CAX-Agent: A Lightweight Agent Harness for Reliable APDL Automation
CAX-Agent is a lightweight agent harness designed for MAPDL finite-element simulation automation, employing a three-layer architecture and a recovery ladder to enhance reliability. Experimental evaluation of three recovery strategies shows that the model-only strategy significantly outperforms rule-based and no-recovery approaches in completion rate, task score, and zero-intervention rate.
[2605.15218] CAX-Agent: A Lightweight Agent Harness for Reliable APDL Automation
[Submitted on 12 May 2026]
Title:CAX-Agent: A Lightweight Agent Harness for Reliable APDL Automation
View a PDF of the paper titled CAX-Agent: A Lightweight Agent Harness for Reliable APDL Automation, by Chenying Lin and Yichen Hai and Yi He and Ran Wang and Haiyan Qiang and Liang Yu
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Abstract:Large language models deployed for MAPDL finite-element simulation face practical reliability challenges: without structured execution control, tool encapsulation, and fault recovery, outputs may be inconsistent and task failures are common. The Agent Harness paradigm addresses this by inserting domain-specific orchestration middleware that manages tool lifecycles, workflow state, and recovery escalation. This paper presents the architecture of CAX-Agent, a lightweight agent harness purpose-built for MAPDL automation, and empirically evaluates one of its core components -- the recovery this http URL-Agent organizes execution into three layers -- LLM service, agent harness, and solver backend -- with a recovery ladder that escalates from deterministic rule patching through model-driven regeneration to context enrichment and human intervention. We evaluate three recovery strategies (no_recovery, rule_only, and model_only) on 50 standard structural benchmarks with three repeated runs per strategy (450 case-runs total). Two independent human raters score task completion under blind conditions; inter-rater agreement is strong (quadratic weighted Cohen's kappa = 0.84, 96 percent of score pairs within one point). Model_only achieves the best completion rate (0.9267), task score (3.59/4), total score (9.16/10), and zero-intervention rate (0.84), outperforming rule_only (0.7733, 3.17/4, 7.03/10, 0.00) and no_recovery (0.6933, 2.74/4, 5.60/10, 0.00) with large effect sizes (Cliff's delta = 0.81-0.87). The benchmark uses deliberately simple geometries to isolate recovery-policy effects; we discuss the scope of these findings and directions for broader validation.
Comments: 8 pages, 6 figures, IEEE conference format
Subjects:
Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
Cite as: arXiv:2605.15218 [cs.AI]
(or arXiv:2605.15218v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.15218
arXiv-issued DOI via DataCite
Submission history
From: Yichen Hai [view email] [v1] Tue, 12 May 2026 14:46:34 UTC (1,699 KB)
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