Latest AI for Science Research Papers

Research on applying AI to scientific discovery, drug design, materials science, and scientific computing.

9 Papers

Showing 9 of 9 papers

MOOSE-Star: Unlocking Tractable Training for Scientific Discovery by Breaking the Complexity Barrier

Zonglin Yang, Lidong Bing

While large language models (LLMs) show promise in scientific discovery, existing research focuses on inference or feedback-driven training, leaving the direct modeling of the generative reasoning process, P(hypothesis|background) (P(h|b)), unexplored. We demonstrate that directly training P(h|b) is...

large language modelsgenerative reasoningprobabilistic equation of discoverymotivation-guided hierarchical searchbounded composition+2 more

Mar 4, 202683

Qute: Towards Quantum-Native Database

Muzhi Chen, Xuanhe Zhou, Wei Zhou +7 more

This paper envisions a quantum database (Qute) that treats quantum computation as a first-class execution option. Unlike prior simulation-based methods that either run quantum algorithms on classical machines or adapt existing databases for quantum simulation, Qute instead (i) compiles an extended f...

Feb 16, 202611

Single-minus gluon tree amplitudes are nonzero

Alfredo Guevara, Alexandru Lupsasca, David Skinner +2 more

Single-minus tree-level n-gluon scattering amplitudes are reconsidered. Often presumed to vanish, they are shown here to be nonvanishing for certain "half-collinear" configurations existing in Klein space or for complexified momenta. We derive a piecewise-constant closed-form expression for the deca...

Feb 12, 20266

BABE: Biology Arena BEnchmark

Junting Zhou, Jin Chen, Linfeng Hao +10 more

The rapid evolution of large language models (LLMs) has expanded their capabilities from basic dialogue to advanced scientific reasoning. However, existing benchmarks in biology often fail to assess a critical skill required of researchers: the ability to integrate experimental results with contextu...

large language modelsbiological AI systemsexperimental reasoningcausal reasoningcross-scale inference+2 more

Feb 5, 20268

EEG Foundation Models: Progresses, Benchmarking, and Open Problems

Dingkun Liu, Yuheng Chen, Zhu Chen +5 more

Electroencephalography (EEG) foundation models have recently emerged as a promising paradigm for brain-computer interfaces (BCIs), aiming to learn transferable neural representations from large-scale heterogeneous recordings. Despite rapid progresses, there lacks fair and comprehensive comparisons o...

electroencephalographybrain-computer interfacesfoundation modelspre-training objectivesself-supervised pre-training+5 more

Jan 25, 202618

Learning to Discover at Test Time

Mert Yuksekgonul, Daniel Koceja, Xinhao Li +8 more

How can we use AI to discover a new state of the art for a scientific problem? Prior work in test-time scaling, such as AlphaEvolve, performs search by prompting a frozen LLM. We perform reinforcement learning at test time, so the LLM can continue to train, but now with experience specific to the te...

reinforcement learningtest-time trainingcontinual learningsearch subroutinelearning objective+2 more

Jan 22, 202635

A^3-Bench: Benchmarking Memory-Driven Scientific Reasoning via Anchor and Attractor Activation

Jian Zhang, Yu He, Zhiyuan Wang +5 more

Scientific reasoning relies not only on logical inference but also on activating prior knowledge and experiential structures. Memory can efficiently reuse knowledge and enhance reasoning consistency and stability. However, existing benchmarks mainly evaluate final answers or step-by-step coherence, ...

Jan 14, 202615

Beyond Static Tools: Test-Time Tool Evolution for Scientific Reasoning

Jiaxuan Lu, Ziyu Kong, Yemin Wang +10 more

The central challenge of AI for Science is not reasoning alone, but the ability to create computational methods in an open-ended scientific world. Existing LLM-based agents rely on static, pre-defined tool libraries, a paradigm that fundamentally fails in scientific domains where tools are sparse, h...

LLM-based agentstool librariesscientific reasoningcomputational methodstest-time tool evolution+5 more

Jan 12, 202643

Sci-Reasoning: A Dataset Decoding AI Innovation Patterns

Jiachen Liu, Maestro Harmon, Zechen Zhang

While AI innovation accelerates rapidly, the intellectual process behind breakthroughs -- how researchers identify gaps, synthesize prior work, and generate insights -- remains poorly understood. The lack of structured data on scientific reasoning hinders systematic analysis and development of AI re...

Jan 8, 20263

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