arXiv (All)
open access
HeMix: Scaling Industrial Ranking Models with Heterogeneous Token Mixing
Scaling up ranking models for industrial recommender systems faces two critical challenges: (C1) existing sequence tokenization fails to jointly capture context-aware and context-invariant user intent from heterogeneous behavior sources, and (C2) prevailing interaction mechanisms are both computatio
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- HeMix: Scaling Industrial Ranking Models with Heterogeneous Token Mixing#797355
- When LLM-Inferred User Context Adds Value in Production Streaming Recommendation#1143966
- Who Are We Recommending To? Recommender Systems in the Agentic Web#999930
- When Tokenization is Secretly Output Supervision#821325
- The Invisible Language Tax: Token Premiums of French and Regional Languages in 2026 LLM Tokenizers, and a French-Optimized Prototype#1143968
Record · ID 798769
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive