554 papers
This collection explores the fascinating world of chemistry and physics, where scientists investigate the fundamental building blocks of matter and the laws that govern our universe. From understanding how atoms bond to create new materials to unraveling the mysteries of quantum mechanics, these studies form the backbone of modern physical science.
At Gist.Science, we process every new preprint in this category directly from arXiv, ensuring you have immediate access to cutting-edge research before formal publication. Each paper is accompanied by both a clear, plain-language explanation for general readers and a detailed technical summary for experts, bridging the gap between complex data and human understanding.
Below are the latest papers in the chemistry and physics section, curated to help you stay informed on the most recent discoveries.
AgenticData is an innovative agentic system that enables autonomous analysis of heterogeneous structured and unstructured data through natural language queries by leveraging a multi-agent collaboration strategy with feedback-driven planning and semantic optimization to achieve superior accuracy over existing methods.
This paper presents RNA-KG v2.0, an enhanced RNA-centered knowledge graph that integrates approximately 100 million manually curated interactions from 91 sources with standardized contextual properties and enriched node attributes to enable advanced context-aware queries and link prediction in RNA research.
Aurora DSQL is a serverless, multi-region active-active SQL database that achieves elastic scalability and strong consistency by decoupling compute, storage, and transaction coordination to minimize cross-region latency through commit-time adjudication.
ReBound is a differentially private framework for interactive decision support that reduces or eliminates additional privacy costs by reusing cached results from previous queries through a novel cache graph structure and negotiation mechanism, while maintaining formal utility guarantees.
This paper investigates the fine-grained complexity of lexicographic direct access to join query answers under functional dependencies, establishing lower and upper bounds that fully characterize when linear preprocessing time suffices for polylogarithmic access, while demonstrating that simple FD incorporation works for unary dependencies but fails for general cases, necessitating an information-theoretic decomposition approach.
PRomop introduces an open-source, flattened longitudinal patient record built on the OMOP Common Data Model that collapses complex clinical histories into a single decision-ready row, significantly accelerating downstream applications like clinical trial matching and analytics by eliminating the need for repeated data derivation.
This paper introduces PathFinder, a unified and highly efficient approach for processing path queries in modern graph languages that leverages compact path representation and pipelined execution to achieve stable performance and outperform existing graph engines by an order of magnitude.
The paper introduces PLuRel, a lightweight framework for synthesizing diverse multi-table relational databases that demonstrates, for the first time, that scaling synthetic data leads to predictable performance improvements and strong generalization for Relational Foundation Models.
The paper proposes GRAFT, a novel framework that models table retrieval in data lakes as a graph matching problem using an IGMS objective and an implicit Q-learning-based subgraph generation process to effectively integrate joinable and unionable tables, thereby significantly outperforming existing baselines in retrieval accuracy and evidence sufficiency.
This paper introduces CRINN, a novel paradigm that leverages contrastive reinforcement learning to automatically generate high-performance approximate nearest neighbor search algorithms by optimizing for execution speed while maintaining accuracy, achieving state-of-the-art results on multiple benchmarks and demonstrating the potential of LLMs for automating complex algorithmic optimization.