RUC's LoopRec Brings Item-Level Adaptive Recursion with Early-Exit Gates to Sequential Recommendation
Zhanshuo Cao · hf · 2026-10-22
Researchers at Renmin University of China's Gaoling School of AI propose LoopRec, challenging the fixed compute budget in sequential recommenders.
- Diagnostic finding: non-uniform computational need — items in the same sequence vary drastically, with most converging after shallow processing while a critical minority stabilizes only at full depth
- Method: recursive shared blocks with early-exit gates enable adaptive per-item computation depth
- Improves both efficiency and accuracy
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