arXiv 2610.20951 · cs.AI · 2026-10-14
LEV: Thinking Fast and Deep with Looped Decision Models
Dingjie Song, Zhiling Yan, Yuxuan Zhang, Lichao Sun
Affiliation unverified
In latest · #30 2026-W43 #2 (in progress) Added on 2026-10-21
One-line takeaway
LEV lets decision models think deeper by looping their layers instead of generating reasoning tokens.
LEV 让决策模型通过循环重跑网络层来“想得更深”，不必生成推理文字。
Read on arXiv PDF Hugging Face discussion Code Project page
Topics & disciplines
More on Reasoning
- Thinking Inertia: LLMs Keep Thinking When Told Not To ▲241
- Rethinking Latent Visual Reasoning: Grounding Latent Reasoning in Visual Evidence ▲224
- Questioning the Questions: Sustaining Self-Evolution in Reasoning Models ▲85
- Improving Test-Time Scaling with Adaptive Looped Transformers ▲62
- Hybrid Reasoning Agents ▲38
About this data
Metadata from the arXiv API (verified); upvotes from Hugging Face Daily Papers; citations from Semantic Scholar. Last refreshed 2026-10-22.
The takeaway is written by AIMap from the abstract; the abstract itself is not reproduced — read the original.