Top Tweets for #colm2026

Agents trained on population-scale data can do a lot of things. But ask a professional to stake their reputation on an AI-generated artifact? “Pretty good” isn’t good enough. We introduce TAHI: a Test-time Adaptive agent framework through Human-agent Interaction. Featuring: 📈 Test-time adaptation via context (memory, skills) and weight training ⚡ Efficient adaptation to individual expertise within tens of tasks ✔️ Creating comprehensive rubrics for “non-verifiable” tasks 🔍 Analysis of shared community guidelines vs. personalized tacit expertise

Excited to share IdeaScientist, our research from my internship at Meta! 🚀 Can AI agents learn to generate novel, grounded scientific ideas? We introduce IdeaScientist, a framework that trains specialized agents to identify research gaps, discover useful connections across scientific domains, and turn them into concrete research proposals. 🔍 Key findings: • +14% overall performance over open autoresearch baselines • 25% improvement in novelty, showing that explicit training can improve scientific ideation • 75–92% human preference win rates against six open baselines in blind evaluations • Cross-domain retrieval substantially improves the transfer of ideas between research fields We also introduce Svalbard Idea Vault, a collection of 2.77M decomposed research ideas for training and evaluating scientific ideation. 📄 Paper: https://t.co/OCFIyCKe4k