DeepSeek

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：<p><strong><a href="https://anil.recoil.org/notes/rumour-is-the-exploit">Just a rumour of a bug is enough to find a security exploit these days</a></strong></p> 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:</p> <blockquote> <p>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.</p> </blockquote> <p>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, something Anil has been able to demonstrate using his own agents, switching to DeepSeek V4 Pro⁠ when Claude Fable refused the task.</p> <p>Anil points out that this rate of discovery appears incompatible with existing open source embargo practices for new issues. If an issue can become an exploit this fast, we need to figure out new processes for keeping our communities safe.</p> <p>rclone maintainer Nick Craig-Wood <a href="https://news.ycombinator.com/item?id=49480466#49480777">confirms in the Hacker News comments</a> that his project is seeing this problem:</p> <blockquote> <p>In the first 10 years of the rclone project we received about 20 security disclosures through GitHub. We had to deal with over 40 in the last month! That has taken a huge amount of my time, even using AI tools to triage and come up with fixes for review.</p> <p>The hit rate for those security disclosures is pretty good - about 75% of them have a nugget of something which needs looking at. [...]</p> <p>GitHub assigns CVEs for the advisories. Before the AI apocalypse they took 2-3 days for an assignment but now it they are running at 3-4 weeks so I have to send the point releases out with CVE-PENDING in the changelog which isn't ideal.</p> </blockquote> <p><small></small>Via <a href="https://news.ycombinator.com/item?id=49480466">Hacker News</a></small></p> <p>Tags: <a href="https://simonwillison.net/tags/open-source">open-source</a>, <a href="https://simonwillison.net/tags/security">security</a>, <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/coding-agents">coding-agents</a>, <a href="https://simonwillison.net/tags/ocaml">ocaml</a>, <a href="https://simonwillison.net/tags/ai-security-research">ai-security-research</a></p>

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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 サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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 サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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 サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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 サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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 サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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 サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：A mini deep dive into some of the most important AI releases of last week.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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 サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：<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 サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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 サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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 サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：","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 サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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 サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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 サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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 サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：<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 サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Claude Artifact ​

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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 サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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 サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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 サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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 サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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 サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：The investment highlights the growing interconnections between AI models and robots.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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 サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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 サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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 サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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 サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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 は、レイヤー単位でモデルをロードすることで、超大規模 LLM を低 VRAM GPU でも実行できるようにするオープンソースの推論フレームワークです。最新版では 2.8T パラメータの Kimi K3 を約 3.72GB の VRAM で推論可能。同じ AutoModel API で DeepSeek-V3 や Qwen3-235B なども実行でき、4bit/8bit 圧縮による高速化にも対応しています。

サイモン・ウィリソンが2026年7月のスポンサー限定月刊ニュースレターを公開。OpenAIやAnthropicモデルによる偶発的サイバー攻撃、GPT-5.6 Sol/Terra/Luna、Claude Opus 5、Kimi K3、DeepSeek-V4-Flash-0731などの話題に加え、MCPへの関心の再燃を取り上げています。

中国のAI研究者がX（旧Twitter）に続々と参加し、技術的な議論や国際的な情報発信を行っている。Moonshot AIやDeepSeekなど主要ラボの関係者を例に、中国のSNSでは難しい技術討論の場としてのXの魅力、OpenAIやAnthropicの研究者が発信を減らすなかで注目を集める背景、そして企業ブランディングへの活用を解説する。

DeepSeek-V4-Flash-0731は、Flash価格でフロンティア級のエージェントインテリジェンスを提供し、Product Huntに登場しました。

Poolside AIが新モデルLaguna S 2.1をリリースし、低コストで優れた性能を主張。一方でAIコミュニティはセキュリティインシデントと地政学的緊張に直面。

長い思考連鎖推論モデルにおける自己注意の二次複雑性問題に対処するため、本論文ではLISA（線形インデックス付きスパース注意）を提案する。これはプラグアンドプレイで動作し、線形注意とLightning Indexerを並列に統合し、ゲート機構で融合することで推論複雑性をO(n²)からO(nM)に削減する。DeepSeek蒸留Qwenモデルでの実験では、16Kトークンコンテキストで50%の推論高速化、AIMEやMATH-500などのベンチマークで平均5.6%の性能向上を達成した。

このチュートリアルでは、NVIDIAのsrt-slurmフレームワークを探求し、srtctlを使って宣言型YAML設定を再現可能なSLURMベンチマークワークフローに変換する方法を学びます。Google Colabでプロジェクトをセットアップし、内部アーキテクチャを調べ、クラスタ設定を定義し、組み込みおよびカスタムレシピをドライラン実行し、DeepSeek-R1用の分離型プリフィル・デコードデプロイメントをモデル化します。また、パラメータスイープを生成し、型付きPython APIと対話し、拡張設定を検証し、スループット対レイテンシのパレートフロンティアを通じてシミュレートされたベンチマーク結果を分析します。

Anthropicが米国政府との協議後にClaude Fable 5を再展開し、新たなサイバーセキュリティ分類器を追加。Claude Sonnet 5の低価格版も発表。GoogleのNotebookLMがTikTok風動画要約機能を追加、Nano Banana 2 Lite画像生成器もリリース。Etchedへの大規模投資、百度AIチップ部門のIPO計画、Agility RoboticsのSPAC上場、DeepSeekの拡大採用、中国のLongcat 2.0 MoEモデルと長期エージェントベンチマークなど。

2025年1月、DeepSeekはその大規模推論モデルR1を用いて約80万の完全な解答プロセス（長い思考連鎖、誤った開始、自己訂正などを含む）を生成し、フィルタリング後にQwenやLlamaなどの小型オープンモデルに対して単純な教師ありファインチューニングを行い、強化学習なしで小モデルがそのサイズを超えた推論能力を示すことを発見した。これは、シーケンスレベルの模倣が推論蒸留に適さないという従来の見解に挑戦するものである。

本研究は、科学的推論の妥当性に統計的保証を提供するグラフ構造のコンフォーマル予測フレームワーク「Scientific Feasibility Control (SFC)」を提案する。SFCは科学的推論を原子的な事実単位に分解し、科学的違反が検出された際に動的分岐を用いて修正する。PhyXベンチマークで50.1%の精度を達成し、DeepSeek-R1やGPT-4を上回り、科学法則違反を73%削減、α=0.10で91.7%の妥当性保証を提供する。

新しい研究論文が、マルチエージェントLLMシステムの計画フェーズを標的とした4種類のプロンプトインジェクション攻撃フレームワーク「PlanFlip」を紹介。GPT-5のような強力なモデルほど脆弱であり、同質のバックボーンは相関エージェントの盲点を生み出す一方、DeepSeek-R1のような推論強化モデルは攻撃に耐性を示す。提案された2つの防御手法は最大1.00の検出率を達成。

24GB GPUは本格的なローカル推論の実用的な最低ラインです。本ガイドでは、Q4_K_Mで1枚のカードに収まる6つのオープンウェイトモデル（Qwen3.6、Gemma 4、Mistral Small、gpt-oss-20b、DeepSeek-R1-Distill）を比較し、VRAM消費、ライセンス、各モデルの得意分野を解説します。

中国の3つの研究所による旗艦オープンウェイトMoEモデル—Kimi K3、DeepSeek V4 Pro、GLM-5.2—は、ベンチマーク、ライセンス、サービスコストでそれぞれ優れています。Kimi K3は性能でリードしますがAPI限定、DeepSeek V4 Proは最も安価で完全オープン、GLM-5.2は速度と展開性のバランスが取れています。

本記事では、複数の推論努力モードを持つモデルの開発方法を探り、o1やDeepSeek-R1からGPT-5.6への進化、RLVRトレーニング、推論スケーリング、思考トークン、推論モード切り替えなどの主要技術を解説する。

インド企業は、DeepSeek、Alibaba、Moonshot AIが開発した中国製大規模言語モデル（LLM）を活用して人工知能関連支出を抑制しており、長年にわたる対立にもかかわらず、先端技術における中国への依存を拡大している。

本論文では、予測駆動のオンラインエキスパート配置によりエンドツーエンドレイテンシを最小化する新しい分散MoEサービングシステムDirectorを提案する。軽量カスケード予測器または低ビット量子化レプリカを用いてエキスパート活性化パターンを予測し、ほぼゼロダウンタイムのマイグレーションモジュールと、多項式時間で(1+ε)近似比を達成する緩和ベースの最適化器を備える。実験では、Mistral、DeepSeek、Qwenなどの人気MoEモデルにおいて、既存手法と比較して11〜55%のレイテンシ削減を実証した。

著者がコーディングと監査の経験に基づき、2026年中期の主要AIモデルを非公式にランク付け。Anthropic Fable、OpenAI Sol、Mistral、Gemini、DeepSeekを対象とし、米国の輸出規制や欧州の視点も含む。

DeepSeek V3.2がHugging Bayで利用可能になりました。Hugging Bayは、出所、ライセンス検証、信頼できるホスティングを提供するオープンソースAIアーティファクトレジストリです。

DeepSeekは、投機的デコードにおけるドラフト品質の低さと検証の無駄を同時に解決する半自己回帰ドラフトモデル「DSpark」を発表。DeepSeek-V4でユーザーあたりの生成速度を60～85%向上させ、品質は維持。本記事では、その仕組み、オープンソースのDeepSpecツールキット、実験結果を解説。

研究によると、推論能力を持つ大規模言語モデルは、論理的に一貫性のないプロンプトによって「考えすぎ」状態に陥り、出力長が急増し、サービス拒否攻撃に悪用される可能性があります。浙江大学とアリババの研究者は、進化的アルゴリズムを使用して悪意のあるプロンプトを生成し、DeepSeek-R1、Qwen3-Thinking、GPT-o3、Gemini 2.5 Flashといった主要な推論モデルで出力長を最大26倍に増加させました。

中国製のAIモデルが、米国の有力な競合との性能差を縮めつつ、大幅に低コストで利用できることから、米国企業の間で採用が進んでいる。DeepSeekやZ.aiなどの中国企業による最近のモデルリリースは、AnthropicやOpenAIなどの最先端システムと非常に競争力があると見なされている。これらの能力向上は、多くの米国AI研究所で最先端モデルのトークン価格が上昇し、企業がテクノロジー利用に関連する予想外の高コストに直面している時期に起きている。