My longtime collaborator Dave Patterson (long-time faculty at UC Berkeley, Association for Computing Machinery Turing Award winner, and fellow Laude Institute board member) wrote a very good op-ed about how continued investing in basic science and technology research is essential for the U.S. Dave

Soon, AI agents will act for us—collaborating, negotiating, and sharing data. But can they truly protect our privacy? We simulate privacy-critical scenarios, using alternating search to evolve attacks and defenses, uncovering severe vulnerabilities and building protections.

Introducing Generative Interfaces - a new paradigm beyond chatbots. We generate interfaces on the fly to better facilitate LLM interaction, so no more passive reading of long text blocks. Adaptive and Interactive: creates the form that best adapts to your goals and needs!

🚨 New paper alert 🚨 Using LLMs as data annotators, you can produce any scientific result you want. We call this **LLM Hacking**. Paper: arxiv.org/pdf/2509.08825

LLMs are powerful, but they don't know your world. This knowledge gap can lead to generic, unhelpful, or incorrect responses. In our #UIST2025 paper, we explore how users can fill these gaps through creating a community knowledge ecosystem, giving models access to more specific

AI is already at work in American newsrooms. We examine 186k articles published this summer and find that ~9% are either fully or partially AI-generated, usually without readers having any idea. Here's what we learned about how AI is influencing local and national journalism:

Agents are joining us at work -- coding, writing, design. But how do they actually work, especially compared to humans? Their workflows tell a different story: They code everything, slow down human flows, and deliver low-quality work fast. Yet when teamed with humans, they shine

Can AI simulate human behavior? 🧠 The promise is revolutionary for science & policy. But there’s a huge "IF": Do these simulations actually reflect reality? To find out, we introduce SimBench: The first large-scale benchmark for group-level social simulation. (1/9)

Training LLMs end to end is hard. Very excited to share our new blog (book?) that cover the full pipeline: pre-training, post-training and infra. 200+ pages of what worked, what didn’t, and how to make it run reliably huggingface.co/spaces/Hugging…

Good morning Suzhou! Amelia Hardy and I will be at EMNLP 2025 to present our work *TODAY, Hall C, 12:30PM; paper number 426* Come learn: ✅ why likelihood is important to simultaneously optimize with attack success ✅ online preference learning tricks for LM falsification

New EMNLP main paper: “Finetuning LLMs for Human Behavior Prediction in Social Science Experiments” We built SocSci210—2.9M human responses from 210 social science experiments. Finetuning Qwen2.5-14B on SocSci210 beats its base model by 26% & GPT-4o by 13% on unseen studies.🧵