Developers who are blind or have low vision have historically been limited to back-end programming, but new research suggests AI programming assistants are changing that in remarkable ways. A Microsoft Research Asia study found that developers who use screen readers can now tackle previously challenging…
Skala is a neural network-based exchange-correlation functional for density functional theory (DFT), developed by Microsoft Research AI for Science. It leverages deep learning to predict exchange-correlation energies from electron density features, achieving chemical accuracy for atomization energies and strong performance on broad thermochemistry and kinetics…
Large Language Model (LLM)-based agents are increasingly applied to tasks requiring structured reasoning, tool use, and environmental adaptation, such as data manipulation, multistep planning, and computer-use automation. However, despite their versatility, current training paradigms for model weight optimization methods, like PPO and GRPO, remain relatively…
Performance bugs are inefficiencies in software that waste computational resources without causing functional failures, making them particularly challenging to detect and fix. While recent advances in Software Engineering agents have shown promise in automated bug fixing, existing benchmarks primarily focus on functional correctness and fail…
We introduce ImaginationVellum, a multi-modal spatial canvas for early-stage visual ideation and concept sketching with generative AI. The resulting system supports a unique style of human-AI co-creation where the canvas is the prompt. This means that ImaginationVellum employs the entire 2D canvas as an active…
Developing AI systems that operate effectively across languages while remaining culturally grounded is a long-standing challenge, particularly in low-resource settings. Synthetic data provides a promising avenue, yet its effectiveness in multilingual and multicultural contexts remains underexplored. We investigate the creation and impact of synthetic, culturally…
Univ of Pittsburgh Institute of Politics, 2025 Regional Progress Forum - Strengthening the Foundation for Shared Economic Opportunities and Growth across Western Pennsylvania.
As large language models (LLMs) handle increasingly longer contexts, serving long inference requests of millions of tokens presents unique challenges. We show that existing work for long context inference is largely based on techniques from long context training, and does not handle the high variability…
Smartphone theft is common, yet little research explores how users prepare for or respond to such incidents. To address this gap in the literature, we conducted 20 semi-structured interviews with victims who had experienced smartphone theft in the past two years. These cases ranged from…
Recent advancements in Large Language Models (LLMs) have established them as agentic systems capable of planning and interacting with various tools. These LLM agents are often paired with web-based tools, enabling access to diverse sources and real-time information. Although these advancements offer significant benefits across…
We present the formal verification of Apple's iMessage PQ3, a highly performant, device-to-device messaging protocol offering strong security guarantees even against an adversary with quantum computing capabilities. PQ3 leverages Apple's identity services together with a custom, post-quantum secure initialization phase and afterwards it employs a…
Email spoofing is a critical technique used in phishing attacks to impersonate a trusted sender. SMTP smuggling is a new vulnerability that allows adversaries to perform email spoofing while bypassing existing authentication protocols such as SPF and DMARC. While SMTP smuggling has been publicly disclosed…
