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research#llm📝 BlogAnalyzed: Jan 4, 2026 07:06

LLM Prompt Token Count and Processing Time Impact of Whitespace and Newlines

Published:Jan 4, 2026 05:30
1 min read
Zenn Gemini

Analysis

This article addresses a practical concern for LLM application developers: the impact of whitespace and newlines on token usage and processing time. While the premise is sound, the summary lacks specific findings and relies on an external GitHub repository for details, making it difficult to assess the significance of the results without further investigation. The use of Gemini and Vertex AI is mentioned, but the experimental setup and data analysis methods are not described.
Reference

“LLMを使用したアプリケーションを開発している際に、空白文字や改行はどの程度料金や処理時間に影響を与えるのかが気になりました。”

PermalinkZenn Gemini
Research Paper#Neural Architecture Search, Large Language Models, Computer Vision🔬 ResearchAnalyzed: Jan 3, 2026 15:53

LLM-Based Neural Network Architecture Design: Few-Shot Prompting and Efficient Validation

Published:Dec 30, 2025 10:01
1 min read
ArXiv

Analysis

This paper addresses the challenge of automated neural network architecture design in computer vision, leveraging Large Language Models (LLMs) as an alternative to computationally expensive Neural Architecture Search (NAS). The key contributions are a systematic study of few-shot prompting for architecture generation and a lightweight deduplication method for efficient validation. The work provides practical guidelines and evaluation practices, making automated design more accessible.
Reference

“Using n = 3 examples best balances architectural diversity and context focus for vision tasks.”

PermalinkArXiv