Sumit on Mythryl: “TransX Technical Report: Scaling Sequence Transduction for…”

TransX Technical Report: Scaling Sequence Transduction for Recommendation LinkedIn presents a recommender that separates history encoding, behavior memory, and candidate scoring so most work runs offline, letting models scale without raising serving cost. 📝 arxiv.org/abs/2610.20913

arxiv.orgTransX Technical Report: Scaling Sequence Transduction for RecommendationWe present TransX, a sequence-transduction framework and co-designed infrastructure for scaling industrial recommendation with explicit control over model-compute allocation and execution amortization...

16/10/26, 8:01 am0 reposts0 likes

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