arXiv:2505. 04997v3 Announce Type: replace Abstract: Computational fluid dynamics (CFD) has been the main workhorse of computational physics, yet its steep learning curve and fragmented, multi-stage workflow create significant barriers to entry.
arXiv:2603. 29152v2 Announce Type: replace Abstract: Metal-organic frameworks (MOFs) offer a vast design space, and as such, computational simulations play a critical role in predicting their structural and physicochemical properties.
The paper introduces complete cyclic subtask graphs for large language model agents, enabling a workflow controller where all subtasks are fully connected and a unified agent selects transitions based on natural‑language criteria. It evaluates task‑specific and benchmark‑generic cyclic graphs on TextCraft, ALFWorld, and Finance‑Agent, comparing them to ReAct and dependency‑directed workflows, and identifies three distinct workflow signatures that influence the effectiveness of cyclic routing. The study also provides a workflow‑signature matrix, robustness analysis, token‑cost accounting, and failure‑mode structure, concluding that cyclic subtask graphs serve as a diagnostic tool to determine when flexible backtracking is worthwhile versus when simpler controllers suffice.
arXiv:2608. 15591v1 Announce Type: new Abstract: Large Language Model (LLM) agents deployed in production environments face a fundamental tension: the agent's behavior is frozen at deployment time, while the business rules and edge cases it must handle continue to evolve.
The paper introduces AutoMat, a benchmark designed to test large language model (LLM) coding agents on their ability to reproduce claims from computational materials science. AutoMat presents three challenges: reconstructing underspecified procedures, navigating specialized toolchains, and assessing whether the evidence supports a claim. Experiments show that current LLM agents achieve low success rates, with the best setting reaching only 53%, and failures stem mainly from incomplete procedures, methodological deviations, and execution fragility.
arXiv:2606. 21228v2 Announce Type: replace Abstract: The capabilities of frontier Large Language Models (LLMs) continue to advance, with different providers increasingly specializing in distinct domains.
arXiv:2608. 10039v1 Announce Type: new Abstract: Agentic workflows have become an important abstraction for building reliable LLM-based automation systems by organizing large language models (LLMs), tools, and control logic into explicit execution structures.
arXiv:2606. 00708v1 Announce Type: new Abstract: Automated data science is a structured model-selection problem.
arXiv:2609.01045v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities as powerful components in agentic systems, enabling sophisticated reasoning and...
The paper introduces Pufibara, an agent harness designed to maintain engineering state and evidence across revisions in Modelica-based physical system modeling. It also presents a 232-task Modelica Agent Workflow Benchmark covering model repair, generation, and tuning, evaluated by an external benchmark-owned evaluator. Experiments show Pufibara outperforms Claude Code in task success and resource efficiency across two LLM backends.
arXiv:2607. 14989v1 Announce Type: cross Abstract: Large language models are increasingly evolving from text generators into general agents capable of understanding user requests, invoking external tools, and completing complex tasks through interaction.
OptSkills is an archetype‑centric agent that learns and reasons about optimization problems using large language models. It clusters problems by underlying archetypes, explores diverse modeling and solver configurations within each cluster, and distills successful trajectories into reusable workflow‑level skills. The system achieves state‑of‑the‑art accuracy on multiple datasets, outperforming prior methods on challenging benchmarks such as MIPLIB‑NL and OOD NLCO.
arXiv:2608. 03600v1 Announce Type: new Abstract: Partial differential equations (PDEs) become actionable in science and engineering not as isolated formulae, but as executable workflows that connect modelling assumptions, governing equations, numerical solvers, diagnostics, and decisions.