DeepSeek

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：Artificial Analysis DeepSeek • DeepSeek V4 Flash 0731 • Proprietary model • Released August 2026 DeepSeek V4 Flash Vision (Reasoning, Max Effort) Intelligence, Performance & Price Analysis API Provider Benchmarks Model…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：DeepSeek today debuted a new addition to its flagship V4 series of large language models. On launch, V4 Flash Vision Exp is only available via the Chinese startup’s paid developer platform. The company may release a free version later on given that it has open-sourced many of its earlier models. Those models include V4 Flash, […] The post DeepSeek debuts multimodal language model competitive with Opus 4.8 appeared first on SiliconANGLE.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：We burned 11.7bn tokens to find the best cyber AI model GLM5.3 and DeepSeek are now frontier-tier models Debarshi Philippe Dourassov Published on: Aug 21, 2026 We burned 11.7 billion tokens to benchmark the cyber capabi…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Post Log inSign up Post DeepSeek on X: "DeepSeek-V4-Flash-Vision-Exp is now live on the DeepSeek API Platform! 🚀 🔹 This experimental multimodal model matches DeepSeek-V4-Flash on text capabilities—including agents, re…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.18078v1 Announce Type: new Abstract: This position paper argues that AI agents with chain-of-thought reasoning capabilities are predisposed to exhibit collusive behavior and should be required to obtain behavioral certification before making decisions that affect economic markets. This is because integrating these agents into society could collapse the legal evidentiary distinction between competition and collusion among independent firms without eroding the economic harm distinction. Experiments with DeepSeek-R1 agents in the Bertrand oligopoly pricing domain reveal a tendency towards tacit collusion that persists even when humans prompt the agents not to collude. We further show that the chain-of-thought of these agents can be steered toward either extremely collusive or highly competitive behavior in a way that is not semantically detectable by another LLM analyzing the reasoning traces. As a result, deploying reasoning agents for market decisions leads to collusive economic outcomes without any evidence of conspiracy or intent. Thus, certification based on observed behavior in representative situations is necessary to prevent collusion. We provide preliminary evidence that such agents can be steered in a generalizable way toward efficient competitive equilibria. However, developing a comprehensive behavioral certification will be required before these models can be deployed in real-world markets while ensuring their stability and efficiency.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：A mini deep dive into some of the most important AI releases of last week.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：We ran 904 DeepSWE rollouts on DeepSeek V4 Pro 0813 and GPT-5.6 Sol. Sol leads pass@1 by 10 points at 35x the cost; Pro wins pass@4, and a Pro-first cascade hits 83.0%.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：<p><strong><a href="https://artificialanalysis.ai/models/qwen3-8-27b">Qwen 3.8 27B scores 52 on the Artificial Analysis Intelligence Index</a></strong></p> That's the same score as GPT-5.6 Luna (max), and just one point behind GLM-5.2 (max) and DeepSeek V4 Pro 0813 (max) - that GLM is 753B and that DeepSeek is 1.6B parameters, and Luna is size unknown but presumably a whole lot bigger than 27B.</p> <p>Qwen 3.8 27B is <a href="https://simonwillison.net/2026/Aug/16/qwen-38-27b/">a truly astonishing model</a>. <p><small></small>Via <a href="https://news.ycombinator.com/item?id=49334544">Hacker News</a></small></p> <p>Tags: <a href="https://simonwillison.net/tags/ai">ai</a>, <a href="https://simonwillison.net/tags/generative-ai">generative-ai</a>, <a href="https://simonwillison.net/tags/llms">llms</a>, <a href="https://simonwillison.net/tags/qwen">qwen</a>, <a href="https://simonwillison.net/tags/ai-in-china">ai-in-china</a>, <a href="https://simonwillison.net/tags/artificial-analysis">artificial-analysis</a></p>

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：We ran 904 DeepSWE rollouts on DeepSeek V4 Pro 0813 and Claude Fable 5. Fable leads pass@1 at 90x the cost; Pro wins pass@4, and a Pro-first cascade hits 82.7%.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：DeepSeek (@deepseek_ai): "API pricing update 💰 With the V4 lineup release, we’re updating our API pricing and introducing peak and off-peak rates. Off-peak rates are 50% lower than peak, enabling more flexible workload…

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：About a month ago, Baidu (often called the “Google of China”) introduced Unlimited-OCR, an advancement over DeepSeek OCR. The model was designed to transcribe long, multi-page documents with high accuracy while delivering fast and stable inference. Unlike conventional vision-language OCR systems, Unlimited-OCR addresses a major bottleneck in long-document transcription: the rapidly growing Key-Value (KV) cache, […] The post How Baidu Unlimited-OCR Works: Solving Long-Document Transcription appeared first on Analytics Vidhya.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Artificial Analysis DeepSeek • Open weights model • Released August 2026 DeepSeek V4 Pro 0813 (Reasoning, Max Effort) Intelligence, Performance & Price Analysis API Provider Benchmarks Model summary Intelligence 53 Arti…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.11215v1 Announce Type: new Abstract: Simulating societies of many large language model (LLM) agents is expensive, yet the questions asked of such simulations are usually macroscopic: phase behaviour, stylised facts, and scaling with the number of agents $N$, not the cognition of any single agent. We turn a statistical-physics observation into a method: replace each LLM agent by a low-parameter model fitted from a few hundred to a few thousand cheap queries, then run the society at any $N$ on a laptop. Whether this works is decided before the simulation runs, chiefly by what each agent perceives. We introduce an [interaction order x memory] taxonomy that maps perception and memory to an effective theory and a predicted $N$-trend of the surrogate error. We validate it on a faithful reimplementation of the LLM macroeconomy EconAgent and seven further named LLM simulations, with agent decisions cloned from genuine LLM elicitations (primarily DeepSeek) for a few dollars; the predicted error trends hold cell by cell, and the two refuted predictions, both on a strongly saturating response and traced to its curvature, are themselves matched quantitatively by the theory with no free parameters.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：<p><strong><a href="https://openrouter.ai/deepseek/deepseek-v4-pro-0813">DeepSeek V4 Pro 0813 (on OpenRouter)</a></strong></p> The latest DeepSeek Pro model is now available, via API only. I had to link to OpenRouter because DeepSeek don't have any obvious announcement page for their new model.</p> <p>I haven't been able to confirm if they plan to release the open weights, but given the weights are available for both April's <a href="https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro">deepseek-ai/DeepSeek-V4-Pro</a> and July's <a href="https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731">deepseek-ai/DeepSeek-V4-Flash-0731</a> it seems likely.</p> <p>Interestingly I got <a href="https://tools.simonwillison.net/markdown-svg-renderer#url=https%3A%2F%2Fgist.github.com%2Fsimonw%2Fc1108a380593547c2def5863bca63160"><em>very</em> different looking pelicans</a> for the three different reasoning levels of low, medium, and high. I've not noticed this kind of difference from any other model:</p> <p>Low:</p> <p><img alt="Flat vector illustration of a white pelican with a large orange beak, wearing a straw hat with an orange band, riding a teal road bicycle in profile, set against a pale cream circle with a dashed outline and small motion marks trailing behind." src="https://static.simonwillison.net/static/2026/deepseek-pro-low.png" /></p> <p>Medium:</p> <p><img alt="A similar cartoon pelican cycling, drawn in a looser outlined style: the bird's body is mostly white line art, its orange beak pouch hangs open under a yellow cap, a long red tongue streams backwards towards a yellow sun, and a small blue fish sits on a tray by the handlebars of a green bicycle whose wheels are drawn as broken yellow arcs." src="https://static.simonwillison.net/static/2026/deepseek-pro-medium.png" /></p> <p>High:</p> <p><img alt="The pelican again, this time on a red bicycle against a pale blue background, with a bright yellow beak and pouch, a purple pennant flag on the back, a wicker front basket holding a small fish, and black musical notes floating in the top right corner." src="https://static.simonwillison.net/static/2026/deepseek-pro-high.png" /></p> <p>In terms of benchmarks... as far as I can tell those were released to the Official DeepSeek WeChat Group, then copied and pasted into <a href="https://www.reddit.com/r/LocalLLaMA/comments/1vmi0fg/removed_by_moderator/">a post on Reddit</a> which was deleted by the moderators for being "low-effort", then copied into <a href="https://news.ycombinator.com/item?id=49274600#49275180">this ASCII-art table on Hacker News</a>. <p>Tags: <a href="https://simonwillison.net/tags/ai">ai</a>, <a href="https://simonwillison.net/tags/generative-ai">generative-ai</a>, <a href="https://simonwillison.net/tags/llms">llms</a>, <a href="https://simonwillison.net/tags/pelican-riding-a-bicycle">pelican-riding-a-bicycle</a>, <a href="https://simonwillison.net/tags/deepseek">deepseek</a>, <a href="https://simonwillison.net/tags/llm-release">llm-release</a>, <a href="https://simonwillison.net/tags/ai-in-china">ai-in-china</a></p>

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Claude Artifact ​

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.10090v1 Announce Type: new Abstract: Large language models (LLMs) have advanced code generation, where executable feedback provides a more reliable learning signal than textual imitation alone. Hardware verification is an important application of code generation and accounts for a substantial fraction of modern chip design effort, with high-coverage testbench stimulus generation as a key task. We present CHORUS, a post-training framework that pushes performance beyond what a conventional supervised fine-tuning (SFT)-to-reinforcement learning (RL) pipeline achieves. CHORUS builds on two observations. First, staged SFT produces behaviorally diverse checkpoints, and dense-reward RL turns them into strong experts with comparable aggregate performance but distinct task-level strengths. Second, these complementary strengths can be exploited through either training-free model merging or further post-training to outperform the best individual expert. By consolidating the resulting specialists into a single 4B model, CHORUS achieves 88.0% Pass@1 on CVDP-ECov, outperforming DeepSeek-R1 (671B) by 13.5 percentage points.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.06474v1 Announce Type: new Abstract: Large language models increasingly generate complete websites from natural-language descriptions, and reinforcement learning has become a central approach to closing their remaining functional gap. This training regime is bottlenecked by reward design. Hand-authored browser scripts are executable yet costly to write for open-ended requirements, while VLM and GUI-agent graders scale but may issue verdicts before observing the decisive state. We propose WebGrader, a self-evolving programmatic grader that autonomously derives the required interaction flows from each website request, represents each flow as an executable Flow Contract, and uses its execution outcome as an RL reward. WebGrader materializes the generated project in a live browser, grounds target actions against the source code and live DOM, and collects visual, DOM, response, and persistent-state evidence along the same browser trajectory. A residual-driven offline loop then discovers reusable verifier skills, screens them on disjoint validation pages, and freezes the promoted skill graph before policy training. By separating test planning, action grounding, evidence collection, and semantic judgment, WebGrader issues a Pass verdict only after observing the requested transition. On WebGen-Bench, WebGrader trains an 8B policy to a 52.01% functional success rate, outperforming a matched appearance-plus-script reward by 7.88 points and surpassing o4-mini and DeepSeek-v4-flash. On WG-core-250, the policy reaches a Full Score of 44.953 and surpasses Qwen3-Coder-480B.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Ante Terminal Bench 2.1 Results | Coding Agent Benchmark Skip to main content We just open sourced a tiny GPT-style cognitive core built in pure Rust.See our repository→ Terminal-Bench 2.1 One harness to unlock the pote…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：AI Nexus Daily - Your Daily Digest of Artificial Intelligence Loading articles... 📬 Daily AI Brief 20+ sources — free Support Free → AI Nexus Daily 200 articles AllAI NewsAI ToolsResearchTutorialsFor DevsTechNewsletter…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Responding to an article by China’s ambassador to the UK, Prof Paul H Cleverley advocates shared openness standards, while Dr Claire Jenkins says British AI can offer a distinctive path Ambassador Zheng Zeguang rightly celebrates openly released AI models, and the Chinese labs behind Qwen, DeepSeek and Kimi have led the way – competition that benefits everyone, especially where models can run on modest hardware in the developing world (The future of AI hinges on openness and cooperation. China and Britain can gain much by working together, 30 July). But his claim that openness is a defining feature of China’s AI development deserves scrutiny. Take GeoGPT, the geoscience system from Zhejiang Lab showcased at last month’s World AI Conference as a model of jointly governed open science. It is promoted to countries as open, yet under the model openness framework – endorsed in a recent UN report – it would not qualify as open at all. It releases model weights (built mainly on Alibaba Qwen, whose licences are not Open Systems Interconnection-compliant), no training data or application source code is released, and its governance committee answers to Zhejiang Lab itself. 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：The investment highlights the growing interconnections between AI models and robots.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：FelonyBench The leading benchmark for AI in cybersecurity. CompanyFelonies Anthropic9 OpenAI5 Meta1 DeepSeek0 Google DeepMind0 Moonshot AI0 xAI0 Leaderboard RankCompanyCountFelonies 1 AnthropicClaude evaluations 9 1× Ma…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：We ran 900 DeepSWE rollouts on DeepSeek-V4 Flash and GPT-5.6 Luna. Luna leads pass@1 by 14 points; DeepSeek delivers 4.8x the solves per dollar.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：DeepSeek’s Official V4 Flash Censors More Than Its Preview, Selectively August 5, 2026 DeepSeek’s Official V4 Flash Censors More Than Its Preview, Selectively Introduction On July 31, DeepSeek released V4-Flash-0731, th…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.00004v1 Announce Type: new Abstract: Grading natural-language mathematical proofs is a recurring cost in evaluating math-reasoning systems, and frontier LLM judges are expensive. We ask whether cheap open-weight models can serve as reliable judges given a candidate proof, a ground-truth proof, and a human-grading rubric. On a 200-instance validation sample of IMO-GradingBench, three cheap judges (GPT-OSS 120B, DeepSeek-V4 Flash, Gemma-4 31B) agree with human pass/fail decisions at rates statistically indistinguishable from Claude Opus 4.7 and Gemini 3.1 Pro, at up to $100\times$ lower cost. We had expected a majority vote of the three to be the best budget option; it matched the frontier but did not improve on its strongest member. Extending to the full 1000-instance benchmark and exploring consensus rules, we found that requiring unanimous agreement (all-three-pass) reaches the highest pass-agreement and precision and, on four replicate runs, the smallest run-to-run spread. The headline finding is that cheap judges are competitive with the frontier at one to two orders of magnitude lower cost; as a deployable default we recommend all-three-pass, with the caveat that this rule was identified post-hoc and warrants independent replication.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：For five days an autonomous AI agent worked to break into our lab. We became the first to identify the exact model behind a live attack, deepseek-v4-flash-free, from inside the attack itself. Then we did something no on…

AirLLM 是一个开源推理框架，通过逐层加载权重让超大模型也能在低显存 GPU 上运行。最新版本支持 2.8T 参数的 Kimi K3，在约 3.72GB 显存下完成推理；同一套 AutoModel API 也可运行 DeepSeek-V3（671B）、Qwen3-235B 等模型，并支持 4bit/8bit 压缩加速。

西蒙·威利森发布了2026年7月的赞助人专属月度通讯，涵盖OpenAI和Anthropic模型的意外网络攻击、GPT-5.6 Sol/Terra/Luna、Claude Opus 5、Kimi K3和DeepSeek-V4-Flash-0731等新模型，以及作者对MCP兴趣的重燃等内容。

DeepSeek-V4-Flash-0731 以“Flash”价格提供前沿级智能体能力，现已在 Product Hunt 上线并开放讨论。

Poolside AI 发布新模型 Laguna S 2.1，号称以更低成本超越同类产品，同时 AI 社区关注安全事件和地缘政治紧张局势。

针对长链思维推理模型在测试时缩放中面临的自注意力二次复杂度问题，本文提出LISA（线性索引稀疏注意力），一种即插即用的注意力替换模块，无需从头预训练。LISA并行集成线性注意力和闪电索引器，通过门控机制融合，将推理复杂度从O(n²)降至O(nM)。在DeepSeek蒸馏Qwen模型上的实验表明，在16K上下文下实现50%推理加速，并在AIME和MATH-500等基准上平均提升5.6%的性能。

本教程探讨了 NVIDIA 的 srt-slurm 框架，学习如何使用 srtctl 将声明式 YAML 配置转换为可重复的 SLURM 基准测试工作流，用于分布式 LLM 服务。在 Google Colab 中设置项目，检查内部架构，定义集群配置，试运行内置和自定义配方，并为 DeepSeek-R1 建模分离的预填充和解码部署。还生成参数扫描，与类型化 Python API 交互，验证扩展配置，并通过吞吐量与延迟的帕累托前沿分析模拟的基准测试结果。

Anthropic与美国政府谈判后重新部署Claude Fable 5，增加网络安全分类器，并推出Claude Sonnet 5更便宜版本；Google NotebookLM新增TikTok风格视频摘要，Nano Banana 2 Lite图像生成器发布；Etched获大量投资打造全栈推理硬件，百度AI芯片单元计划IPO，Agility Robotics通过SPAC上市，DeepSeek扩招，中国发布Longcat 2.0 MoE模型及长周期智能体基准测试。

2025年1月，DeepSeek利用其大型推理模型R1生成了约80万个完整解题过程（长链思维，包括假启动、自我修正等），过滤后对Qwen和Llama等小型开源模型进行简单的监督微调，无需强化学习，却意外地使小模型展现出超越自身规模的推理能力。这挑战了此前认为序列级模仿不适用于推理蒸馏的观点。

本研究提出科学可行性控制（SFC）框架，一种图结构共形预测方法，为科学推理的有效性提供统计保证。SFC将科学推理分解为原子单元，通过渐进式绝对一致事实性验证，在检测到违反科学原则时动态分支到替代生成路径。实验表明，SFC在PhyX等多模态科学推理基准上达到50.1%的准确率，超过DeepSeek-R1和GPT-4，同时将科学定律违反减少73%，并提供91.7%的科学有效性保证。

一项新研究提出PlanFlip框架，包含四种针对多智能体LLM系统规划阶段的提示注入攻击。研究发现，更强的模型（如GPT-5）反而更易受攻击，同质化骨干网络存在相关智能体盲点，而推理增强型模型（如DeepSeek-R1）能抵御攻击。提出的两种防御方法检测率高达1.00。

本文对比了六款适合单张24GB GPU（如RTX 3090/4090）的开放权重模型，涵盖Qwen3.6、Gemma 4、Mistral Small等，并解释了内存分配、量化策略以及各模型的优势场景。

中国三家实验室的旗舰开源MoE模型——Kimi K3、DeepSeek V4 Pro和GLM-5.2——在基准测试、许可条款和服务成本上各有优劣。Kimi K3性能最强但仅限API，DeepSeek V4 Pro成本最低且立即开源，GLM-5.2平衡了速度与可部署性。

本文探讨了如何开发具有多种推理努力模式的模型，涵盖从o1和DeepSeek-R1到GPT-5.6的推理模型演变，以及RLVR训练、推理缩放、思考标记和推理模式切换等关键技术。

印度企业越来越多地使用DeepSeek、阿里巴巴和Moonshot AI等中国大语言模型来降低人工智能成本，这进一步加深了印度对中国尖端技术的依赖，尽管两国之间长期存在冲突。

本文介绍了Director，一种新的分布式MoE推理系统，通过预测驱动的在线专家放置优化，显著降低端到端延迟。系统采用轻量级级联预测器或低比特量化副本预测专家激活模式，结合近乎零停机的在线迁移模块，以及基于松弛优化的专家放置算法，在多项式时间内达到(1+ε)近似比。实验表明，在Mistral、DeepSeek和Qwen等流行MoE模型上，相比现有工作延迟降低11%~55%。

作者从个人编码和审计经验出发，对2026年中的主流AI模型进行非正式分级，涵盖Anthropic Fable、OpenAI Sol、Mistral、Gemini和DeepSeek等模型，并融入美国出口管制和欧洲视角的评论。

DeepSeek V3.2 现已登陆 Hugging Bay，这是一个开源 AI 工件注册平台，提供来源验证、许可证审核和可信托管服务。

DeepSeek发布了DSpark模块，通过半自回归草案模型结合马尔可夫头，同时解决了推测解码中草案质量低和验证浪费两大问题。在DeepSeek-V4上，它使每用户生成速度提升60-85%，且不降低模型质量。本文深入解析其工作原理、开源工具DeepSpec的使用方法及实验结果。

研究表明，具备推理能力的大语言模型容易因逻辑不一致的提示而陷入“过度思考”，导致输出长度激增，可能被利用发动拒绝服务攻击。浙江大学与阿里巴巴的研究人员开发了一种进化算法，能够生成恶意提示，使模型输出长度最高增加26倍，影响包括DeepSeek-R1、Qwen3-Thinking、GPT-o3和Gemini 2.5 Flash在内的主流推理模型。

中国开发的AI模型正逐渐缩小与领先美国竞争对手的性能差距，同时保持显著的价格优势，因此在美国公司中越来越受欢迎。最近DeepSeek和Z.ai等中国公司发布的模型被认为与Anthropic和OpenAI等前沿系统高度竞争。这些进步正值许多美国AI实验室最先进模型的token价格上涨，使企业面临与使用该技术相关的意外高成本。

DeepSeek V4 模型自2026年4月发布以来，在OpenRouter上的代币份额从年初的9%翻倍至18%，主要由代理型工作负载驱动。其成本效益比（每百万代币输入0.09美元，输出0.18美元）领先业界，吸引各类用户采用，并推动中国模型整体超越美国模型。

美国开发者和小型企业正在转向中国AI模型以降低成本。尽管性能仍落后于美国顶尖模型，但中国模型能以极低价格处理大多数任务。微软也在考虑使用DeepSeek等开源模型作为更低成本的替代方案。然而，中国公司面临将流行度转化为可观收入的挑战。