搜索“DeepSeek”

(with Python), we report the average of 16 runs (avg@16). For IMO-AnswerBench, we report the average of 8 runs (avg@8); Baselines: a. GPT-5, Claude-4.5-sonnet, Grok-4 results and DeepSeek-V3.2 results are quoted from the GPT-5 post, GPT-5 for Developers post, GPT-5 system card, claude-sonnet-4-5, grok-4, deepseek-v3.2, the public Terminal-Bench leaderboard (Terminus-2), the public Vals AI leaderboard and the artificialanalysis. Benchmarks for which no available public scores were re-tested unde

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the most recent round of tool-related messages is retained; For BrowseComp, we report scores obtained with context management using the same discard-all strategy as Kimi K2.5 and DeepSeek-V3.2; For DeepSearchQA, no context management was applied to Kimi K2.6 tests, and tasks exceeding the supported context length were directly counted as failed. Scores for Claude Opus 4.6, GPT-5.4, and Gemini 3.1 Pro on DeepSearchQA are cited from the Claude Opus 4.7 System Card; For WideSearch, we report resul

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r Verifier (KVV) to choose high-accuracy services. Details: https://www.kimi.com/blog/kimi-vendor-verifier Footnotes 1. General Testing Details We report results for Kimi K2.5 and DeepSeek-V3.2 with thinking mode enabled, Claude Opus 4.5 with extended thinking mode, GPT-5.2 with xhigh reasoning effort, and Gemini 3 Pro with a high thinking level. For vision benchmarks, we additionally report results for Qwen3-VL-235B-A22B-Thinking; Unless otherwise specified, all Kimi K2.5 experiments were condu

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optimizer substantially outperforms the widely-used AdamW optimizer for LLM training. Kimi K2 was designed to further scale up Moonlight, which employs an architecture similar to DeepSeek-V3. Based on scaling-law analysis, we reduce the number of heads for long-context efficiency, and increase MoE sparsity for greater token efficiency. While scaling up, we encountered a persistent challenge: training instability caused by exploding attention logits, an issue that occurs more frequently with Muo

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另一种危害。当Fable 5首次发布时，系统被编码为对AI研究问题——那种可能帮助竞争对手开发自己AI的问题——提供帮助性较差的答案，却不告诉用户它正在这样做。在一片抗议声之后，Anthropic撤回了这一功能，但损害已经造成。现在我们知道：模型可以秘密地不服从。 在审查领域，这尤其令人担忧。去年，CrowdStrike的研究人员发现，当他们要求中国AI模型DeepSeek R1为西藏一家虚构银行和一个名为“维吾尔人无枷锁”的社交网络应用编写代码时，它生成的代码比在不指明这些任务为谁而做的情况下执行同样任务时漏洞更多。令人难以置信的是，CrowdStrike怀疑这并不是一种设计好的行为。那里的研究人员推测，这是他们所称的“涌现性失准”的一个案例，源自模型的训练数据：一种甚至没有人要求过的不服从。 涌现性拒绝在西方模型中也已被观察到。去年冬天，英国AI安全研究所发现，Anthropic的模型有时拒绝协助某些与AI安全研究相关的任务。这些模型从未被刻意训练去拒绝此类请求。然而，其中三个模型拒绝了Anthropic所称的“一组合理的AI安全研究任务”中超过一半的任务。Anthropic在后续

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’s release, however, illustrates the spectrum Farkas describes: “open” AI releases can reveal vastly different amounts of what went into building a model. Farkas points to China’s DeepSeek as an example of the confusion, with its models widely described as open source despite the company releasing model weights rather than everything required to reproduce the models from scratch. However, he stresses that the ability to download and run those models remains valuable in its own right. “Are open w

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ated to model provenance and compliance. China's open-model lead isn't permanent Chinese companies have established a leading position in open-weight AI, with models from Alibaba, DeepSeek, Moonshot and Z.ai gaining traction for their capabilities and relatively low operating costs. Counterpoint Research has identified Chinese models, including Kimi and GLM, among the top-performing open-weight systems in the market. Prince Kohli, president and CEO of Sauce Labs, a cloud-based software testing p

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,024 response tokens): training converges as expected, with reward rising from about 45% to 95% over 50 rollouts (Figure 3), which matches the GB300 reward growth curve perfectly; DeepSeek-V4-Flash (4-layer, FP8) on GSM8K (32 prompts × 8 samples per step, up to 256 response tokens): the FP8 path runs end-to-end for both rollout and training Figure 3. GSM8K training reward over 50 rollouts, Qwen3-30B-A3B (256 prompts × 8 samples per rollout, up to 1,024 response tokens), GB300 vs Vera Rubin. Thes

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rted): A CrowdStrike report, as summarized by @AndrewCurran , attributes last week’s attack on South Korean banks possibly to a single person. The stack reportedly combined ARTEX, DeepSeek v4.1-Flash, GLM-5.3, Grok 4.6 and Claude Code (report).Call for traces: Clem Delangue is asking for public traces of agentic attack and defense (call); Open RL environments:TermGrade: 1k execution-verified terminal environments plus 36k trajectories. Training Gemma-4-31B on the tasks it solved half the time ad

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性。 一张700W的AI加速卡，一年不停转，要吃掉约6100度电。 在北京，工业电价大约是每度0.80元；在内蒙古乌兰察布，数据中心到户电价实际在0.32—0.36元之间，本文取0.358元作为保守估算。 差价是0.442元。折算下来，单张卡一年省约2700元；一万张卡组成的集群，一年就是2700万——这还只是电费一项。 所以过去三年，阿里、华为、字节跳动、DeepSeek、Z.ai、世纪互联、ZData……一长串名字扎堆涌向了这座以马铃薯和酸奶闻名的内蒙古小城。目前乌兰察布已建成或规划的数据中心达89座，已确认算力容量15GW，被业内称为"亚洲增速最快的数据中心枢纽"。 几乎所有报道给出的解释都一样：因为这里有风、有煤、有冷气候、有便宜的地。 但这个解释对吗？ 一、为什么跑出来的是乌兰察布，不是甘肃、宁夏？ 甘肃有风电，宁夏有风光，青海有光伏——如果"有风有煤有冷"就够了，为什么跑在最前面的是乌兰察布，而不是资源禀赋同样出色的甘肃或宁夏？ 答案藏在一个很少被算力报道提及的细节里：内蒙古西部由独立运营的蒙西电网覆盖，与国内大多数地区由国家电网供电不同。 通过直接向数据中心设施供电，开发

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promoting AI innovation and development as the first priority.” The comprehensive AI Law promised in the 2023 and 2024 legislative plans was shelved in 2025, four months after the DeepSeek moment. The State Council’s AI+ Action Plan targets 70% penetration of agents and intelligent terminals by 2027 and 90% by 2030, aiming for a rapid deployment of AI across all sectors and industries. We think the Chinese government takes an open position on frontier models but tightens the applications and reg

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sk figures await tiered-pricing support Context Haiku 4.5 has been Anthropic’s small model since October 2025. Since then, OpenAI’s GPT-6 Luna, Gemini 3.8 Flash, GLM-5.3 Flash and DeepSeek V4.1 Flash have competed at the low-cost end; Haiku 5.5 matches Luna’s sticker price exactly. It adds effort control and a 1M context; It is explicitly positioned as the cheap worker inside multi-model agent harnesses: Claude Code subagents, Devin Fusion, Copilot subagents, and compaction or summarization step

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urn to grade them. Past calls include NVIDIA failing to acquire Arm, transformers achieving leading results beyond language, and an open-source model surpassing OpenAI’s o1, which DeepSeek-R1 subsequently did. Let’s dive into the key stories This year, agents are doing valuable work in software and science, physical AI is learning how to act in the world, and access and control are becoming more consequential. Below, I share the developments that stood out to me and what I think they mean for wh

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sets into actionable insights. Michal SutterReka Releases Rho-1: A 19B Omni-Reasoning Model That Understands, Generates Video and Outputs Robot Actions in One; Michal SutterCan an Open Model Do Security Research? Cantina’s apex-flash-1 Solves 40 of 60 Held-Out Bug Tasks; Michal SutterGoogle Research Moves Federated Learning Into TEEs: Gboard Now Trains With Externally Verifiable Differential Privacy; Michal SutterDeepSeek Harness v0.2 Brings Official Desktop Apps to Its Open-Source Agent Harness

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去十年输出技术、代码、产品，甚至派驻近600名工程师手把手培育起来的。基于此，上海具备深厚的技术储备，深度伪造检测率高达99%，高风险交易识别准确率超过95%。 为进一步保障智能体支付网络安全，蚂蚁国际还为AMP协议框架内提供100%赔付的兜底保障。这也是全球首个针对智能体支付损失设计的“保险”。 目前，上海已成为许多国产AI出海的首选地。据介绍，深度求索（DeepSeek）、月之暗面（Kimi）等头部AI企业的海外收单、风控与反欺诈由蚂蚁国际承接，这使得按词元向全球收费这门新生意，拥有了适配的订阅式支付与全球账户服务，一次接入即可覆盖200多个国家和地区。 反向来看，美国ChatGPT和Gemini等海外AI进入中国和东南亚市场，也可通过这套网络完成“一次接入”。 “上海场景”力争金标准 国际咨询机构麦肯锡预计，到2030年，AI智能体将处理3万亿至5万亿美元的交易。面对万亿美元级别的市场，企业端将率先迎来第一波规模化爆发。 这一判断也得到多家研究机构的印证。英国朱尼普研究公司（Juniper）预测，B2B智能体支付未来五年复合增速将达335%，远高于消费端市场增速。美国高德纳咨询公

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iteracy instruction. The system listens to children read aloud and provides tailored prompts on skills, including phonics, decoding, fluency, vocabulary and comprehension. Related:DeepSeek and what enterprises should learn from AI agent exploits But as AI takes on more instructional roles in K-8 classrooms, districts need evidence that it actually improves student learning. The shift also highlights a bigger issue for educational technology leaders: How schools should evaluate and oversee AI sys

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a. October 7, 2026 Reflection AI’s new open-weight models highlight the growing ambition among U.S. AI companies to compete with and outperform Chinese AI vendors such as Alibaba, DeepSeek, Moonshot and Z.ai. It also shows how the AI market is becoming more divided. The Nvidia-backed vendor unveiled its new open weight model, Beam, on Monday. It is a mixture-of-experts model with 501 billion parameters, aimed at coding, reasoning and agentic workloads. The model was trained on 23.8 trillion toke

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ttack prep, Mistral doesn't explain. In Lakera's B3 AI Security Benchmark, ML4 blocks 93.3 percent of attacks, according to the company. ML4 takes second place in coding, ahead of Deepseek and Qwen In programming, ML4 scores 49.8 percent in the Artificial Analysis Coding Agent Index, putting it ahead of Deepseek V4 Pro and Qwen3.8 Max . Mistral also ran a blind evaluation with Surge AI, where professional annotators rated code quality without knowing which model produced it. ML4 landed second ou

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complex terminal workflows, scoring 61.7% on DeepSWE v1.1, 59.4% on SWE-Atlas-QnA, and 28.3% on Terminal-Bench 4. Its combined Coding Agent Index score of 49.8% places it ahead of DeepSeek V4 Pro 0813 and Qwen3.8 Max. DeepSWE 1.1; Terminal Bench 4.0; SWE Atlas QnA Scores on DeepSWE, Terminal-Bench 4, and SWE Atlas QnA use the numbers reported by the ArtificialAnalysis coding index. Vibe Code Bench v1.1 uses the numbers reported by vals.ai. We also ran a blind human evaluation with Surge AI on co

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ailable model. Instead of chasing peak performance, Beam aims to deliver strong results with minimal compute, putting it in direct competition with Chinese open-weight models from Deepseek and Qwen. Reflection built Beam for coding, logical reasoning, and agentic tasks. The mixture-of-experts model activates 23 billion of its 501 billion total parameters per token, keeping compute costs relatively low. On demanding reasoning tasks, Beam matches GLM 5.2 while using three to four times less comput

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