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China‑bound Apple Intelligence enters beta testing in 4 days! Alibaba officially announces full integration of Qwen.
Author: Li Shuiqing | Editor: Xin Yuan — According to Zhidongxi (WeChat official account: zhidxcom), on October 31, Alibaba announced a partnership with Apple to integrate Qwen into Apple Intelligence. Chinese users will be able to directly access Qwen’s capabilities for text and image understanding, content generation, and more, within Siri, writing tools, and visual intelligence features. These functions will begin beta testing starting November 4 alongside the latest iOS Developer Beta version, and...
Prefix Keys and Values Matter Late: RIPE Caching for Self-Speculative Decoding in Looped Language Models
arXiv:2610.34436v1 Announce Type: new Abstract: A looped language model runs one block of layers $R$ times per token, so its key-value (KV) cache holds $R$ planes per recurrent layer, and self-speculative decoding (draft with a few loops, verify with all $R$) reads them all. We find that prefix keys and values matter late: at equal perturbation size, loops 25-32 are $e^{15.0}$ (Huginn-0125) and $e^{12.8}$ (Raven-Llama) times more sensitive than loops 2-9, and the planes approach the last one geometrically, at 0.88 and 0.81 per loop. Whether the deepest plane alone suffices then depends on how ripe the late planes are: on Raven, storing only it is indistinguishable from a bf16-to-fp32 change; on Huginn, 1% of positions carry 74% of its error. We turn this into RIPE, which stores the deepest
Generalizing an LLM from 8k to 1M Context using Qwen-Agent
We’ve created an agent using Qwen2 models with an 8k context size to understand documents with 1M tokens, surpassing RAG and native long-context models. This agent was also used to generate data for training new long-context Qwen models.