发布节奏
最近 12 周每周中位浏览
最近 12 周发布时间
颜色 = 中位浏览爆款 vs 日常
爆款 = 浏览前 10%| 指标 | 爆款 | 日常 |
|---|---|---|
| 帖子 | 2 | 15 |
| 带媒体 | 0% | 0% |
| 回复 | 0% | 0% |
| 平均字数 | 268 | 221 |
| 中位浏览 | 3.6万 | 1,477 |
打法建议
只陈述相关性- 互动最好的时段是 05:00 前后（北京时间），中位浏览 1.2万。
- 一周里 周五 的中位浏览最高。
- 爆款里带媒体占 0%、回复占 0%；日常分别是 0%、0%。
- 当前节奏约 0.3 条/天，日均浏览 8,844。
内容结构
按本站分析的分类（近 120 天）还没有分类分析。
形式与语言
帖子占比- 纯文字17
回复占比 0% · 引用 64.7% · 带媒体 0%
互动最高的帖子
按浏览排序- 1
What would pretraining with zero real data look like? Can a randomly initialized model learn to generate all of its training data entirely through self-play? Find out below! :)
- 2
Introducing Context-Sharded Block Parallelism (CSBP), a new distributed parallelism strategy enabling significant training efficiency for diffusion LLMs, with gains that grow with context length 🚀 ⚡ 7.59× faster DFlash2 speculative decoding drafter training ⚡ 1.61× faster block diffusion fine-tuning ⚡ 1.33× faster autoregressive → block diffusion adaptation
- 3
@StanfordAILab grads hard at work! 🚀
- 4
The most capable models available in the world are using Terminal-Bench-Science to show it (just one week after the benchmark’s release). Congratulations to @StevenDillmann and team for their remarkable impact!
- 5
SWE-chat by @joabaum is a living dataset of human interactions with AI coding agents. 230K prompts from real developers collected in the wild are now available on HuggingFace 🚀 https://huggingface.co/datasets/SALT-NLP/SWE-chat
- 6
Some interesting and perhaps unexpected properties of tokenization for language models!
- 7
This work directly tackles the largely unexplored problem of submission overload at conferences. It argues that technical governance solutions can help everyone build trust and faith in our system, and in turn allows building on each other's contributions. Check it out!
- 8
Stanford AI Lab is alive and well at COLM! Check out these SAIL projects this week in SF. 🎉 https://ai.stanford.edu/blog/colm-2026/
- 9
Can the until-now idle CPUs do useful work while the GPU trains? Turns out they can cut training time a lot!
- 10
SAIL is live with EMNLP in Budapest! Congrats to @cyruszzhou on winning the outstanding paper award!🏆 https://ai.stanford.edu/blog/emnlp-2026/
- 11
Token-Gated LLMs achieve 11x faster processing over long-context inputs by learning to skim context and only forward tokens that matter. We saw this paradigm shift before when going from dense models to MoEs, and now TokenGate enables another generation of efficient LLMs!
- 12
Can a robot learn dexterous manipulation from just one human video? Check out our latest SAIL blog post on DexAgent, an agentic framework that turns a single egocentric video into robot training data using a self-evolving tool library! https://ai.stanford.edu/blog/dexagent/
点赞、转发、浏览是采集那一刻的快照，不是帖子最终数据；时间为北京时间。KPI 卡片里的同比只在账号有 8 周以上数据时显示， 否则只看近 4 周。“打法建议”只说明数据里的相关性，不代表因果。