scientific-discovery

This paper proposes a self-evolving memory framework for lifelong AI partners in materials science, storing scientific experience as facts and skills to improve agent performance across models. Evaluations show memory nearly doubles GPT-5.2 task success in materials tool-use questions and reduces repeated errors in simulations.

MIT Technology Review's daily newsletter covers an op-ed on AI agents for scientific discovery, an investigation into the 'censorship-industrial complex' influencing US policy, and news about an Amazon data center's potential pollution.

This MIT Technology Review article argues that AlphaFold-style deep learning on massive datasets is not the ideal template for accelerating science, and that AI agents capable of reasoning and experimentation will drive future breakthroughs.

This paper introduces SEE, a multimodal benchmark of expert-curated questions for scientific discovery in chemistry, biology, and materials science. Evaluation of 19 MLLMs shows the best model reaches only 48.7% accuracy, and even with tool use only 52.7%, revealing that current models lack reliable evidence-bounded scientific reasoning.

Jeff Dean and several other top Google AI researchers are leaving to launch Discovery Loop, a public benefit startup aimed at using AI to automate and massively scale scientific experimentation, with backing from Alphabet and major VC firms.

The paper introduces BLAZE, a paradigm of socialized scientific intelligence that organizes AI agents, knowledge, experiments, and human judgment into a continuous, cumulative research lifecycle for scientific discovery.

This paper introduces SciDisco, a scalable framework for training scientific discovery agents via process-verifiable environments, DAG-grounded trajectory synthesis, and turn-level reinforcement learning. The proposed SciDisco-14B model achieves state-of-the-art on hypothesis-driven scientific data analysis benchmarks.

This paper studies the LSR-Synth benchmark and finds that a fixed vocabulary covers most tasks, while language-model-generated candidates rarely expand the set of solvable instances except when vocabulary coverage is disrupted.

This position paper argues that AI agents in scientific teams should be studied as human-agent systems to enhance collaboration and mitigate risks such as reduced diversity in scientific inquiry.

OpenAI launches ChatGPT for Academic Researchers, granting 100,000 researchers free access to frontier models including GPT-5.6 Sol Pro to accelerate scientific discovery, with a $250M commitment through 2027.

The Genesis Mission aims to create a domestic end-to-end ecosystem integrating AI, quantum computing, and nuclear energy to accelerate scientific discovery and address climate, energy, and health challenges.

IDEAgent introduces a multi-agent framework that treats research ideation as a Quality-Diversity search, jointly optimizing idea quality and diversity through lineage evolution, outperforming baselines by 3.89x on a novel joint metric.

MIT researchers will lead or contribute to 15 projects funded under the US Department of Energy's Genesis Mission, which aims to build an integrated science discovery platform using AI, supercomputing, and quantum systems.

SciForge is an open-source, AI-native multimodal workbench for scientific discovery that integrates search, reasoning, workflow execution, and evidence governance, demonstrated through eight end-to-end use cases including gene discovery and protein design.

This paper introduces Mycelium, an active shared workspace that automatically connects researchers and AI agents to enable networked intelligence in team science, evaluated in a multi-omics campaign.

Google DeepMind's essay explores how AI agents are transforming scientific discovery by generating hypotheses and designing experiments, while highlighting a growing validation bottleneck and proposing priorities for policymakers and funders.

This paper argues that current AI models are predictive but not explanatory, and proposes Mechanistic World Models as a new paradigm that places reusable mechanisms at the center of representation, computation, and learning to enable autonomous scientific discovery.

A preprint arguing that AI can map known biology but cannot perform 'kind formation'—the minting of new variables and constraints—which requires the embodied engagement of human scientists. The authors propose a curriculum to train scientists as 'detectors of the unparameterized' to complement AI in biological discovery.

GAE introduces a framework combining graph neural networks, reinforcement learning, and LLM fine-tuning to overcome bottlenecks in evolutionary program search, achieving state-of-the-art performance on symbolic regression for complex nonlinear oscillator systems.

A physicist named Yuji Tachikawa is using Claude Fable (an AI model) to make progress in mathematical physics, sparking discussion about a new era of AI-assisted scientific discovery.