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      "statement": "# Monolith: collisionless embeddings and incremental online training\n\nIntegrated a dynamic key-preserving embedding table with admission/expiry filtering, streaming feature–label joins and incremental synchronization between training and serving parameter servers.\n\n## Scientific scope\n\nUses cuckoo hashing, TensorFlow, Kafka and Flink rather than inventing those components. Collisionless lookup does not retain every ID: filtering and expiry discard some features. Sparse/dense synchronization can be inconsistent and delayed labels require storage; empirical reliability/freshness trade-offs are not a universal fault-tolerance or unbiasedness proof.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nZhuoran Liu; Leqi Zou; Xuan Zou; Caihua Wang; Biao Zhang; Da Tang; Bolin Zhu; Yijie Zhu; Peng Wu; Ke Wang; Youlong Cheng\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Monolith: Real Time Recommendation System With Collisionless Embedding Table](https://arxiv.org/abs/2209.07663v1)\n",
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