arXiv (All)
open access
A Quantitative Study of Sustained Focus in Large Language Models via Repetitive Deterministic Prediction Tasks
We investigate the performance of large language models (LLMs) on repetitive deterministic prediction tasks and study how the sequence accuracy rate (SAR) scales with output length. Each such task involves the repetition of the same operation $N$ times. Examples of such tasks include letter replacem
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
- Large Language models are stochastic measuring devices#82773
- ESLM: Risk-Averse Selective Language Modeling for Efficient Pretraining#1050755
- Hearing the Order: Investigating Position Bias in Large Audio-Language Models#798651
- Hearing the Order: Investigating Position Bias in Large Audio-Language Models#797237
- Don't Forget! Decomposing the Training Dynamics of Memorization in Language Models#1129542
Record · ID 1142571
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