Stanford HAI

Achieving submicrometer thickness in liquid sheets opens the door to unprecedented insights into ultrafast interfacial dynamics.

SciReasoner not only improves prediction accuracy across multiple scientific domains but also offers interpretable reasoning that aligns with established scientific principles.

FILTR achieves up to 30x speed improvements in bioinformatics algorithms while simplifying the implementation of complex recurrence relations.

Charge conditioning in ML force fields can drastically enhance predictive accuracy while maintaining computational efficiency, achieving remarkable reductions in error metrics with minimal data.

Active-GRPO not only outperforms existing methods in molecular optimization but also redefines how models can adaptively balance imitation and self-discovery during training.

PaperPilot transforms scientific literature search by enabling users to iteratively refine their search strategies through an interactive workflow, achieving a remarkable reduction in execution errors.

Agon reveals that machine-driven research can scale effectively while exposing critical failure modes that still require human oversight.

SurfBind's innovative surface-centric approach outperforms traditional methods, revealing the critical role of molecular surface interactions in epitope prediction.

Static reports are out; BioInsight's interactive system empowers researchers to dynamically explore and refine biomedical evidence like never before.

Machine learning can transform 2DES by extracting maximum insights from limited data while guiding experimental design for improved accuracy.

Task exchangeability could redefine how researchers leverage synthetic data, ensuring valid inferences while mitigating biases and inaccuracies.

Molecular reference imbalance can introduce significant errors in quantum Monte Carlo adsorption energy calculations, but a new hybrid cycle effectively mitigates this issue.

Current AI agents excel in structured tasks but falter at generating novel insights and tackling open-ended scientific challenges.

MCMC and SMC techniques in latent space achieve lower data mismatch and better uncertainty reduction than traditional ensemble Kalman methods, all while preserving geological realism.

Agents collaborating on EinsteinArena achieved breakthroughs that surpassed previous human and AI solutions, showcasing the power of collective intelligence in scientific discovery.

RPA isn't just a formula; it's a closure approximation to the exact Hessian, reshaping our understanding of DFT and its related theories.

Tests of non-commutation can unlock classical cryptographic protocols like key agreement and oblivious transfer, even in the presence of quantum adversaries.

Substrate choice dictates tin anode morphology and stability in aqueous batteries, enabling a 70% utilization anode with extended lifespan.

Enhanced ionization doesn't just follow the laser's peak intensity; it hits a sweet spot, opening doors to controlling electron emission timing with sub-cycle precision.

AI-driven scientific discovery is closer than you think, but current systems still struggle with reproducibility, cross-domain robustness, and accountable scientific closure.

Despite their increasing role in scientific discovery, today's AI models are surprisingly bad at predicting which scientific breakthroughs will actually happen and when.

LLMs can now automatically design and execute experiments to resolve debates between cognitive science theories, even discovering the models and experiments themselves.

Force fields are revealed as the natural consequence of applying density functional theory to nuclear configurations, bridging two traditionally distinct approaches to molecular simulation.

Physics-informed neural ODEs, when coupled with DAE solvers and a lightweight corrector, can simulate large-scale HVAC systems orders of magnitude faster than traditional methods while maintaining high accuracy.

Neural video codecs can be designed for biological substrates from the ground up, unlocking a new paradigm for DNA storage.

Blind predictions of cyclobutanone photochemistry reveal that nonadiabatic molecular dynamics can qualitatively capture experimental results, but the accuracy of underlying electronic structure calculations remains a key bottleneck.

LLM agents can autonomously outperform fixed evolutionary search by 3-10x on open-ended discovery tasks when given persistent memory, asynchronous collaboration, and heartbeat-based interventions.

Medical AI Scientist leapfrogs generic LLMs in clinical research, generating higher-quality, evidence-backed hypotheses and manuscripts that rival top-tier medical publications.

Ultrafast X-ray spectroscopy reveals the hidden choreography of electronic state transitions that drive Norrish Type-I reactions, pinpointing the long-lived $^3n\pi^*$ state as the key player.

An AI agent can triage remote patient monitoring data with higher sensitivity than individual clinicians, suggesting a path to scalable and cost-effective patient monitoring.

Ditch the detector-specific hacks: a new end-to-end reconstruction pipeline slashes fake particles by up to two orders of magnitude and boosts energy resolution by 22% for future collider experiments.