中国 AI

Aerial-R1 是首个针对航空领域的多模态大模型框架，通过强化视觉推理来提升航空推理与分割能力。它借鉴 DeepSeek-R1 的推理激励机制，提出混合视角组优势加权回归（H-GAWR），在清晰输入上强制一致性，在模糊输入上严格惩罚幻觉，并引入显式拒绝机制。在 VSAI-Ref 基准上的实验表明，该方法在分割精度上显著优于 SFT 基线，同时大幅减少低信息场景中的致命幻觉。

该研究提出FSVL框架，结合轻量级ResNet50快速分类与YOLOv8n引导的慢速精细识别，并集成检索增强的Qwen3-14B生成领域知识。在607张测试图像上，快速分类器准确率达99.67%，级联校正后达100%；知识生成任务中，科学名称准确率从91.37%提升至100%， genus准确率从90.20%提升至98.82%，ROUGE-L从0.335提升至0.655。

一项针对Qwen3.5-27B工具使用任务的研究通过32个新场景发现，针对特定案例的残差写入干预能将熟悉的目标选择正确率从约27%提升到约90%，但这些增益在同任务中重组熟悉事实和标识符时消失，且对候选资格或是否应选择的独立判断改善甚微。

Yuval Kansal 等提出 Self-Direction 机制，让智能体在任务失败后从自身轨迹中提炼教训，并即时蒸馏成仅约基础模型参数量 0.02% 的低秩适配器，存入持久化经验库并按需路由。在 GAIA、Claw-Eval 和 Terminal-Bench 2.1 上，基于 Qwen 系列开放权重模型的智能体成功率提升 8.9 至 17.0 个百分点，重复错误显著减少，少量适配器还能泛化到数十个新任务。

Hugging Face Bio 发布 Carbon-X，一个基于 Qwen3-14B 的 14.8B 参数开源 DNA 语言模型，采用 Apache 2.0 许可。它可在对话中直接读取原始 DNA 序列并用英文作答，在 5 项 DNA 基准上平均得分 66.7%，仅次于 GPT-6.1 Sol 的 67.5%。

斯坦福大学与加州大学伯克利分校的研究者发布了 TokenGate，这是一个可插入现有大语言模型的学习式 Token 剪枝模块。它通过一个小型 MLP 门控为输入文档中的每个 Token 打分并只保留得分最高的一部分，从而缩短长上下文预填充的首 Token 延迟（TTFT）1.4 至 11.3 倍，峰值显存降低 1.5 至 3.4 倍，且不损失准确率。研究在 Qwen3-1.7B 与混合注意力的 Qwen3.5-2B 上验证，代码与检查点以 Apache-2.0 许可开源。

Analytics Vidhya 对 Moonshot AI 的开放权重模型 Kimi K3.1 Pro 进行实测。该模型在多项基准上接近 Claude Opus 5.5，在金融文档分析上领先，幻觉率较前代大幅下降，成本与生成速度优势明显；但在工具辅助 3D 任务与幻觉控制上仍落后于 Opus 5.5 和 GPT-6 Astra，完整权重将于 2026 年 11 月 4 日发布，独立验证尚待进行。

Moonshot 推出 Kimi K3.1 Pro，这是一款2.8万亿总参数、1040亿激活参数、支持百万token上下文的多模态混合专家模型，在独立基准测试中接近前沿水平。API 已在官网、应用和编程工具上线，官方承诺于2026年11月4日发布开放权重。Artificial Analysis 将其排在智能指数第四位（55分），仅次于 Opus 5.5 的58分，开放与闭源之间的差距若权重如期放出将从12分缩小到3分。

Moonshot AI 推出 K3.1 系列首款模型 Kimi K3.1 Pro，采用 MoE 架构，总参数 2.8T、激活参数 104B，上下文窗口达 100 万 token。它在 Terminal-Bench 4.0 上得分 65.7%，仅次于 Claude Opus 5.5；API 定价为每百万 token 输入 2 美元、输出 10 美元，低于 K3。开放权重预计 2026 年 11 月释出，目前尚不可自托管。

Moonshot 于 10 月 23 日将 Kimi K3.1 Pro 上线 Kimi API，目前尚未开放权重，但公司承诺在 2026 年 11 月 4 日前释出完整模型权重。其定价为输入每百万 token 2 美元、输出每百万 token 10 美元，与 GPT-6.1 Sol 持平，约为 Claude Opus 5.5 的一半。Artificial Analysis 给出 55 分，仅次于 Opus 5.5 的 58 分，是该机构记录中（即将）开放权重的模型首次如此接近前沿水平。

亚马逊云科技（AWS）宣布 SageMaker AI 无服务器模型定制功能全面推出训练会话（training sessions）。用户可自行编写并协调后训练循环，包括自定义损失函数、奖励信号和多轮强化学习，而 GPU 分配、分布式执行、故障恢复与检查点存储均由 SageMaker AI 托管。该功能支持 gpt-oss-20b、gemma-4-31B-it、Qwen3.6-27B 和 Qwen3.8-27B，按处理的 token 计费。

Liquid AI 推出专为实时请求路径设计的推理引擎 RIPL，可在单张 B200 上以 50 毫秒以内完成端到端生成。官方基准显示，其 p95 延迟比运行在 vLLM 上的 Qwen3.5 低 5.6 至 28.4 倍，Shopify 已将其用于生产搜索场景。不过 RIPL 仅面向企业客户、闭源，且所有数据均为自报。

该论文研究大型语言模型在处理法律叙事时，其内部激活中是否存在与CATO全部26个法律要素相对应的表征。作者在Qwen3-1.7B、Qwen3-8B和Gemma-2 2B三个开放模型上进行线性可解码性与稀疏自编码器稀疏定位实验，发现全部26个要素在三个模型中均可线性解码，最佳层测试AUC为0.86至1.00，并报告了78个模型特定的层-特征坐标。

该论文提出ReAPO，一种从随机最短路径（Stochastic Shortest Paths）视角重新审视信用分配、由GRPO简单修改而来的方法。它把额外生成预算分配给失败轨迹，使成功的重试得到强化而不奖励此前的失败。在Qwen3与OLMo-2（0.6B至4B）以及八个数学与科学推理基准上，ReAPO的首轮尝试表现优于GRPO和近期基线；在Qwen3-0.6B上，它以少39%和少31%的生成token分别达到GRPO双倍组大小和双倍训练步数的峰值准确率。

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Train Qwen3 on AWS Trainium with torchtitan three ways on a single trn2.48xlarge: the Hugging Face Transformers v5 modeling backend, torchtitan's native Qwen3, and NKI mixture-of-experts kernels. The same torchrun launcher and config structure work unchanged across all three paths, including loading and saving published Hugging Face safetensors.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：An open source “bomb” drops from China

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：An open source “bomb” drops from China

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2610.27040v1 Announce Type: new Abstract: General-purpose multimodal models, including GPT-6 Astra and Claude Opus 5.5, demonstrate broad capabilities in embodied manipulation. Our analysis indicates that this generality is accompanied by strong spatial perception, as Astra can determine where to interact with task-relevant entities amid visual distractors. Because spatial perception connects high-level reasoning to the physical scene, we investigate scaling visual grounding to strengthen the perceptual foundation of general embodied operation. We develop a unified data pipeline that combines robot manipulation recordings with complementary visual data from diverse sources. After automatically annotating the robot recordings, the pipeline converts the col…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2610.26937v1 Announce Type: new Abstract: Large clinical language models often require multi-GPU infrastructure, limiting local deployment. We introduce CERTA, a decision-level statistical certificate that determines when a quantized model may substitute for its native BF16 reference and escalates decisions that cannot be certified to it. Across eleven instruction-tuned models, both general-purpose and domain-adapted, from three families (7-72B), we evaluated 2,500 clinical-verification decisions from de-identified patient contexts (2,484 patients), with patient-disjoint calibration and testing. In the higher-power operational analysis, round-to-nearest 8-bit quantization at BF16 storage served 99.8-100% of held-out decisions across all eleven models; for…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2610.26866v1 Announce Type: new Abstract: Mechanistic studies of large language models localize function in parameters, attention heads or neurons, but those parts do not specify how one inference unfolds. We treat the ordered layer-state trajectory as the empirical process and test whether layer order defines a measurable computation. Across representative Qwen, Llama and Gemma checkpoints, realized order produced paths 1.60--2.24 times as aligned with their endpoint chord as endpoint-preserving shuffles. The order effect transferred to a lexical task, but was stronger in three random initializations of the same Qwen architecture, identifying an architecture-compatible scaffold rather than a training-specific law. Direct hidden states provided stronger m…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2610.26887v1 Announce Type: new Abstract: Large language model (LLM) agents are evolving from conversational assistants into Cowork Agents capable of autonomous planning, tool use, and complex task execution. Financial analysis requires these agents to integrate cross-document evidence and perform multistep numerical reasoning, but expert task authoring is costly, while direct LLM synthesis struggles to produce challenging tasks with reliable answers. We introduce Navalia, a framework for Cowork Agents that combines verifiable task synthesis with long-horizon post-training. Using corporate financial reports as its data source, Navalia semantically selects supporting evidence from complete fact sets and constructs executable computation chains to generate…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Western open models strike back

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：DeepSeek has open-sourced the inference engine it uses to serve its models in production, a step it said last year The post DeepSeek open-sources the inference engine behind its API. And Huawei gets equal billing with Nvidia. 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：DeepSeek has open-sourced DeepInfer, the Apache 2.0 inference framework that serves DeepSeek-V4.1-Flash in production. It targets NVIDIA SM100 GPUs and Huawei Ascend 950 NPUs, and ships offline inference only. The post DeepSeek Open-Sources DeepInfer, the Inference Framework Behind DeepSeek-V4.1-Flash 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：Use of tools ‘may result in the transfer of sensitive data to (China)’, defense department says The US government on Monday ordered its agencies to stop using artificial intelligence (AI) tools from Chinese startup DeepSeek, as the countries race for dominance in the fast-moving sector. A notice from the Department of Homeland Security (DHS) announced the ban on products developed by DeepSeek – and tools created using DeepSeek by third parties – saying they must be removed from IT devices. Continue reading...

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Congrats to DeepSeek V4.1 Flash 1017, MiniMax M3.1 Flash, and Mercury 3 (details below) but they’re not going to be in super wide use just yet.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Congrats to DeepSeek V4.1 Flash 1017, MiniMax M3.1 Flash, and Mercury 3 (details below) but they’re not going to be in super wide use just yet.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Multi-turn agentic workloads reprocess growing conversation context each turn, driving up latency. See how bi-directional KV cache transfer lets prefill and decode nodes reuse previously computed context across turns, cutting time to first token by up to 1.6x for Qwen3-32B and 3.3x for GLM-5-FP8 on Amazon EC2 P5en instances with EFA.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Not content with challenging the major frontier AI labs with lower-cost models, Chinese AI company DeepSeek is setting its sights The post DeepSeek made its name with cheap AI models. Now it’s undercutting search API rivals, too. 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:2610.22454v1 Announce Type: new Abstract: Running prompt tokens through fewer routed experts during prefill is a simple way to make mixture-of-experts (MoE) serving cheaper, yet it is unclear what this damages. We locate the damage with token-, layer- and attention-path interventions on short-answer question answering in four MoE families. At half the experts the F1 loss of three families is mostly answer form: the model still finds the answer but stops answering briefly. In Qwen3-30B-A3B this loss is made at the two chat-template tokens before the answer, and in DeepSeek-V2-Lite at the first 8-32 prompt tokens, where its instruction sits; by answer likelihood these positions account for about half and a fifth of the damage. Reduction changes the first to…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2610.22381v1 Announce Type: new Abstract: When a fact becomes harder to elicit from a language model, has the model lost it, or has it merely become less accessible? Parameter-updating test-time continual learning agents are usually evaluated by current output accuracy; we ask whether that measurement captures how prior experience shapes later updates. We test whether learning a specific factual binding produces a later relearning advantage beyond familiarity with its entity, answer vocabulary, format, or query slot. In Qwen2.5-1.5B, we compare three matched, counterbalanced histories: LEARNED receives the correct binding, CONTROL receives matched exposure without it, and WRONG receives a competing relation-valid answer in the same slot. After an identica…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Post-train open LLMs like Qwen, GLM and Kimi into custom models you own, with SFT or RL. You write the training loop. Together runs the hard infrastructure.

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 west the technology comes with at least some effort to provide security and a benign intent – not so from China There is much to fear from artificial intelligence developed in China and handed to businesses and governments in the west almost free of charge. Every month Chinese AI developers such as DeepSeek and Xiaomi dispatch the latest versions of their “open-weight” models that allow users to take the core software and use it without paying an expensive ongoing licence. Continue reading...

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译： Release: web-edit 0.1a2 web-edit (previously) is my tiny web app for browsing and editing files in a folder. I mainly built it for editing files on different machines via Tailscale. I added a "copy to clipboard" option for both text and image data. I needed this because I was running pelican prompts on a laptop from my phone (here's DeepSeek 4.1 Flash 1017) and I wanted an easy way to copy the resulting logs into a Gist. Tags: projects, pelican-riding-a-bicycle, deepseek

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：DeepSeek has just released DeepSeek-V4.1-Flash-1017, a new checkpoint of its V4.1-Flash model. It is live on the DeepSeek website, app and API. DeepSeek claims stronger scores, faster task completion and shorter outputs than the September V4.1-Flash release. The company calls it a Pareto improvement in performance and cost. Alongside it, DeepSeek opened its Search and […] The post DeepSeek Just Released V4.1-Flash-1017, Scores 77.0 on DeepSWE, Opens Search API 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:2610.20627v1 Announce Type: new Abstract: Vision-language models are increasingly used for driving-scene understanding, yet their defensive reasoning under weakly structured Global South traffic remains unclear. We introduce BD-HazardVLM, a diagnostic benchmark of 500 Bangladesh road-scene images, comprising 375 from RSUD20K and 125 from TFP-BD, annotated for hazard presence, primary hazard category, risk timing, severity, defensive actions, visible evidence, and hallucination- and Western-road-bias traps. We evaluate Qwen2.5-VL-7B, InternVL3-8B, and MiniCPM-V-4.6 using a shared zero-shot task definition and structured output schema. Binary hazard-presence accuracy ranges from 88.0 percent to 89.2 percent, essentially matching the 89.0 percent majority-cl…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2610.20633v1 Announce Type: new Abstract: Long-horizon failures of language models are usually measured as end-task success or as a horizon length. We instead score, step by step, three components of a 400-step ledger task in which every step is easy in isolation and the model's own answers are re-injected into its context: recognising a record already seen 5 to 300 steps earlier, processing the current record locally, and writing a counter incremented at every step. On Qwen3-8B (non-thinking, 20 seeds, replicated on a second machine and backend), with every step scored component by component, two results hold. First, duplicate-recognition errors rise late in the episode -- from 0.0019 to 0.011 per step at risk across step 200 -- while the counter makes n…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2610.20681v1 Announce Type: new Abstract: Formal inequality proving requires mathematical reasoning and exact computation, often connected through intermediate lemmas and goal reformulations. We introduce IneqAgent, an agentic framework combining LLM reasoning with symbolic computation for inequality proving in Lean4. Lemma-level Monte Carlo Tree Search coordinates lemma construction, goal reformulation, and symbolic backend selection. Complementary sum-of-squares, Bernstein, and successive difference-substitution backends produce exact certificates for local Lean proof generation. The agent integrates these proofs with LLM-generated steps and uses Lean feedback to construct complete proofs checked against the original theorem statements. On four benchmar…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2610.19033v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) applies every rank-one component of its update to every token, and mixture-of-experts variants replace this with a learned router that activates a fixed number of components. We argue that the number of components a token needs is a detection question with a classical answer. A token's responses to the rank-one components of a trained LoRA consist of a few large values on a Gaussian noise floor whose width matches that of an untrained adapter, and the floor carries cross-talk rather than adaptation. We introduce SQUELCH, a router-free mixture of rank-one experts that (i) soft-thresholds each token's responses at a multiple of its own robust noise level, so the active rank varies per toke…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2610.18883v1 Announce Type: new Abstract: Token-selection methods for on-policy distillation (OPD) prioritize student-visited positions using diverse signals, including uncertainty, teacher-student disagreement, and mismatch direction. We instead derive token importance from the distillation objective itself: how much can each position's loss decrease under an equal local KL budget on the student's predictive distribution? The answer is the utility $s_t=g_t^\top F_t^\dagger g_t$, where $g_t$ is the logit gradient and $F_t$ is the categorical Fisher matrix. For reverse-KL it equals the student-weighted variance of the student-teacher log-probability ratio, and for forward-KL it equals the Pearson $\chi^2$ divergence, so the appropriate utility follows from…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2610.18878v1 Announce Type: new Abstract: Post-training reasoning LLMs often requires making a tradeoff between costly supervision and compute inefficiency: supervised finetuning typically requires reasoning traces, which is data inefficient since in many applications one only has prompt-answer tuples and lacks the groundtruth chain-of-thought (CoT) sequence. On the other hand, reinforcement learning (with verifiable rewards, for example) can operate without CoT data but it is much more compute-inefficient since it requires multiple autoregressive completions per optimization step, none of which are guaranteed to give a non-zero policy gradient. In this work we explore a middleground, a self-distillation technique that uses answer-only data, treating CoT…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2610.14190v1 Announce Type: new Abstract: Explaining a vision--language model (VLM) prediction requires tracing training-example influence to the multimodal evidence that mediates it, yet existing attribution methods largely operate only at the sample level. Recent latent-mediated influence methods provide feature-level attribution in language models, but lack the multimodal and spatial structure needed to ground influence in VLMs. We introduce MOSAIC (Multimodal Orthogonalized Sparse Autoencoders for Influence Characterization). It learns a shared sparse image--text latent space whose concepts are 1) sparse within each modality, 2) supported on contiguous image regions, 3) aligned to the phrases that describe them, and 4) approximately decorrelated. It t…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2610.14118v1 Announce Type: new Abstract: The rapid advancement of Large Language Models (LLMs) has driven their deployment toward general-purpose harness environments, where LLM-based agents operate in sandbox workspaces and interact directly with file systems and shells. While numerous post-training pipelines exist for tool-use agents, their counterparts for harness-based agents remain scarce and are predominantly one-shot and teacher-dependent: a stronger external model synthesizes tasks and collects trajectories, the target agent is trained once, and evaluation results are consumed by human researchers rather than fed back into the loop. Such pipelines are inherently bounded by the capability of the external model and the scope of human prior, leaving…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：The social media giant tries to capitalize on, monetize its successful personal agent and maybe crack the business market. Chinese models go astray, like their U.S. counterparts.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：A new case of an out-of-control AI model shows that enterprises should treat models and agents as toddlers that need supervision and explicit instructions.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Training or finetuning a multi-billion parameter foundation model requires significant compute...

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译： Release: llm-gemini 0.35 I added support to llm-gemini for the new Gemini 4 Argon model, made available today via the Gemini API and for customers of their $100/month subscription plan. Here are the low, medium and high pelicans. As expected, they are all excellent: Gemini has a track record of producing great SVGs, and are the only lab where I know their staff have invested in SVG specifically (and who have boasted about their pelicans before.) Here's the "high" illustration, which cost me 17.166 cents: The pricing is interesting. At launch, Argon is $2/million input and $10/million output - the same price as GPT-6 Sol, half the price of Claude Opus 5.5 and 1/5th the price of Claude Fable 5.1 and GPT-6 Astra. But on December 1st Argon is set to double in pric…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Alibaba’s Qwen team has introduced QwenGyre, an elastic reinforcement learning RL framework for extreme-long-horizon agent training. QwenGyre is an online reinforcement learning system from Alibaba that trains coding agents through unmodified, black-box harnesses. It targets rollouts that run for hours and process close to 1M tokens. The QwenGyre elastic RL framework was used on Qwen’s […] The post Alibaba Qwen Introduces QwenGyre: Elastic Reinforcement Learning RL for XLong-Horizon Agents 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:2610.12602v1 Announce Type: new Abstract: Continual learning (CL) seeks to adapt pretrained models while preserving prior capabilities. Yet the tasks supplied for preservation cover only part of what a model knows, while forgetting can extend beyond them. Recent work shows that language models can supplement this limited evidence by generating useful replay without access to their original training data. This raises a question about self-preservation: can a model help choose preservation targets that protect its own capabilities more broadly? To study this question, we organize task descriptions and their examples into Task Banks, whose entries can be selected and combined to guide replay generation by a frozen model. Continual instruction tuning with Qwe…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2610.12582v1 Announce Type: new Abstract: A latent recurrent language model applies a shared block of layers repeatedly, so one checkpoint can trade inference compute for accuracy by changing the loop count. At one loop, however, such models typically fall well short of a non-recurrent model of the same size, a one-loop tax that makes a separate non-recurrent model the better choice whenever latency matters. We present Elastic Latent Recurrence (ELR), a recipe for training looped language models without this tax. At one loop, an ELR model costs the same to serve as a non-recurrent model of the same size and, trained on the same data, reaches similar accuracy. Each additional loop then buys more accuracy for more inference compute. Three choices make this…