オープニングストーリー 931枚の温度計画像に、一枚ずつ正解を付けた。 「4.6℃」 「4.7℃」 「5.0℃」 Day29で完成した教師データ作成ツールを使い、AIに教えるための材料をようやく準備できた。

サンプリング手法も処理速度が向上している。従来のLLMは1トークンずつ順番に生成する「自己回帰型の逐次サンプリング」を採用するので、トークン数に比例してレイテンシが増加する。一方、Jevは出力構造が事前に確定している「並列サンプラー」を採用し、1回のクエリで全ての出力を並列に一括生成する。

Introduction A few years ago, I was in charge of demand forecasting models at my company. Even though the accuracy was decent ...

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A scramble for CentOS replacements sparked new community distros and vendor plays reshaping enterprise Linux dynamics.

I argued that a drone simulator should be easy to open as a URL, without becoming a toy. This is the architectural decision ...

Google Research open-sources RRSI, letting agents improve prompts, tools and memory without overfitting, lifting ...

Lakshya A. Agrawal, creator of GEPA, argues the AI industry has a sample-efficiency problem it is ignoring. Reinforcement learning like ...

According to @_avichawla, AnyJev extracts choices, scores, and probabilities from open causal LLMs with L0–L2 calibration for better decision outputs.

Raghu Praneeth Akula, a finance technology lead, explains why what a customer sees on the screen matters as much as the AI ...