微软提出机器人推理卸载架构，以突破物理AI硬件算力瓶颈
微软研究院针对机器人硬件难以跟上AI能力增长的瓶颈，发布了有关“推理卸载”的最新研究成果。研究表明，将AI模型的推理计算任务转移至机器人本体之外的外部计算平台，能够显著提升任务执行的成功率并提高系统效率，同时支持运行更为复杂的先进物理AI工作负载，为智能机器人突破车载硬件限制提供了可行的计算架构方案。
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9月24日2026-09-24
9月21日2026-09-21
微软研究院推出RetroChimera模型，大幅提升小分子化学合成预测效率
定制化分子在医药、材料和农业领域具有广泛应用前景，但其人工制备过程往往耗时且昂贵。微软研究院在《自然》（Nature）期刊发表最新研究，介绍了名为RetroChimera的预测模型。该模型专注于大规模提升小分子的逆合成预测能力，能够显著加速化学合成路径的规划，帮助科研人员更高效、广泛地探索和筛选新型分子结构。
9月1日2026-09-01
GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models
What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on Microsoft Research .
8月21日2026-08-21
Broadening access to Skala creates a faster path to predictive DFT
Skala 1.1, the updated deep-learning exchange-correlation functional from Microsoft Research, provides greater accuracy, expanded accessibility across the computational chemistry ecosystem, and a living benchmark to track computational performance. The post Broadening access to Skala creates a faster path to predictive DFT appeared first on Microsoft Research .
8月13日2026-08-13
MindTopo reveals VLMs’ spatial reasoning abilities
A path, a fence, a knot. MindTopo sets a new benchmark for testing how AI understands topological relationships and highlights new opportunities to strengthen spatial reasoning and planning. The post MindTopo reveals VLMs’ spatial reasoning abilities appeared first on Microsoft Research .
8月12日2026-08-12
Introducing CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement
Radiology AI is evolving beyond report generation. CARE-X explores a unified approach that combines flexible reasoning, calibrated predictions, and measurement-based tools for chest X-ray interpretation. The post Introducing CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement appeared first on Microsoft Research .
8月4日2026-08-04
Orchard: An open framework for scalable agentic AI
Orchard is an open-source framework for the research community to train and evaluate AI agents across task types. It reduces complexity while supporting strong performance from smaller models by enabling researchers to reuse the same infrastructure. The post Orchard: An open framework for scalable agentic AI appeared first on Microsoft Research .
7月31日2026-07-31
Echoverse: Deep, evolving environments for computer-use agents
Computer-use AI agents struggle with multi-step workflows like email and customer support. Echoverse trains agents in realistic environments rather than simply providing more training tasks, helping them improve as the tasks, tests, and environments evolve. The post Echoverse: Deep, evolving environments for computer-use agents appeared first on Microsoft Research .
EvoLib: Turning experience into evolving knowledge
LLMs do not get smarter just by remembering more. EvoLib turns experience into evolving knowledge, taking reusable skills and insights that help models learn and adapt across tasks long after deployment. The post EvoLib: Turning experience into evolving knowledge appeared first on Microsoft Research .
7月14日2026-07-14
Verifying Rust cryptography in SymCrypt, from standards to code
Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research .