The paper introduces TabPrep, a feature engineering pipeline that systematically improves performance across various tabular machine learning models by addressing structural data patterns ignored by c…

This paper introduces StructureClaw, an artifact-centered workbench for evaluating structural-engineering agents, and presents StructureClaw-Bench, an executable benchmark for testing these agents.

The paper introduces NewtPhys, a novel 4D dataset of real-world scenes with dense physical annotations, to systematically evaluate and reveal the limitations of foundation models in low-level Newtonia…

The paper introduces FinBoardBench, a novel evaluation suite using financial board games to demonstrate that current LLMs, despite strong static reasoning, fail at complex, dynamic wealth management a…

The paper introduces AbaqusAgent, a multi-AI-agent framework that uses large language models to translate natural language instructions into executable Finite Element Analysis (FEA) simulations using…

The BEAMS initiative establishes comprehensive benchmarks and evaluates AI tools for modeling and simulation, finding that current AI tools excel at qualitative discussion tasks but struggle with comp…

The paper introduces MUSE, a comprehensive benchmark that evaluates Text-to-CAD generation by assessing complex assemblies based on functionality, manufacturability, and assemblability, moving beyond…

This paper introduces a new benchmark dataset and evaluation framework for 'data snapshot extraction,' focusing on identifying and localizing semantically meaningful analytical artifacts within operat…

TabChange proposes a novel framework to generate natural and minimally altered counterfactual instances in tabular data by precisely controlling attribute modifications based on their relationship str…

The paper introduces Geometry-Aware Tabular Diffusion (GATD), a method that enhances tabular data synthesis by explicitly incorporating pairwise geometric relationships (angles and lengths) into the d…

VFEAgent is a novel multi-agent framework that automates the entire Finite Element Analysis (FEA) workflow, achieving high success rates in generating complete and physically valid simulations directl…

The paper proposes a flexible meta-programming framework to declaratively operationalize and explore varied temporal logics, such as TEL, MEL, and DEL, within standard Answer Set Programming systems.

The paper introduces BlueFin, a challenging benchmark for evaluating LLM agents on complex financial spreadsheet tasks, finding that even frontier models perform poorly, scoring less than 50% on avera…

The paper introduces AutoformBot, a multi-agent system that successfully autoformalizes a large corpus of open-access graduate-level mathematics textbooks into a verified library in Lean 4, demonstrat…

The paper introduces PRAXIS, a novel algorithm that efficiently approximates the computation of 'Rashomon sets' for decision trees, significantly reducing memory and runtime complexity.

The paper introduces SkillBrew, a multi-objective framework that treats skill bank curation as a constrained optimization problem to build efficient and well-curated skill repositories for LLM agents.

The paper introduces E-Bench, a synthetic benchmark for evaluating multi-step tool use in Large Language Models across three product domains.

SpecDB is a novel system that uses LLMs to synthesize highly customized, purpose-built relational databases, achieving performance comparable to commercial systems while significantly reducing code si…

This paper proposes an automated method to generate complete PDDL planning problems directly from Asset Administration Shell (AAS) capability models, eliminating the need for specialized planning expe…

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.