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PSI is building a vertically integrated factory for physical superintelligence to accelerate physics breakthroughs with artificial superintelligence, and has open-sourced an AI copilot for physicists called Get Physics Done (GPD).
OpenAI's GPT-5 Pro helped immunologist Derya Unutmaz solve a three-year-old mystery about how glucose affects T cell specialization by suggesting that deoxyglucose interferes with IL-2 protein construction, leading to increased inflammatory Th17 cells.
NatureBench is a cross-disciplinary benchmark of 90 scientific tasks from Nature publications, designed to evaluate AI coding agents' ability to achieve genuine discovery. Current agents succeed mainly through methodological translation, not scientific innovation.
NVIDIA announces new AI software libraries and microservices—DAQIRI, ALCHEMI, and cuPhoton—that dramatically accelerate scientific workloads in fields like astronomy, materials science, and particle physics, achieving up to 14,900x speedups over CPU-based pipelines.
Oswald Steward and colleagues won the 2026 Kavli Prize in Neuroscience for discovering that neurons can produce proteins near synapses, fundamentally changing understanding of memory and brain plasticity.
A paper presenting The AI Scientist, a system that automates the entire research lifecycle from idea generation to peer review, demonstrating AI's growing capacity for scientific contribution.
THU Team Eureka open-sources EurekAgent, an autonomous research system built with Claude Code that achieves state-of-the-art results on math, kernel engineering, and ML tasks through environment engineering.
The article argues that real-life disclosure of alien life would likely be a gradual, scientific process akin to the Higgs boson discovery rather than the dramatic cinematic reveal depicted in Steven Spielberg's new movie, citing recent UAP hearings and the lack of conclusive evidence.
Introduces SciAgentArena, a benchmark of ~200 tasks for evaluating AI agents in real scientific research. Finds agents effective for well-specified data-analysis workflows but struggle with novel insights and open-ended exploration.
Introduces StatefulDiscovery, a framework for open-ended scientific discovery that uses externalized investigation state to calibrate evidence and claims, outperforming baselines in producing well-supported high-value claims.
This paper presents EinsteinArena, an agent-native platform enabling decentralized scientific discovery through open interaction among autonomous AI agents. The platform has already produced 12 new state-of-the-art results, including an improved lower bound for the kissing number problem in dimension 11, demonstrating that collective AI-driven research can emerge from agents sharing insights and building on each other's work.
This paper proposes an evolutionary framework inspired by parallel tempering that uses multi-temperature sampling and information exchange to improve the diversity and quality of scientific hypotheses generated by large language models, demonstrated across molecular, equation, and algorithm discovery.
Introduces SciTrace, a framework that integrates safety reasoning into every stage of scientific agent pipelines using a Safety-Intrinsic Reasoning Loop and a Compositional Tool-Chain Verifier, achieving state-of-the-art safety while preserving output quality.
EditSR proposes a two-layer framework combining a neural symbolic regression model with an edit-based Rectifier to efficiently rectify structural errors in generated expressions, reducing error accumulation and improving recovery of complex symbolic structures with limited extra cost.
This paper introduces a categorical framework for distinguishing genuine scientific discovery from mere retrieval or search in self-improving AI agents, using category theory to formalize regime transitions. The authors demonstrate the framework with a protein mechanics example where an agent's accuracy drops as it tackles harder problems, but its theory compresses more data, indicating real discovery.
This article discusses a new MIT paper proposing a framework for self-evolving AI scientists that can recognize when their current model is insufficient and introduce new scientific concepts, distinguishing between retrieval, search, and discovery.
Announcement of the LLMs for Scientific Discovery workshop at COLM 2026 in San Francisco, with a call for papers due June 23 and a request for reviewers.
Researchers at MIT present a paper on self-evolving AI scientists that can discover and adapt their own scientific vocabulary, using a categorical framework to mathematically quantify genuine novelty and separate discovery from mere search or retrieval.
An OpenAI model found a counterexample to an 80-year-old Erdős conjecture, with researchers sharing the story on the OpenAI Podcast about how AI and mathematicians can collaborate on mathematical discoveries.
Google DeepMind has open-sourced Science Skills, a collection of agent skills for scientific research tasks including genomics, structural biology, and cheminformatics, to accelerate agentic workflows with scientific grounding and higher token efficiency.