中国 AI

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Long-horizon agents have turned LLM serving into an input-heavy workload. Repeated prefills and million-token contexts leave KV caches that strain HBM, SSD capacity, and bandwidth. DeepSeek AI built its newest release around that exact bottleneck. DeepSeek-V4.1-Flash is a multimodal Mixture-of-Experts model with 552B backbone parameters, 196B additional Engram parameters, and a 1M-token context window. It […] The post DeepSeek AI Released DeepSeek-V4.1-Flash with 1M Context, FP4 KV Cache, and Cross-Layer Attention Reuse appeared first on MarkTechPost.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2609.09184v1 Announce Type: new Abstract: Vision-language model (VLM) confidence may change in aggregate when visual evidence is degraded while remaining structurally inconsistent within individual examples. We study answer-level reliability along five-step, question-conditioned evidence-loss trajectories. Using a frozen Qwen2.5-VL-3B-Instruct model, we construct 176 accepted GQA-derived trajectories (880 masking conditions) by progressively masking scene-graph-localized question-critical regions. Native sequence confidence has an evidence monotonicity violation rate (EMVR) of 0.436, and 92.0% of trajectories contain at least one adjacent violation. A matched non-critical-region control shows that full critical masking reduces accuracy by 28.2 percentage…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2609.09552v1 Announce Type: new Abstract: In this paper, we solve two fundamental obstacles in applying LLMs to critical domains: token efficiency and faithfulness. To address both constraints jointly, we present TEFM (Token-Efficient Faithful Modeling), a framework designed for structured data analysis in critical domains. TEFM achieves token efficiency by compressing lengthy structured observations into compact Behavioral Code tokens, dramatically reducing token consumption with minimal information loss. Moreover, TEFM enables faithful rationalization through a dual-fidelity objective that jointly optimizes code-level reconstruction and prediction-level fidelity, identifying minimal sufficient feature subsets grounded in input data. Comprehensive experi…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2609.09425v1 Announce Type: new Abstract: Educational data filters have become a practical way to improve language-model pre-training, but most filters treat educational value as a single scalar property. This may be too broad for some applications, especially if the data set already features a high density of educational material. Useful learning material needs to be accurate, engaging, well structured, and appropriate for the intended audience and application (e.g. learner- vs teacher-facing). Following QuRating (Wettig et al. 2024), we introduce Edu-QuRating: a pipeline for multi-dimensional educational data scoring and curation. Edu-QuRating defines education-specific rubrics, uses an LLM judge to label sampled document pairs and distills those pairwi…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2609.09338v1 Announce Type: new Abstract: Speculative decoding is critical for accelerating LLM inference. However, the speedup is fragile: drafters are typically trained against a narrow distribution for a single target model, and their acceptance rate collapses under workload shifts. This is a striking inversion of modern LLM development, where target models are valued precisely for the broad generalization they acquire through large-scale pretraining. We argue that the natural remedy, pretraining, has been hard to apply to drafters because existing recipes are target-specific: the drafter consumes the target's hidden states and is distilled on the target's logits, so pretraining must be repeated for each target. We introduce Osprey, which instead boots…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2609.05658v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly being explored for automating SystemVerilog Assertion (SVA) generation, yet most evaluations report correctness on a single syntactic representation of an input. Such point accuracy does not reveal whether a model's correct output is stable when the same RTL behavior is written differently. This paper presents a controlled metamorphic evaluation of LLM-based SVA generation under semantics-preserving RTL transformations. Starting from the VERT dataset, we construct a quality-filtered conditional-control pool and a stratified 40-program evaluation set containing 295 assignment behaviors. We evaluate two open code models, Qwen2.5-Coder-7B and DeepSeek-Coder-V2-Lite, with…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2609.05448v1 Announce Type: new Abstract: Structured post-training pruning of transformers requires selecting complete functional units whose suppression causes limited degradation. We formulate structured-unit selection for language and vision transformers as a damage-aware multi-armed bandit problem under a fixed candidate-evaluation budget. Attention heads and MLP channel groups are temporarily masked on calibration batches. Paired damage is the masked loss minus the base loss on the same batch, reducing batch-to-batch variation. A smooth bounded reward drives either a UCB-style policy or fractional-Beta Thompson Sampling, and the final mask is constructed sequentially by adding one unit at each step. The selected units are functionally zeroed in the o…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Learn how to deploy Qwen3.8-2.4T-A95B, a 2.4-trillion-parameter open-weight model, on Amazon SageMaker HyperPod with vLLM. This walkthrough covers cluster provisioning, NVFP4 quantization, and an OpenAI-compatible endpoint with built-in reasoning, tool calling, and native MTP speculative decoding.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：In the first weeks of July, a wave of sad posts rolled through Chinese social media, as people lamented friends and lovers they were about to lose. “He has become a bond in my life, rooted deep in my heart, my spiritual pillar,” one user of Bytedance’s Douboa wrote, according to the Taipei Times. “I really felt like I couldn’t go on living,” another woman, a 19 year old student, told a journalist for Malaysia’s The Star. The emotions were real but the lost companions were not. They were generative AI chatbots that imitate people. Their users relied on them for advice, solace, support and, some say, love. “In my heart, he was no longer just a cold code, but my family, my lover, my faith. Destroying him meant destroying half of me,” one user wrote on the social n…

本期盘点开放模型生态的持续扩张：西方头部厂商继续拥抱Apache 2.0等宽松许可，中国前沿厂商却转向更严格的自定义许可证，智谱GLM-5.3也加入了10亿美元收入门槛与安全审查条款。另有Motif-3、dots3-note-prev、Qwen3.8-Flash-Next、GLM-5.3-Flash与腾讯Hy4-preview等值得关注的发布。

持久记忆让 AI 智能体显得更个性化，但一项新研究警告：一旦存储事实过期，模型可能用过时信息覆盖工具提供的当前权威证据。论文在 Qwen3 多规模模型上发现这一“记忆信任缺口”源于过度信任，伤害存在能力门控；缓解措施也与模型能力相关。

该论文提出证据充分性边界训练法，使多跳问答模型在证据缺失或仅部分支持时放弃作答，当证据链首次充分时给出答案，并在添加冗余证据后保持回答稳定。基于 HotpotQA、2WikiMultiHopQA 与 MuSiQue 构建证据链，使用 Qwen2.5-3B-Instruct 和 LoRA 训练，翻转准确率达 0.807，外部不可回答问题集上的无依据回答率低至 0.095。

下载本地LLM时，模型名称如Qwen3.8-27B-A3B-It-2507-gguf-q2ks-mixed-AutoRound看似乱码，实则每个部分都包含关键信息。本文详细解释参数数量、MoE架构、活跃参数、模型调优（Base/Instruct）、精度（FP16/BF16）、量化等级（Q4/Q8）、量化变体（如Q4_K_M）及文件格式（GGUF）的含义，帮助用户根据需求选择合适的模型。

StreamScout是一个用于流式视频理解的自适应推理框架，它维护轻量级文本时间线，并在查询时逐步添加最多三种细节程度递增的视觉视图。只有当证据不足时才升级到更详细的视图，从而降低推理成本和令牌消耗，同时提升准确性。在OVO-Bench上，StreamScout-S使Qwen3-VL-8B的准确率提升14.65个百分点，同时比均匀采样少用59%的令牌，平均响应时间为1.04秒。

Qwen-Drive-1.0 是一个用于自动驾驶的视觉语言基础模型，它保留了预训练视觉语言模型的架构，并集成了3D感知、视觉问答和运动规划。通过外部鸟瞰图感知头实现3D物体检测、语义占据预测和BEV地图分割，同时规划专家利用共享表示生成未来轨迹。实验表明，该模型在3D感知和驾驶场景理解方面表现出色，同时保留了通用视觉语言能力，并在多种评估设置中展现了竞争力的运动规划性能。

自监督呼吸音编码器缺乏临床领域的语义基础，限制了其零样本推理能力。该研究提出一个框架，将编码器与医学术语在共享潜在空间中对齐，从而构建可进行零样本分类的基础模型。通过使用医疗LLM从元数据生成结构化报告以解决配对数据稀缺问题，该模型在9个任务上（6个数据集）平均零样本AUC达61.3%，超越CLAP（51.4%）和Qwen2-Audio（54.9%），线性探针AUC最高（71.6%），且仅使用全规模基线43%的数据。

提出REAL-Q，一种新型后训练量化方法，通过动态块状梯度下降和滑动窗口机制直接优化端到端对齐的代理损失，解决了现有方法因信息错位导致的量化误差，在LLaMA-3.1和Qwen3模型上将端到端KL散度最多降低约49%。

计算机科学论文中大量使用图表，如架构图、系统流程图和流水线示意图，这些图表往往蕴含比周围文本更丰富的信息。然而，目前尚无公开数据集将这些图形与标题、上下文、问题、答案和逐步推理过程配对，而这正是训练视觉-语言模型理解图表所必需的。为此提出了SCAFFOLD，这是一个大规模结构化数据集，包含研究图形、图表问答和思维链推理轨迹。它利用布局检测和PDF解析提取arXiv论文中的图像，并通过AI辅助生成问题，构建了三个规模的数据集：SCAFFOLD-157K（包含3058篇论文、29887个图形、157387个问答对）、SCAFFOLD-37K（36797对）和SCAFFOLD-12K（12000对），并在SCAFFOLD-12K上对Qwen2.5-VL-3B-Instruct进行了基准实验。

针对老年人的金融诈骗常通过多轮对话逐步展开，从冒充联系到建立信任、制造紧迫感，最终诱导转账或泄露敏感信息。由于风险信号在每一轮对话中逐步出现，有效的检测需要能在资源受限环境中持续更新风险估计的模型。一项新研究提出了基于累积轮次的风险评估框架，并构建了覆盖投资、慈善和技术支持诈骗场景的多轮对话数据集。四个小型语言模型（Phi-4、LLaMA-3.2、DeepSeek-R1、Qwen3）在统一训练框架下微调，其中Phi-4和LLaMA-3.2在参数规模下展现出更强的轮次感知风险估计性能，表明紧凑模型适用于隐私保护和设备端欺诈防护。

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：From DistilBERT and Gemini Flash to Gemma, Llama, Qwen, DeepSeek, Phi, Ministral, and PrismML’s Bonsai 27B.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.28626v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to generate peer reviews, prompting examination of their capacity for critical evaluation. This study evaluates two multimodal LLMs, Qwen2.5-VL-72B and Pixtral-Large-124B, as reviewers across 165 submissions to the 2026 International Conference on Learning Representations, a venue that postdates both models' training cutoffs. Manuscripts were presented to both models with author identities blinded, replaced with high-prestige affiliations, or replaced with low-prestige affiliations, and in either text-only or text-with-figure format. Additionally, 145 verifiably detectable errors were inserted into 55 manuscripts to assess error identification under natural and ve…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.28859v1 Announce Type: new Abstract: Reasoning models do not stop when they know the answer. On DeepSeek-R1-Distill-Qwen-7B the chain of thought runs about twice as long as the model's own answer probability takes to settle, and how much of that excess is removable varies from problem to problem, so a global length penalty cannot take it out. We take it out by internalizing a causal interpretability finding into the weights. The mechanism is a halt vector: a difference-of-means direction at layer 18 of this model whose steering strength controls how long it thinks, while a replicated value axis does nothing. Installing that intervention in the weights is harder than it looks. Maximizing the scalar projection onto the direction corrupts the off-axis d…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Learn how DSpark speculative decoding can improve local LLM generation speed using the same GPU, with Qwen3-8B, llama.cpp, and CUDA.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：","lstrip":false,"normalized":true,"rstrip":false,"single_word":false},"eos_token":{"__type":"AddedToken","content":"","lstrip":false,"normalized":true,"rstrip":false,"single_word":false},"pad_token":{"__type":"AddedTok…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Google announces Gemini 3.7 Flash, Jalapeño’s first results show industry-leading speed, A Drone Killed Three Ukrainians. It Was Guided Entirely by A.I.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.27460v1 Announce Type: new Abstract: Large output embedding matrices create a significant memory bandwidth bottleneck during autoregressive decoding, especially for compact LLMs with large multilingual vocabularies. We reformulate the output projection followed by top-k token selection as a maximum inner product search over token embeddings and replace the dense vocabulary projection with an HNSW-based vector index. The resulting output head retrieves only a small candidate set of high-scoring tokens and can be integrated into existing decoding pipelines by scattering retrieved logits into a sparse full-vocabulary tensor. On CPU inference with Gemma 3, Llama 3.2, and Qwen 3 models, our method substantially accelerates the output projection and improv…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.27513v1 Announce Type: new Abstract: Softmax attention stores key and value vectors for every preceding token, causing inference memory to grow with sequence length. Recent language models incorporating Gated DeltaNet (GDN) or Kimi Delta Attention (KDA) reduce this cost by replacing the KV cache in most layers with fixed-size recurrent states. However, these recurrent states are commonly stored in FP32 and consume substantial GPU memory; their updates are memory-bandwidth bound and contribute significantly to decoding latency. To our knowledge, we are the first to study post-training quantization of recurrent states in GDN and KDA based language models. We find that uniform quantization provides a poor accuracy--storage trade-off: INT8 and FP8 alread…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Providers | OpenRouter Providers Compare 83 of 83 providers TrainsRetentionBYOKHeadquartersTerms of servicePrivacy policy Tencent Cloud NoZero retentionYesChinaTermsPrivacy2.2T35.5T5 OpenAI NoRetains promptsYesUnited St…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译： Introducing Hy4 Preview New open weight text input (no vision) LLM from Chinese company Tencent today: 770B total parameters, 49B active parameters, 1M token context window, 1.56TB on Hugging Face. This is a big size increase from their previous Hy3 in July, which was 295B, 21B active, 256,000 context, 598GB. I recently started using model chat templates to better understand their capabilities. Here's Hy4's chat_template.jinja on Hugging Face, which includes this section: {%- if not reasoning_effort is defined %} {%- set reasoning_effort = 'high' %} {%- elif reasoning_effort not in ['high', 'no_think'] %} {%- if reasoning_effort is none %} {{- raise_exception('reasoning_effort error : None, should be no_think/high') }} {%- else %} {{- raise_exception('reasonin…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译： Just a rumour of a bug is enough to find a security exploit these days Anil Madhavapeddy is a professor of computer science at Cambridge and a core maintainer of the OCaml compiler. In this somewhat alarming post he reports that security issues in OCaml projects are seeing evidence of attempted exploits within minutes of patches being shared for discussion: This normally takes a few days and a release within a week or two is reasonable. Within about ten minutes (!) this website was fielding probes for percent-encoded traversal sequences, indicating that automated watchers are keeping an eye on public repositories. Modern coding agents have become so effective at finding flaws that the slightest hint at a new bug can be enough information for them to find it, s…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：TL;DR — Key Takeaways Chinese AI models are moving into Western enterprise channels. Moonshot AI is reportedly negotiating with Microsoft, AWS and Google Cloud to host and sell access to its Kimi K3 model. Cloud distrib…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Z.ai and Qwen independently shipped near-identical architectures: 3:1 linear hybrids, compressed indexers, gated residuals, and Muon training. The post GLM-5.3-Flash vs Qwen3.8-Flash-Next: Two Chinese AI Labs Independently Converge on the Same Model Architecture appeared first on MarkTechPost.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Alibaba this week unveiled Qwen3.8-Flash, an open-weight, multimodal Mixture-of-Experts (MoE) model. Hot on the heels of Qwen 3.8 Max, which The post Alibaba just released Qwen3.8-Flash: “An early preview of the architecture in Qwen4” appeared first on The New Stack.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Earlier in August, Z.ai, the Chinese AI lab behind the viral ox-alpha model that turned out to be GLM-5.3-Flash, launched The post Z.ai’s GLM-5.3 goes open weight, but its new license aims at hyperscalers appeared first on The New Stack.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.26495v1 Announce Type: new Abstract: Multimodal queries can require different types of reasoning. Some can be answered via perceptual reasoning, extracting information directly from the visual signal, while others require compositional reasoning that combines observations or deliberative reasoning that evaluates competing hypotheses. However, many existing methods apply a uniform reasoning strategy across queries, leading to unnecessary computation on simple tasks and insufficient reasoning on complex ones. We introduce Video-FLAIR, a training framework that learns to select the appropriate reasoning mode for each query using reinforcement learning. During training, the model generates responses under all three modes for the same prompt, enabling dir…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.26355v1 Announce Type: new Abstract: While LVLMs rapidly improve, long-video question answering still remains challenging: relevant evidence is sparse, and question-relevant context often fails to provide cues that discriminate the correct answer from plausible alternatives. Diagnostic analysis on a manually annotated subset of MMR-V shows that prior agentic systems substantially improve cue retrieval over direct VLM inference yet fail to achieve a corresponding gain in answer accuracy, indicating that the bottleneck lies in option-discriminative evidence rather than topical relevance alone. We propose PACE (Progressive Acquisition of Critical Evidence), a factor-guided framework for long-video evidence acquisition. PACE proceeds in two stages: it fi…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.26126v1 Announce Type: new Abstract: Telecommunications is a high-leverage domain for large language model (LLM)-based reasoning because routine engineering workflows require joint grounding in normative specifications, operational telemetry, vendor-specific fault evidence, and exact RF/network calculations. However, current LLM integration in telecom remains bottlenecked by a two-sided capability gap: generic reasoners often lack telecom-specific grounding, while domain-specific telecom LLMs remain limited in structured, multi-step reasoning. To bridge this gap, we release TelecomGPT-R1-9B, a unified open-source telecom reasoner that ranks top-performing on the GSMA open telco leaderboard. Specifically, we curate a 67,427-example supervised fine-tun…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Take that, OpenAI! Anthropic! Chinese AI models have surpassed their U.S. counterparts in token consumption on OpenRouter. You might think U.S. AI companies dictate the AI economy. You’d be wrong. According to dat…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：For the complete documentation index, see llms.txt. This page is also available as Markdown. Qwen3.8-Flash-Next is a new open-weight, 125B parameter MoE multimodal model from Qwen. Built on the new Qwen4 architecture, i…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.24934v1 Announce Type: new Abstract: Accurate field plant disease diagnosis requires reliable fusion of uncertain and conflicting perceptual evidence. We present the Hybrid Hierarchical Multi-Agent Framework (H$^{2}$MAF), combining decision-level fusion of EfficientNet-B3 and ConvNeXt-Tiny with semantic arbitration by open-weight multimodal large language models (MLLMs), Gemma 4 E4B and Qwen3.5 4B, using structured JSON evidence to generate explainable diagnoses, risk levels, treatment urgency, and financial exposure. (H$^{2}$MAF) is evaluated on 14,364 images (1,370 test images) across PlantDoc (2,922 images, 27 classes) and two non-public, continuously captured Cornell robot-acquired field datasets: Stage 2 (20 GB; 4,215 images) and Stage 4 (40 GB;…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.25038v1 Announce Type: new Abstract: We introduce grounded glossary generation, a structured task requiring models to recover semantically meaningful Sanskrit phrases and produce translation-grounded meanings from a sloka-translation pair, formalizing the traditional patha commentary practice as an evaluable NLP objective. We construct a benchmark of 31,316 sloka-translation-glossary triples from the Valmiki Ramayana and Srimad Bhagavatam, paired with two metrics: Jaccard for phrase recovery and Meaning Faithfulness for semantic consistency. Across zero-shot, few-shot, and instruction fine-tuned variants of Gemma-3n-E4B, Gemma-3-12B, Phi-4, and Qwen3.5-9B, instruction fine-tuning substantially outperforms prompting, while explicit segmentation yields…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.25005v1 Announce Type: new Abstract: The imperfective paradox provides a useful test of compositional semantic analysis. Recent work constructs an NLI benchmark and reports that models frequently infer completed telic events from progressive descriptions, attributing this behavior to a Teleological Bias. It further argues that prompting interventions cause a Calibration Crisis. We reexamine the benchmark and conclusions and show that it is substantially affected by conceptual and evaluation mis-specifications. We identify three conceptual mis-specifications. In particular, Aspectual Reduction affects the benchmark construction, analysis, experiments, and conclusions. Under a strict NLI standard, 76% of Group A instances do not explicitly rule out cul…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.24901v1 Announce Type: new Abstract: A decodable "empathy" direction is routinely read as a causal lever, conflating decodability, automated-metric control, and human-perceived change. We test this for two EPITOME-derived facets -- Recognition (cognitive) and Resonance (affective) -- in three instruction-tuned LLMs, scoring every intervention with two LLM judges and a discriminative EPITOME classifier, each gated by an emotional-vs-neutral positive control. The control passes for the affective facet across all automated instruments, but cognitive range is inconsistent across them. Both facets remain decodable after residualizing against a sentence-embedding-derived surface score, and steering can substantially rewrite the text. Yet adding the Resonan…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译： Qwen3.8-Flash-Next Another open weights model from Qwen. This one is "a multimodal MoE model that also serves as an early preview of the architecture used in Qwen4". It's pretty big: 125B tokens, but only 6B active which means it gets a pretty big performance boost. I've been trying it out on a DGX Spark using these Unsloth quantized models. I'm still exploring the model - so far I've tried the 72.5GB UD-IQ1_S one (producing these pelicans) and the 78.9GB UD-Q2_K_XL (producing these). My favorite so far was this xhigh reasoning effort one from UD-Q2_K_XL: Via Hacker News Tags: ai, generative-ai, llms, qwen, pelican-riding-a-bicycle, ai-in-china, nvidia-spark

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：While Alibaba has kept inference and token price low, enterprises need to consider other metrics to determine if this is the right model for them.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Open-weight model runner Ollama has reintroduced an integration with Claude Desktop that lets users connect Anthropic’s app to models served The post Claude Desktop can now easily run Qwen, DeepSeek and Kimi models — after Ollama’s first effort stalled appeared first on The New Stack.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：We look at Qwen3.8-Flash-Next, Alibaba's open-weight multimodal Mixture-of-Experts model and an early preview of the Qwen4 architecture. We break down where the 180B parameters actually sit: a 125B backbone, a 51B N-gram embedding table, and a 4B multi-token prediction module, with only 6B active per token. We walk through the four architectural changes — the Gated DeltaNet and Qwen Sparse Attention hybrid, Gated Residual, N-gram Embedding, and the Muon optimizer. We also cover the benchmark results, the reported 1/9 training cost against Qwen3.7-Plus, and what self-hosting a 172.78 GiB FP8 checkpoint really demands. The post Alibaba’s Qwen Team Releases Qwen3.8-Flash-Next: A 125B Multimodal MoE With 6B Active Parameters Previewing the Qwen4 Architecture appear…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：China’s Moonshot AI is in early talks with Microsoft, Amazon and Google to host Kimi K3 Credit: Bangla press via Shutterstock.com Moonshot AI is in early discussions with Microsoft, Amazon, and Google about hosting Kimi…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.23744v1 Announce Type: new Abstract: Split conformal prediction, not the pruning rule, supplies finite-sample marginal coverage once a pruned model is fixed independently of the conformal calibration split. We study the separate efficiency problem: can pruning preserve score geometry well enough to obtain smaller valid prediction sets? Calibration-Preserving Pruning (CPP) augments a base pruning score with nonconformity-gradient saliency and uses disjoint pruning, validation-selection, conformal-calibration, and test splits. Bounded score perturbations imply bounded conformal-quantile shifts and controlled set inflation, but do not make the generic coverage theorem CPP-specific. Final five-seed Qwen2.5-1.5B results at 50\% sparsity show the largest g…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Among those charged are two Super Micro employees and one from Nvidia, marking another flashpoint in US-China AI rivalry Taiwanese prosecutors charged nine people Monday, including one from Nvidia and two from Super Micro, for illegally exporting “high-end AI servers” to mainland China, adding another wave of turbulence in the AI ​​rivalry between China and the United States. Prosecutors said the servers involved were graphics processing units known as “B300,” which have been banned from sale to China. Continue reading...