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受益。 Maya Research CEO 迪曼特 · 雷迪指出，现有许多分词器仍然主要针对英语等数据资源丰富的语言进行优化，根本问题在于缺乏高质量的数字化数据。 雷迪强调，改善训练数据本身还不够：“Token 价格下降得很快，费用差异造成的直接影响应该会逐渐减轻。更好的多语言数据和合成数据也有助于提高模型质量。但要真正缩小不同语言之间的分词差距，AI 实验室仍然需要专门改进下一代分词器的设计，让它们更好地处理这些语言和文字。” “特别声明：以上作品内容(包括在内的视频、图片或音频)为凤凰网旗下自媒体平台“大风号”用户上传并发布，本平台仅提供信息存储空间服务。 Notice: The content above (including the videos, pictures and audios if any) is uploaded and posted by the user of Dafeng Hao, which is a social media platform and merely provides information storage space services.”

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题的技术基础设施。 从单点命中到跨靶点、跨模态的系统性验证，从底层模型突破到可复用的研发工作流，MMDesign的最新报告把“AI能不能设计出分子”的问题，推进到了“AI能不能稳定、可复用地产出可用分子”。 少量候选、稳定命中的设计效率；亲和力、可开发性与新颖性并重的分子质量；不依赖“参考答案”、从功能需求直接给出分子、成功拿下高难度靶点的突破价值。三者共同支撑起“可复用”能力，让AI分子设计可以被产业持续用于解决真实研发需求。 “特别声明：以上作品内容(包括在内的视频、图片或音频)为凤凰网旗下自媒体平台“大风号”用户上传并发布，本平台仅提供信息存储空间服务。 Notice: The content above (including the videos, pictures and audios if any) is uploaded and posted by the user of Dafeng Hao, which is a social media platform and merely provides information storage space services.”

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学与AI咨询小组（AGMAI）又专门发布指南，强调已经被人类数学家充分理解的AI研究成果，应该按照传统学术规范发布论文，接受期刊同行评审。 数学研究有一套延续多年的规矩。证明写出来了，还得经过专家检查、同行讨论，最后才能逐渐成为学术界公认的知识。 10月7日，一个名叫“人类数学协会”（AHM）的组织公开批评OpenAI一次性发布700多份数学研究文件，认为这更像是在展示力量，而非认真做学术。 但事情发展到这里，多少显得有些荒诞。 “特别声明：以上作品内容(包括在内的视频、图片或音频)为凤凰网旗下自媒体平台“大风号”用户上传并发布，本平台仅提供信息存储空间服务。 Notice: The content above (including the videos, pictures and audios if any) is uploaded and posted by the user of Dafeng Hao, which is a social media platform and merely provides information storage space services.”

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eration pushes further The first key trend is that each successive model generation outperforms its predecessor at fixed token budgets. On “The Last Ones”, GPT-4o (August 2024) averaged 1.7 steps at 10M tokens, while Opus 4.6 (February 2026) averages 9.8. At 100M tokens, the gap is even larger: Opus 4.5 averaged 11.0 steps compared to Opus 4.6's 15.6 – a 42% improvement between models released roughly two months apart. This improvement likely operates on two dimensions. The first is token effici

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作关系，我们将在不同的医院环境中评估临床推理能力。在一项前瞻性研究中，患者将直接与系统交互，并将表现与医生的独立评估进行对比。与临床团队并肩工作将揭示该技术在哪些方面取得成功、哪些方面存在不足，以及如何融入繁忙的工作流程。 微软健康 医学AI的快速进步正在为患者和临床医生开辟新的可能性。为了将这一进展转化为实际影响，我们正在将微软各处的健康团队——包括来自微软研究院的科学家，以及Dragon Copilot和PowerScribe等产品背后的团队——汇聚到Microsoft AI的一项整合行动中。通过携手合作，我们将加速交付可信赖的AI系统，以推动更好的临床结果和照护体验。 与我们一起构建未来 我们是一个精干、行动迅速的实验室，汇聚了世界上一些最有才华的人才。我们在MAI拥有一份令人振奋的算力路线图，其规模正在快速且大规模地提升。我们还有一个我们真正相信的雄心勃勃的使命。我们也很幸运能与出色的产品团队合作，让我们的模型有机会触达数十亿用户并创造巨大的积极影响。如果你是一位才华横溢、雄心勃勃且不摆架子的人，你会非常适合这里——来加入我们，一起研发我们的下一代模型！ 探索所有职位 相关区块

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In our earlier 𝜏-Voice experiments, accents caused the largest average drop in task completion among the factors we tested. That raised a broader question: how well do voice agents work for callers in other languages? Today we’re introducing 𝜏-i18n (pronounced tau-internationalization), which extends 𝜏-Voice to five more languages: Spanish, Brazilian Portuguese, Hindi, Korean, and Mandarin. 𝜏-i18n uses the same airline, retail, and telecom tasks across languages. Tasks, policies, tools, and succ

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e subsequent appearance of alternative approaches and formal verification further strengthens confidence that the field has uncovered a genuine underlying principle rather than a fragile technical argument. The authors have also shortened their proof and verified the streamlined argument in Lean; they plan to release both after final review. Together, these developments provide complementary forms of confirmation. Human-AI guidance In the process of proving KLS, this research also demonstrates t

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e and requires a multi-year commitment, enabling these health workers to perform assessments with minimal training has the potential to significantly expand prenatal screening coverage. Combining the minimal training requirement with AI interpretations could ultimately mean a successfully developed tool that has the potential to increase availability of ultrasound-based pregnancy assessment, and thereby improve access to higher quality maternal health. In “Generalization of AI-Based Gestational

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d Meta-Harness on task streams that changed topic, workflow, or domain. They tracked how much each method learned on the current task and lost on the others. GEPA learned the most, 6.2 points per block on average, and also lost the most elsewhere, 4.3 points. After it learned to ask users for missing details, a later block rewrote that rule and its 15.5-point gain fell to 2.5. If your agent edits its own prompt or memory, keep task-specific rules separate and retest old tasks after every update.

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past session goes into the agent's context. Across all three benchmarks, MemCurrent scores highest among all memory backends and stays on the cost-accuracy Pareto frontier. On average, MemCurrent (88.1%) performs on par with full context (87.8%) at 12x fewer input tokens per question (8.4K vs 103K). On the two benchmarks with the longest histories, it even beats full context, where dumping everything into the prompt ends up confusing the agent. Figure 5. Cost–accuracy1 trade-off of memory backe

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n airline); and lossless, which keeps every retained guideline (292 in retail, 101 in airline). The study tracks two metrics across the four trials per task: Mean Accuracy, the average success rate, which the authors frame as the capability axis, and Pass^4, the percentage of tasks solved on all four trials, framed as the reliability axis and identified as the metric the τ²-bench leaderboard reports. On 40 held-out retail test tasks, IBM reports the baseline agent scored 78.1% mean accuracy and

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produced its final list of 180 designs. The campaign used on the order of 10,000 GPU hours. 2 Claude worked autonomously for 24 hours following our first prompt. Its results encouraged us to prompt Claude to continue its campaign and look for better designs. Our follow-up prompts were mainly focused on directing Claude to design a multitoxin binder. Claude selected most of the 180 designs—including our strongest binder (Cn2-B1)—only after our third follow-up. At our request, Claude sent the pro

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specialties. A separate panel of physicians independently reviewed each task. The tasks are attempted against synthetically reconstructed patient records in an EHR sandbox. An average multi-step task takes 27 tool calls. Collectively, evaluating the task results in 670 checks in total, covering data retrieval, clinical reasoning, action execution, and documentation. Grading is execution-grounded: Task-specific scripts check whether the right orders exist in the record, with LLM judgments for th

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y diagnose a pathology in the fetal brain. “There are very fine details that you need a very good volumetric picture of the brain to see,” explains Muthukrishnan. “If you have coverage gaps, you can’t see critical regions, and you need to be able to see well to detect abnormal development.” What makes E(3)-Pose particularly good at tracking fetal motion is its focus on physics. Rather than simply training the model to learn physics from the data, the very principles of physics are baked into the

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verything their Claude agents produce would take up about half their workweek. And private work, done in unlogged meetings and direct messages, is “invisible, confusing, and discouraged in an AI-heavy workplace,” the report said. The median employee now spends 20% more time in meetings than a year ago. Part of that is a result of the company’s growth (Anthropic now has more than 3,500 employees), and part is owing to an emphasis on hiring in nontechnical roles. But meeting time is up 16% even am

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wo companies, card-skimming scripts confirmed in place on 19 sites with more than 100 further infected websites detected alongside researcher Varys, and an operator cost review averaging $25.46 across 101 completed scans. Inside the Cairn Harness NorForge describes Cairn less as an autonomous penetration-testing engine than as a state-based problem-solving engine. Each task is a project with a start point called an origin and a predefined end goal, and the framework progresses in cycles of facts

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aces, while yellow circles represent female faces only. Overall, 23% of generated faces exceeded this benchmark, rising to 37% for female faces. The dashed lines mark these two averages. The paper† states: ‘We audited 27 text-to-image generators released between 2022 and 2026 using simple prompts that never mention attractiveness or beauty, analyzing 51,300 generated faces and 160,196 photographs of real people across primary and supplementary comparisons. ‘We used three automated measures and c

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’s standard benchmarks; most of its cost comes from a single step, modular inversion, the wraparound version of division used in elliptic-curve arithmetic. Claude received a one-paragraph brief explaining the mathematical rule and how the evaluator would check and score each design, then worked for 53 hours with minimal human input, limited to the brief and occasional prompts to think more broadly, before arriving at a completely new approach, Anthropic reported. Classical computers perform modu

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About $0.30–$0.35 in model costs per solved case. That estimate uses DeepSeek’s off-peak API rates and includes failed attempts on RoboTwin, LIBERO and LIBERO-Pro. The 40 showcase episodes average $0.13 each. https://t.co/sUNWMar0zz

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