Researcher trajectory points to Google overhauling Search with generative ranking
dejanseo · x · 2026-10-05
dejan.ai traces the publication trajectory of Nilesh Gupta, Chong You, Srinadh Bhojanapalli and Felix Xinnan Yu, arguing their work aims to rebuild Google Search, Shopping, and Gemini RAG infrastructure with scalable, low-latency generative ranking. Key claim: dual encoders hit a mathematical capacity wall (embedding dims must grow with corpus size), while a constant-dimension autoregressive ranker can express arbitrary rankings — paving the way to collapse Google's two-stage retrieval+rerank pipeline into a single generative model serving AI Overviews and Deep Research.
Related event: BlockRank Speeds Up LLM Document Ranking with Block-Sparse Attention(2 posts)→
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