This paper compares two agentic AI systems, Claude Code and Codex, on a gravitational wave data analysis pipeline, finding that while both achieve scientific convergence, they exhibit vastly different…

VESTA introduces a novel agent framework that enhances Visual Language Models (VLMs) by equipping them with a dynamic, reusable toolkit of diagnostic and statistical tools, significantly improving aut…

MOOSE-Copilot is a novel web-based framework that unifies scientific hypothesis discovery by formalizing human-AI interaction, significantly improving performance over autonomous LLM baselines.

The paper proposes Multi-Agent Computer Use (MACU) systems, which significantly improve performance on complex, long-horizon tasks by enabling parallel execution and dynamic task decomposition compare…

This case study demonstrates that in complex scientific software development, human domain expertise and careful supervision are more critical to ensuring the trustworthiness of AI-generated code than…

This paper introduces a machine learning model, RuBR, and a methodology to reliably distinguish genuine astronomical transients from spurious detections for the upcoming Roman Space Telescope's data p…

The paper proposes using geometric metrics, specifically eigenspace alignment, to monitor the structural integrity of large behavioral populations, demonstrating its effectiveness in detecting network…

The paper empirically and theoretically demonstrates that incorporating Lamarckian and Baldwinian mechanisms into evolutionary algorithms significantly outperforms standard Darwinian evolution, especi…

The paper validates a specialized mathematical metric (the Burau-Lyapunov exponent) designed for detecting privilege escalation in cloud IAM graphs by applying it to an unrelated physical system: sola…

The paper formally models structure-informed multiple sequence alignment (MSA-S) as an NP-complete optimization problem, establishing a strong computational complexity baseline for the field.

This paper presents an algorithmic framework for exhaustively generating and tabulating knot and link diagrams on the thickened torus.

The paper introduces Andes, a framework that treats data generation as a plug-and-play agent skill, enabling autonomous alignment of LLMs by providing an intelligent, closed-loop data synthesis interf…

The paper introduces Hyperparam, a set of lightweight JavaScript libraries designed to enable direct, model-aware querying of unstructured data (like agent traces) within client-side AI applications.

The paper introduces Compass, an expert-guided LLM agent framework that successfully extracts and integrates thousands of previously inaccessible marine lead records from vast corpora of scientific pa…

This paper analyzes the performance and explainability requirements of evolutionary algorithms when applied to complex, real-world physics-informed optimization problems, identifying a gap between cur…

The paper proposes HADT, a novel transformer-based architecture using differential attention and relational tokenization, to enable adaptive and real-time autonomous resource management for heterogene…

The paper conducts a runtime analysis of the Strength Pareto Evolutionary Algorithm 2 (SPEA2) and proposes an improved variant, SPEA2$^+$, to address its limitations in handling dominated solutions.

OrbitBFT introduces a novel two-stage hierarchical BFT consensus protocol that enables scalable and robust Byzantine Fault-Tolerant coordination for large-scale Low Earth Orbit satellite constellation…

The paper introduces a subgrid marching tetrahedra scheme that accurately recovers complex, intersection-free manifold meshes from tetrahedral grids, overcoming limitations of classic marching methods…