arXiv (All)
open access
Shared Weights, Selected Computations: How Looped Transformers Route What Each Loop Does
Looped Transformers repeatedly apply the same set of Transformer layers, giving them a recurrent architecture for latent computation. Their strong performance on iterative reasoning and length-generalization tasks suggests an appealing explanation: recurrence may provide an inductive bias that lets
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
- A Unified Perspective on the Dynamics of Deep Transformers#1120649
- Linear attention is (maybe) all you need (to understand Transformer optimization)#411952
- Decoding Looped Transformers Better for (Almost) Free#1161021
- Decoding Looped Transformers Better for (Almost) Free#1157533
- Event-Driven Language Models with Sparse Neural Activity for Neuromorphic Hardware#799641
Record · ID 1145059
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