NextBigFuture

Tokens and Tokenization are an Important for Fundamental LLM Understanding

Tokens are the fundamental units that LLMs process. Instead of working with raw text (characters or whole words), LLMs convert input text into a sequence of numeric IDs called tokens using a ...
VentureBeat

Researchers automated LLM reasoning strategy design and cut token usage by 69.5%

Test-time scaling (TTS) has emerged as a proven method to improve the performance of large language models in real-world applications by giving them extra compute cycles at inference time. However, ...
i-SCOOP

Token minimizing, how to cut LLM costs without losing quality

Token minimizing is the fastest way to lower LLM costs and latency. Learn practical techniques: prompt trimming, compaction, semantic caching and smart routing.
Forbes

Freedom: The Rise Of The LLM-Agnostic, Token-Efficient Agentic System

This voice experience is generated by AI. Learn more. This voice experience is generated by AI. Learn more. Companies initially embraced AI for "tokenmaxxing," driving up usage with incentives and ...
InfoQ

Meta Open-Sources Byte Latent Transformer LLM with Improved Scalability

Meta open-sourced Byte Latent Transformer (BLT), an LLM architecture that uses a learned dynamic scheme for processing patches of bytes instead of a tokenizer. This allows BLT models to match the ...