VISTA: Steering LLMs with Latent User Values Inferred from a Few Survey Answers

Researchers introduce self-building laboratories that autonomously design, configure, and operate experimental setups from high-level scientific goals.

Researchers propose Hybrid Reasoning Agents that combine latent reasoning with text-based interaction, achieving 10.4% performance gains and 35-55% token reduction.

Researchers developed LLM agents that automate scientific reproducibility checks, successfully reproducing 77.4% of claims in medical studies and flagging 9.5% for expert review.

Researchers introduce DiscoveryRSI, a framework that recursively learns to generate valuable scientific hypotheses through multi-agent evolutionary search and self-improvement mechanisms.

Researchers introduce ENFR, a novel method using flow-matched references for amortized evidence estimation in simulation-based Bayesian model inference.