Daniel Weld
1K posts
@dsweld
Computer science prof, entrepreneur & leader at Ai2. Excited by AI for science, human-AI interaction, and Web-scale NLP.
New AstaBench results show frontier models making progress on scientific research, but the benchmark remains far from solved. Claude Opus 4.7 leads overall at 58.0%, while GPT-5.5 comes within 5.1 points at less than half the measured cost per problem. 🧵
Today we're releasing MolmoWeb, an open source agent that can navigate + complete tasks in a browser on your behalf. Built on Molmo 2 in 4B & 8B sizes, it sets a new open-weight SOTA across four major web-agent benchmarks & even surpasses agents built on proprietary models. 🧵
Introducing Theorizer: Turning thousands of papers into scientific laws 📚➡️📜 Most automated discovery systems focus on experimentation. Theorizer tackles the other half of science: theory building—compressing scattered findings into structured, testable claims. 🧵
🎀 Really excited to start cadence for 2026! At @allen_ai, we are at the intersection of AI making a real impact in accelerating science, with several serious collaborations with domain scientists (Economics, Oncology, Neuroscience, Climate, Epidemiology). If you are passionate about turning the stack upside down to break and build current LLMs to be adaptive for a continual discovery process, join us! If you are interested in my work, such as data-driven discovery, open-ended hypothesis search, test-time adoption for hypothesis generation, causal mechanism discovery using data and literature, mention my name in your application! Past interns might vouch for their experiences, but working with Asta interns for the last two years has been one of my most rewarding journeys at Ai2! 🩷
🔥Thrilled to introduce DR Tulu-8B, an open long-form Deep Research model that matches OpenAI DR 💪Yes, just 8B! 🚀 The secret? We present Reinforcement Learning with Evolving Rubrics (RLER) for long-form non-verifiable DR tasks! Our rubrics: - co-evolve with the policy model - are grounded on search knowledge 🧵
Agent benchmarks don't measure true *AI* advances We built one that's hard & trustworthy 👉AstaBench tests agents w/ *standardized tools* on 2400+ scientific research problems 👉SOTA results across 22 agent *classes* 👉AgentBaselines agents suite 🆕arxiv.org/abs/2510.21652 🧵👇
Introducing Asta DataVoyager—our new AI capability in Asta that turns structured data into transparent, reproducible insights. Built for scientists, grounded in open, inspectable workflows. 🧵
