representations trained on patent text can recover relevant connections that code-restricted search cannot. A hyperbolic Variational Autoencoder retrieves genuinely cross-category prior art 52% more often than a Euclidean model.
Source: arXiv
https://arxiv.org/abs/2610.12997
#MachineLearning
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