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This arXiv paper studies how language models decide whether two scientific findings are comparable before resolving contradictions. In a controlled task with unsatisfiable XOR constraints translated into lab reports, GPT-5.6 Sol and Claude Opo 5 recover the best-supported assignment well when constraints are explicit, but models often defer to biological expectations when findings are presented in scientific prose.
A speaker announcement for Modal's Runtime event in San Francisco, featuring talks on AI x Science, inference, training, and agents from engineers at NVIDIA, Typesafe AI, Runway, and Achira AI.
An AI system autonomously built an atom-by-atom simulator and ran experiments for days, discovering graphene designs that are about 25% stronger at the same mass.
ScienceBuddy is a free, biomed-focused AI tool by PhAI Labs that assists researchers in scientific analysis, such as differential expression, and emphasizes accuracy by correcting user input and avoiding data fabrication.
Lila Sciences is developing an integrated AI-driven platform to automate scientific experiments, using AI models and robotics to tackle unverifiable hypotheses in science, with a vision to create scaling laws for scientific intelligence.
This paper evaluates AI's performance against traditional statistics and scientific computing in 27 scientific disciplines, finding that AI often outperforms statistics at higher computational cost but increasingly outperforms computing at lower cost, reshaping the scientific frontier.
ARCHE is an autonomous agentic system that integrates reasoning and computational models to automate the discovery and validation of chemical reaction mechanisms, validated through various chemical scenarios.
The article discusses how AI models like AlphaGenome can advance scientific breakthroughs by narrowing search spaces in biology, emphasizing AI's role in guiding research efficiently.
Dr. Anima Anandkumar was named one of TIME's 100 most influential people in AI and has launched her company Accelerated Understanding to integrate AI and science.
Prof. Anima Anandkumar was named one of TIME's 100 most influential people in AI, coinciding with the public launch of her company Accelerated Understanding, which focuses on integrating AI and science.
This article explores whether current AI models could drive breakthrough scientific discoveries, such as new materials, by accelerating hypothesis generation and research, drawing parallels to the LK-99 superconductor episode.
Introduces IG-Bench, a benchmark for scientific lineage reasoning and idea generation, representing scientific works as Idea Genome objects. Evaluates 14 LLM-based scientists, finding a compositional bottleneck with the strongest system achieving only 27.3% exact accuracy on lineage reasoning.
Anthropic launched Claude Science, a flagship product for scientific research that can autonomously carry out tasks in computational biology and drug development, signaling a major bet on AI for science.
This perspective paper develops a conceptual and methodological framework for evaluating evidence-licensed claims in AI-assisted research, emphasizing calibration as a mechanism for managing scientific assertion rights and distinguishing between different AI research routes.
Anthropic launched Claude Science, a new flagship product for autonomous scientific research in computational biology and drug development, available to all paid Claude subscribers.
Discusses the challenge of verifying AI-generated hypotheses in scientific discovery where no ground truth exists, and presents Apodex's multi-agent approach with independent verifier agents as a solution.
This position paper argues that a scientific understanding of AI must go beyond post-hoc analysis and instead study the training dynamics that shape model behavior, with implications for predicting, intervening, and designing training procedures for desired properties like capabilities and safety.
MIT Technology Review's newsletter covers three major stories: Anthropic's Code with Claude event showing developers increasingly shipping AI-written code without review, the upcoming Enhanced Games for athletes using performance-enhancing drugs, and Google I/O's shift towards agentic AI for science with Gemini for Science.
The article discusses how current AI systems can assist parts of the scientific workflow, potentially accelerating incremental discovery in data-rich fields, but they remain limited by dependence on existing literature and human-defined objectives, risking epistemic homogenization.
Google I/O keynote highlighted a shift in AI-driven science, contrasting specialized tools like WeatherNext with the rise of agentic AI systems that can conduct research autonomously, signaling a realignment in resources and enthusiasm.