# Microsoft Research

Explore research at Microsoft, a site featuring the impact of research along with publications, products, downloads, and research careers.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models

DevFeed: [GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models](<https://devfeed.tech/articles/gigapath-flash-and-gigatime-flash-toward-population-scale-discovery-with-efficient-pathology-foundation-models-6796.md>)

Original publisher: [Read original article](<https://www.microsoft.com/en-us/research/blog/gigapath-flash-and-gigatime-flash-toward-population-scale-discovery-with-efficient-pathology-foundation-models/>)

Author: Naoto Usuyama, Jeya Maria Jose Valanarasu, Tristan Naumann

Published: 2026-08-31T16:00:00Z

Content type: article

Language: en

Sources: [Microsoft Research](<https://devfeed.tech/sources/microsoft-research.md>)

Topics: [foundation-models](<https://devfeed.tech/topics/foundation-models.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [analysis](<https://devfeed.tech/tags/analysis.md>), [data](<https://devfeed.tech/tags/data.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [foundation-models](<https://devfeed.tech/tags/foundation-models.md>), [models](<https://devfeed.tech/tags/models.md>), [performance](<https://devfeed.tech/tags/performance.md>), [research](<https://devfeed.tech/tags/research.md>), [research-blog](<https://devfeed.tech/tags/research-blog.md>), [scale](<https://devfeed.tech/tags/scale.md>), [validation](<https://devfeed.tech/tags/validation.md>)

### AI overview

GigaPath-Flash and GigaTIME-Flash are efficient pathology foundation models designed to reduce computational demands while maintaining strong performance. They enable repeated analysis of larger cancer cohorts and support population-scale research into disease biology, biomarkers, and clinical outcomes.

### Source excerpt

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.

## Broadening access to Skala creates a faster path to predictive DFT

DevFeed: [Broadening access to Skala creates a faster path to predictive DFT](<https://devfeed.tech/articles/broadening-access-to-skala-creates-a-faster-path-to-predictive-dft-6783.md>)

Original publisher: [Read original article](<https://www.microsoft.com/en-us/research/blog/broadening-access-to-skala-creates-a-faster-path-to-predictive-dft/>)

Author: Sebastian Ehlert, Stefano Battaglia, Thijs Vogels, Jan Hermann, Jens Wehner, Giulia Luise, Klaas Giesbertz, Chin-Wei Huang, Aaron Kaplan, Kate Milton, Stephanie Marisa Lanius, Derk Kooi, P. Bernát Sza

Published: 2026-08-20T16:00:00Z

Content type: article

Language: en

Sources: [Microsoft Research](<https://devfeed.tech/sources/microsoft-research.md>)

Topics: [Deep neural networks](<https://devfeed.tech/topics/deep-neural-networks.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [Microsoft](<https://devfeed.tech/topics/microsoft.md>), [data](<https://devfeed.tech/topics/data.md>), [Software](<https://devfeed.tech/topics/software.md>)

Tags: [benchmark](<https://devfeed.tech/tags/benchmark.md>), [chemistry](<https://devfeed.tech/tags/chemistry.md>), [data](<https://devfeed.tech/tags/data.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [integration](<https://devfeed.tech/tags/integration.md>), [microsoft](<https://devfeed.tech/tags/microsoft.md>), [performance](<https://devfeed.tech/tags/performance.md>), [research](<https://devfeed.tech/tags/research.md>), [research-blog](<https://devfeed.tech/tags/research-blog.md>), [software](<https://devfeed.tech/tags/software.md>), [workflows](<https://devfeed.tech/tags/workflows.md>)

### AI overview

Microsoft Research presents Skala 1.1, a deep-learning exchange-correlation functional that improves accuracy for molecular simulations while expanding access through integrations with major electronic-structure software. A living benchmark will track the computational performance of future releases.

### Source excerpt

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.

## MindTopo reveals VLMs' spatial reasoning abilities

DevFeed: [MindTopo reveals VLMs' spatial reasoning abilities](<https://devfeed.tech/articles/mindtopo-reveals-vlms-spatial-reasoning-abilities-6802.md>)

Original publisher: [Read original article](<https://www.microsoft.com/en-us/research/blog/mindtopo-reveals-vlms-spatial-reasoning-abilities/>)

Author: Yunfei Ge, Anbang Liu, Qineng Wang, Johnalbert Garnica, Zihan Wang, Reuben Tan, Jianfeng Gao, Ruohan Zhang, Yining Hong, Jiajun Wu, Manling Li

Published: 2026-08-12T16:00:00Z

Content type: article

Language: en

Sources: [Microsoft Research](<https://devfeed.tech/sources/microsoft-research.md>)

Topics: [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>), [multimodal-ai](<https://devfeed.tech/topics/multimodal-ai.md>), [Language models](<https://devfeed.tech/topics/language-models.md>), [Robotics](<https://devfeed.tech/topics/robotics.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [large-language-models](<https://devfeed.tech/tags/large-language-models.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [research](<https://devfeed.tech/tags/research.md>), [research-blog](<https://devfeed.tech/tags/research-blog.md>), [testing](<https://devfeed.tech/tags/testing.md>), [vlms](<https://devfeed.tech/tags/vlms.md>)

### AI overview

MindTopo is a benchmark for evaluating whether multimodal large language models can understand and manipulate topological relationships such as connectivity, enclosure, order, separation, and knots. It compares static recognition with interactive planning and finds that current models often lose track of structural relationships during sequences of actions.

### Source excerpt

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.

## Introducing CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement

DevFeed: [Introducing CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement](<https://devfeed.tech/articles/introducing-care-x-towards-clinically-useful-radiology-vlms-with-auxiliary-supervision-reward-aligned-learning-and-tool-augmented-measurement-6800.md>)

Original publisher: [Read original article](<https://www.microsoft.com/en-us/research/blog/introducing-care-x-towards-clinically-useful-radiology-vlms-with-auxiliary-supervision-reward-aligned-learning-and-tool-augmented-measurement/>)

Author: Mercy Ranjit, Nikhilesh E, Dr. Abhyuday Kumara Swamy, Tanuja Ganu

Published: 2026-08-11T16:00:00Z

Content type: article

Language: en

Sources: [Microsoft Research](<https://devfeed.tech/sources/microsoft-research.md>)

Topics: [vlm](<https://devfeed.tech/topics/vlm.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [data](<https://devfeed.tech/tags/data.md>), [generation](<https://devfeed.tech/tags/generation.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [research](<https://devfeed.tech/tags/research.md>), [research-blog](<https://devfeed.tech/tags/research-blog.md>), [tools](<https://devfeed.tech/tags/tools.md>), [vlms](<https://devfeed.tech/tags/vlms.md>)

### AI overview

CARE-X is a research chest X-ray vision-language model that combines free-text report generation, structured diagnostic prediction, and reinforcement learning for multi-task clinical interpretation. The article also describes a separate experiment using deterministic measurement tools with Qwen3-VL-4B-Instruct and reports validation on real-world Indian clinical data, while emphasizing that CARE-X is not approved for clinical use.

### Source excerpt

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.

## Orchard: An open framework for scalable agentic AI

DevFeed: [Orchard: An open framework for scalable agentic AI](<https://devfeed.tech/articles/orchard-an-open-framework-for-scalable-agentic-ai-6806.md>)

Original publisher: [Read original article](<https://www.microsoft.com/en-us/research/blog/orchard-an-open-framework-for-scalable-agentic-ai/>)

Author: Baolin Peng, Wenlin Yao, Qianhui Wu, Hao Cheng, Jianfeng Gao

Published: 2026-08-03T16:00:00Z

Content type: article

Language: en

Sources: [Microsoft Research](<https://devfeed.tech/sources/microsoft-research.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [AI Research](<https://devfeed.tech/topics/ai-research.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Frameworks](<https://devfeed.tech/topics/frameworks.md>), [Kubernetes](<https://devfeed.tech/topics/kubernetes.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [computer-use](<https://devfeed.tech/topics/computer-use.md>), [codex](<https://devfeed.tech/topics/codex.md>), [OpenClaw](<https://devfeed.tech/topics/openclaw.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [autonomous](<https://devfeed.tech/tags/autonomous.md>), [autonomous-agents](<https://devfeed.tech/tags/autonomous-agents.md>), [building](<https://devfeed.tech/tags/building.md>), [codex](<https://devfeed.tech/tags/codex.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [framework](<https://devfeed.tech/tags/framework.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [learning](<https://devfeed.tech/tags/learning.md>), [models](<https://devfeed.tech/tags/models.md>), [open](<https://devfeed.tech/tags/open.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [research](<https://devfeed.tech/tags/research.md>), [research-blog](<https://devfeed.tech/tags/research-blog.md>), [train](<https://devfeed.tech/tags/train.md>), [workflows](<https://devfeed.tech/tags/workflows.md>)

### AI overview

Orchard is an open-source framework for training and evaluating agentic AI systems across software engineering, web navigation, and personal-assistant tasks. Its reusable Orchard Env provides Kubernetes-based infrastructure for data collection, reinforcement-learning rollouts, and evaluation, while Orchard-SWE, Orchard-GUI, and Orchard-Claw demonstrate strong results from relatively small open-weight models.

### Source excerpt

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.

## Echoverse: Deep, evolving environments for computer-use agents

DevFeed: [Echoverse: Deep, evolving environments for computer-use agents](<https://devfeed.tech/articles/echoverse-deep-evolving-environments-for-computer-use-agents-6787.md>)

Original publisher: [Read original article](<https://www.microsoft.com/en-us/research/blog/echoverse-deep-evolving-environments-for-computer-use-agents/>)

Author: Akshay Nambi, Yash Pandya, Sahil Gupta, Sarthak Harne, Kavyansh Chourasia, Yash Lara, Ahmed Awadallah

Published: 2026-07-30T17:00:00Z

Content type: article

Language: en

Sources: [Microsoft Research](<https://devfeed.tech/sources/microsoft-research.md>)

Topics: [computer-use](<https://devfeed.tech/topics/computer-use.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Microsoft](<https://devfeed.tech/topics/microsoft.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [computer-use](<https://devfeed.tech/tags/computer-use.md>), [data](<https://devfeed.tech/tags/data.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [microsoft](<https://devfeed.tech/tags/microsoft.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [research](<https://devfeed.tech/tags/research.md>), [research-blog](<https://devfeed.tech/tags/research-blog.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

Echoverse presents twelve high-fidelity training worlds for computer-use agents, designed around realistic application behavior, coherent state, seeded data, and challenging interface controls. Training a 9B model on these environments substantially improved its score, while reinforcement learning with grounded verification helped it generalize and complete goals in fewer steps. Four worlds are released with code, data, and graders to support research.

### Source excerpt

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

DevFeed: [EvoLib: Turning experience into evolving knowledge](<https://devfeed.tech/articles/evolib-turning-experience-into-evolving-knowledge-6790.md>)

Original publisher: [Read original article](<https://www.microsoft.com/en-us/research/blog/evolib-turning-experience-into-evolving-knowledge/>)

Author: Weijia Xu, Zelalem Gero, Michel Galley, Eric Yuan, Jianfeng Gao

Published: 2026-07-30T16:00:00Z

Content type: article

Language: en

Sources: [Microsoft Research](<https://devfeed.tech/sources/microsoft-research.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [AI Models](<https://devfeed.tech/topics/ai-models.md>), [Language models](<https://devfeed.tech/topics/language-models.md>), [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [LLM Techniques](<https://devfeed.tech/topics/llm-techniques.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-models](<https://devfeed.tech/tags/ai-models.md>), [inference](<https://devfeed.tech/tags/inference.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [learning](<https://devfeed.tech/tags/learning.md>), [llms](<https://devfeed.tech/tags/llms.md>), [memory](<https://devfeed.tech/tags/memory.md>), [performance](<https://devfeed.tech/tags/performance.md>), [post](<https://devfeed.tech/tags/post.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [research](<https://devfeed.tech/tags/research.md>), [research-blog](<https://devfeed.tech/tags/research-blog.md>)

### AI overview

EvoLib is a framework for turning an AI system's past attempts into reusable skills and reflective insights. It continually refines, consolidates, and reweights this evolving knowledge so models can learn from successes and failures across tasks without updating the underlying model.

### Source excerpt

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.

## Verifying Rust cryptography in SymCrypt, from standards to code

DevFeed: [Verifying Rust cryptography in SymCrypt, from standards to code](<https://devfeed.tech/articles/verifying-rust-cryptography-in-symcrypt-from-standards-to-code-6810.md>)

Original publisher: [Read original article](<https://www.microsoft.com/en-us/research/blog/verifying-rust-cryptography-in-symcrypt-from-standards-to-code/>)

Author: Son Ho, Cédric Fournet, Antoine Delignat-Lavaud, Samuel Lee, Jason Fisher, Jessica Krynitsky

Published: 2026-07-13T16:00:00Z

Content type: article

Language: en

Sources: [Microsoft Research](<https://devfeed.tech/sources/microsoft-research.md>)

Topics: [Rust](<https://devfeed.tech/topics/rust.md>), [Rust formal verification](<https://devfeed.tech/topics/rust-formal-verification.md>), [Cryptography](<https://devfeed.tech/topics/cryptography.md>), [Formal verification](<https://devfeed.tech/topics/formal-verification.md>), [Post-quantum cryptography](<https://devfeed.tech/topics/post-quantum-cryptography.md>), [Lean](<https://devfeed.tech/topics/lean.md>), [toolchain](<https://devfeed.tech/topics/toolchain.md>), [Microsoft](<https://devfeed.tech/topics/microsoft.md>)

Tags: [cryptography](<https://devfeed.tech/tags/cryptography.md>), [formal-verification](<https://devfeed.tech/tags/formal-verification.md>), [memory-safety](<https://devfeed.tech/tags/memory-safety.md>), [microsoft](<https://devfeed.tech/tags/microsoft.md>), [post-quantum](<https://devfeed.tech/tags/post-quantum.md>), [research](<https://devfeed.tech/tags/research.md>), [research-blog](<https://devfeed.tech/tags/research-blog.md>), [rust](<https://devfeed.tech/tags/rust.md>), [security](<https://devfeed.tech/tags/security.md>)

### AI overview

Microsoft Research describes how SymCrypt uses safe Rust, Lean, and the Aeneas toolchain to formally verify production cryptographic implementations. The approach combines Rust's memory-safety guarantees with machine-checked proofs of functional correctness, with initial verified code for SHA-3 and ML-KEM and support from independently verifiable proof-writing agents.

### Source excerpt

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.

## Aurora 1.5: Extending open foundation models for weather and Earth-system applications

DevFeed: [Aurora 1.5: Extending open foundation models for weather and Earth-system applications](<https://devfeed.tech/articles/aurora-1-5-extending-open-foundation-models-for-weather-and-earth-system-applications-6780.md>)

Original publisher: [Read original article](<https://www.microsoft.com/en-us/research/blog/aurora-1-5-extending-open-foundation-models-for-weather-and-earth-system-applications/>)

Author: Kenji Takeda, Haiyu Dong, Jonathan Weyn, Amit Misra, Matt Corey, Kevin White, Shannon Monroe, Juan M. Lavista Ferres, Ashley Llorens, Bonnie Kruft

Published: 2026-07-09T16:46:22Z

Content type: article

Language: en

Sources: [Microsoft Research](<https://devfeed.tech/sources/microsoft-research.md>)

Topics: [foundation-models](<https://devfeed.tech/topics/foundation-models.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Microsoft](<https://devfeed.tech/topics/microsoft.md>), [GitHub](<https://devfeed.tech/topics/github.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [foundation-models](<https://devfeed.tech/tags/foundation-models.md>), [github](<https://devfeed.tech/tags/github.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [microsoft](<https://devfeed.tech/tags/microsoft.md>), [models](<https://devfeed.tech/tags/models.md>), [open](<https://devfeed.tech/tags/open.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [research](<https://devfeed.tech/tags/research.md>), [research-blog](<https://devfeed.tech/tags/research-blog.md>), [update](<https://devfeed.tech/tags/update.md>)

### AI overview

Aurora 1.5 extends Microsoft's open Aurora Earth-system foundation model with 22 additional weather variables, hourly temporal resolution, and probabilistic ensemble forecasting. Released through GitHub with checkpoints on Hugging Face, it is intended for researchers and developers working on weather, climate, energy, agriculture, transport, and related applications.

### Source excerpt

Aurora 1.5 adds 22 more variables, hourly temporal resolution, and probabilistic ensemble forecasting to the Aurora foundation model, making it more useful for real-world weather, climate, and energy applications. The post Aurora 1.5: Extending open foundation models for weather and Earth-system applications appeared first on Microsoft Research.

## Flint: A visualization language for the AI era

DevFeed: [Flint: A visualization language for the AI era](<https://devfeed.tech/articles/flint-a-visualization-language-for-the-ai-era-6794.md>)

Original publisher: [Read original article](<https://www.microsoft.com/en-us/research/blog/flint-a-visualization-language-for-the-ai-era/>)

Author: Chenglong Wang, Alper Sarikaya, Scott Tsukamaki, Michel Galley, Jianfeng Gao

Published: 2026-07-08T16:00:00Z

Content type: article

Language: en

Sources: [Microsoft Research](<https://devfeed.tech/sources/microsoft-research.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [MCP Server](<https://devfeed.tech/topics/mcp-server.md>), [data](<https://devfeed.tech/topics/data.md>), [Server](<https://devfeed.tech/topics/server.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [llms](<https://devfeed.tech/tags/llms.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [mcp-server](<https://devfeed.tech/tags/mcp-server.md>), [open](<https://devfeed.tech/tags/open.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [research-blog](<https://devfeed.tech/tags/research-blog.md>), [visualization](<https://devfeed.tech/tags/visualization.md>)

### AI overview

Flint is an open-source visualization intermediate language designed for AI-driven chart creation. It lets AI agents produce expressive charts from compact, human-editable specifications, using semantic data types and automatic layout decisions while targeting multiple visualization backends.

### Source excerpt

Short chart specifications are easy to write, but often produce uninspiring results. Flint is an open-source visualization language that offers a middle path, letting AI agents create expressive charts from compact, human-editable specifications. The post Flint: A visualization language for the AI era appeared first on Microsoft Research.