arXiv 2610.30128 · cs.IR · 2026-10-21
Think Harder Where It Matters: Item-Level Adaptive Recursion for Sequential Recommendation
Zhanshuo Cao, Jiakai Tang, Yuqi Zhou, Sunhao Dai, Jun Xu
Affiliation unverified
In latest · #337 Added on 2026-10-25
One-line takeaway
LoopRec makes sequential recommenders allocate depth per item, after showing most history items converge shallowly while a few need full depth, so fixed-depth computation both wastes compute and hurts accuracy.
LoopRec 让序列推荐模型按物品分配计算深度:发现多数历史物品浅层就收敛、少数要算到底,固定深度既浪费又拖累效果,于是改为逐物品自适应递归。
Read on arXiv PDF Hugging Face discussion Code
Topics & disciplines
More on Efficient training & inference
- The Teacher Is a Direction, Not a Destination: Extrapolating RL-Induced Representation Residuals in On-Policy Distillation ▲511
- Scaling Properties of Same-Family On-Policy Distillation ▲327
- Beyond Domain-Routed Supervision: Cross-Teacher Guarding for Multi-Teacher On-Policy Distillation ▲250
- On-Policy or Off-Policy Learning? A Systematic Study of Distillation Dynamics ▲205
- Beyond Teacher Assignment: Domain-Normalized Multi-Teacher On-Policy Distillation ▲181
More on Reasoning
- LoopVL: Recurrent Visual Intelligence 2026-09-29 · ▲417
- LEV: Thinking Fast and Deep with Looped Decision Models 2026-10-14 · ▲337
- Thinking Inertia: LLMs Keep Thinking When Told Not To 2026-10-08 · ▲241
- Rethinking Latent Visual Reasoning: Grounding Latent Reasoning in Visual Evidence 2026-09-28 · ▲224
- RoTA-Bench: Evaluating 3D Spatial Reasoning through Interactive Projection Alignment 2026-10-13 · ▲203
About this data
Metadata from the arXiv API (verified); upvotes from Hugging Face Daily Papers; citations from Semantic Scholar. Last refreshed 2026-10-26.
The takeaway is written by AIMap from the abstract; the abstract itself is not reproduced — read the original.