TechCrunch AI
Industry News1m
India’s app market is booming — but global platforms are capturing most of the gains
Non-gaming apps, led by streaming and AI, are driving growth, even as India's spending per user lags global peers.
Research
April 22, 2026
1 min read
3 views
AutoAdapt: Automated domain adaptation for large language models
Original Source
Microsoft Research AI
by Sidharth Sinha, Anson Bastos, Xuchao Zhang, Akshay Nambi, Rujia Wang, Chetan Bansal
Deploying large language models (LLMs) in real-world, high-stakes settings is harder than it should be. In high-stakes settings like law, medicine, and cloud incident response, performance and reliability can quickly break down because adapting models to domain-specific requireme
Deploying large language models (LLMs) in real-world, high-stakes settings is harder than it should be. In high-stakes settings like law, medicine, and cloud incident response, performance and reliability can quickly break down because adapting models to domain-specific requirements is a slow and manual process that is difficult to reproduce. The core challenge is domain adaptation, […] The post AutoAdapt: Automated domain adaptation for large language models appeared first on Microsoft Research .
Tags:LLMAI
Original Content Credit
This summary is sourced from Microsoft Research AI. For the complete article with full details, research data, and author insights, please visit the original source.
Visit Microsoft Research AIRelated Articles
ArXiv AI (cs.AI)
Industry News1m
The Tool-Overuse Illusion: Why Does LLM Prefer External Tools over Internal Knowledge?
arXiv:2604.19749v1 Announce Type: new Abstract: Equipping LLMs with external tools effectively addresses internal reasoning limitations. However, it introduces a critical yet under-explored phenomenon: tool overuse, the unnecessary tool-use during reasoning. In this paper, we fir
ArXiv AI (cs.AI)
Industry News1m
AI to Learn 2.0: A Deliverable-Oriented Governance Framework and Maturity Rubric for Opaque AI in Learning-Intensive Domains
arXiv:2604.19751v1 Announce Type: new Abstract: Generative AI is entering research, education, and professional work faster than current governance frameworks can specify how AI-assisted outputs should be judged in learning-intensive settings. The central problem is proxy failure