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2026-07-29
日期、来源、摘要、可信度和合规说明均来自通过完整审核门禁与哈希校验的每日公开快照；周报发布后由同期完整发布包接管。
RSS 公开元数据：The OlmoEarth Platform: Geospatial inference at planetary scale。🌍 Learn more about OlmoEarth Platform: https://allenai.org/olmoearth The OlmoEarth models are our family of Earth observation foundation models, pretrained on roughly 10 terabytes of multimodal satellite data. Governments, NGOs, and other mission-driven organizations are already adapting OlmoEarth for applications including deforestation monitoring, food security, and wildfire risk. At Ai2, we know how to train and release powerful open models, and for organizations with strong engineering teams, an open model is all they need to run with. But most organizations in the environmental space – the ones best placed to ap... 来源：RSSHub。RSSHub public RSS metadata only; no full article body stored. Trafilatura public URL extraction; stored short extract only, no full article body, cookies, or headers.
打开公开来源RSS 公开元数据：Accelerating scientific discovery with ChatGPT for Academic Researchers。AI is accelerating research AI is accelerating research What participants will get Training and research support How to apply AI is accelerating research What participants will get Training and research support How to apply We believe the benefits of frontier AI should not be concentrated in a few companies and well-resourced labs. Scientific progress depends on researchers asking the right questions, testing new ideas, and building on what others have discovered. Our role is to put powerful tools in their hands—and work alongside them to design models that accelerate their research while keeping them in contr... 来源：RSSHub。RSSHub public RSS metadata only; no full article body stored.
打开公开来源RSS 公开元数据：How GPT-5.6 fuses frontier intelligence with frontier efficiency。Accelerating inference with GPT-5.6 Sol Accelerating inference with GPT-5.6 Sol How our agentic harness streamlines repeated work Avoid context bloat Preserve exact prefixes for prompt caching Efficiency across the intelligence curve Accelerating inference with GPT-5.6 Sol How our agentic harness streamlines repeated work Avoid context bloat Preserve exact prefixes for prompt caching Efficiency across the intelligence curve We designed the GPT‑5.6 model family to balance capability and cost across the spectrum of tasks people use our m odels for. Our flagship model, GPT‑5.6 Sol, with max reasoning outperforms Claude ... 来源：RSSHub。RSSHub public RSS metadata only; no full article body stored.
打开公开来源RSS 公开元数据：How enabling two settings tripled our scores on the ARC-AGI-3 benchmark。ARC-AGI-3 ARC-AGI-3 Agents do best when they remember what they’ve done Conclusion and recommendations ARC-AGI-3 Agents do best when they remember what they’ve done Conclusion and recommendations p]:my-0 [&>p]:text-caption [&>p]:text-primary-100"> A sped-up video of GPT‑5.6 Sol attempting to solve puzzles in the ARC-AGI-3 benchmark, with the official harness (left) and our Responses API harness (right), which retains reasoning and enables compaction. On the leaderboard for this game (opens in a new window), no frontier model solves any level beyond the first. With our harness, GPT‑5.6 Sol solves all six. Whe... 来源：RSSHub。RSSHub public RSS metadata only; no full article body stored.
打开公开来源RSS 公开元数据：Scientific computing in the age of agentic AI。Case studies Case studies Recurring themes Long-term stewardship remains essential Toward more durable scientific software Case studies Recurring themes Long-term stewardship remains essential Toward more durable scientific software Scientific computing is a core pillar of modern research across academia and industry. Yet the software needed to analyze scientific information has struggled to keep pace with the rapid rate of data generation. Many widely used research tools began as code accompanying a research paper, built by small academic teams with limited engineering experience and minimal time for packaging, testing, optimization, ... 来源：RSSHub。RSSHub public RSS metadata only; no full article body stored.
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