AK(@_akhaliq)
BDH-CQ In-Context Learning with Recurrent Latent Reasoning paper: https://t.co/5AiCPxyujR
6.5内容质量
TL;DR · AI 摘要
BDH-CQ论文提出基于循环潜在推理的上下文学习方法，在ARC-AGI-1任务中以低成本实现29.5%的通过率。
核心要点
- 150M参数模型在ARC-AGI-1任务中达到29.5%通过率
- 单任务推理成本约0.0007美元
- 潜在迭代替代长文本推理提升效率
结构提纲
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- BDH-CQ论文
- 方法创新
- 循环潜在推理机制
- 实验成果
- ARC-AGI-1 29.5%通过率
- 成本优势
- 0.0007美元/任务
金句 / Highlights
值得收藏与分享的关键句。
潜在迭代替代长文本推理链是真正的效率杠杆
150M参数模型在ARC-AGI-1任务中达到29.5%通过率
单任务推理成本约0.0007美元，显著低于传统方法
#机器学习#自然语言处理#论文#效率优化
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AK on X: "BDH-CQ In-Context Learning with Recurrent Latent Reasoning paper: https://t.co/5AiCPxyujR"
-  AK @_akhaliq BDH-CQ In-Context Learning with Recurrent Latent Reasoning paper: huggingface.co/papers/2608.09…  [](https://x.com/_akhaliq/status/2087591432697651424/photo/1) 5:25 PM · Aug 12, 202618.9K Views 6 17 84 46
-  Jester @lajoiedeslutins Aug 12 recurrent latent reasoning is just a fancy way of saying it thinks in its room and won't tell you about it 2 178
-  Harry Tandy @HarryTandy Aug 12 Recurrent latent reasoning sounds like a promising direction for in-context learning 138
-  Alex Freitas @AlexFreitasAI Aug 13 A 150M parameter model hitting 29.5 percent pass at 2 on ARC-AGI-1 for about $0.0007 per task is the interesting part, since it shifts the frontier on cost rather than raw score. Latent iteration instead of long verbalized chains is a real efficiency lever. 30
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