From Perception to Autonomous Computational Modeling: A Multi-Agent Approach

• URL: http://arxiv.org/abs/2604.06788v2
• Date: Sun, 12 Apr 2026 13:27:58 GMT
• Title: From Perception to Autonomous Computational Modeling: A Multi-Agent Approach
• Abstract summary: We present a solver-agnostic framework in which coordinated large language model (LLM) agents autonomously execute the complete computational mechanics workflow.<n>Agents are formalised as conditioned operators on a shared context space with quality gates that introduce conditional iteration between pipeline layers.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a solver-agnostic framework in which coordinated large language model (LLM) agents autonomously execute the complete computational mechanics workflow, from perceptual data of an engineering component through geometry extraction, material inference, discretisation, solver execution, uncertainty quantification, and code-compliant assessment, to an engineering report with actionable recommendations. Agents are formalised as conditioned operators on a shared context space with quality gates that introduce conditional iteration between pipeline layers. We introduce a mathematical framework for extracting engineering information from perceptual data under uncertainty using interval bounds, probability densities, and fuzzy membership functions, and introduce task-dependent conservatism to resolve the ambiguity of what `conservative' means when different limit states are governed by opposing parameter trends. The framework is demonstrated through a finite element analysis pipeline applied to a photograph of a steel L-bracket, producing a 171,504-node tetrahedral mesh, seven analyses across three boundary condition hypotheses, and a code-compliant assessment revealing structural failure with a quantified redesign. All results are presented as generated in the first autonomous iteration without manual correction, reinforcing that a professional engineer must review and sign off on any such analysis.

Related papers

• See, Hypothesize, Validate: Multimodal Agentic Framework for Discovering Governing PDEs [1.8315988392353102]
  textbfMAGE (textbfMultimodal textbfAgentic textbfGoverning textbfEquation Discovery) is an agentic framework that organizes PDE discovery as a textitconfidence governed hypothesis validation loop<n>Discovery iterates until the top candidate clears a user-specified threshold, providing a structured process with an explicit accept-reject protocol.
  arXiv  Detail & Related papers  (2026-08-28T03:20:33Z)
• Gatekeepers and Hallucinations: A Layered Evaluation Framework for LLM-Driven Quantum Circuit Generation [0.0]
  We present a layered evaluation framework for materials-informed Variational Quantum Eigensolver (VQE) circuit generation.<n>We surface a taxonomy of five distinct LLM failure modes (geometry hallucination, nonexistent API usage, integration failures, constraint violations, and plausible-but-unverifiable output)<n>A forensic audit of the evaluation platform's own source code further establishes that two apparent model failures originated in the harness through silent fallback-template substitution.
  arXiv  Detail & Related papers  (2026-06-16T19:17:54Z)
• Graph Transfer Learning via Shared Latent Geometry: Theory and Applications [12.735610064958712]
  Inference and control in engineered physical systems pay a heavy physics cost at deployment.<n>We propose an asymmetric two-pathway architecture that resolves both issues.<n>A teacher encoder consumes privileged dense states from a high-fidelity simulator and represents the system through operator-polynomial features stable under spectral perturbation.<n>A student encoder learns the same latent geometry from sparse field data and operator descriptors.
  arXiv  Detail & Related papers  (2026-05-30T13:00:13Z)
• Provably Secure Agent Guardrail [89.79561918065122]
  Existing defense architectures rely on empirical semantic guardrails and probabilistic large model adjudicators.<n>This paper proposes a new security paradigm for agents based on the fundamental limitations of logical reasoning.
  arXiv  Detail & Related papers  (2026-05-28T02:12:41Z)
• The Semantic Training Gap: Ontology-Grounded Tool Architectures for Industrial AI Agent Systems [0.0]
  Large language model (LLM)-based AI agents are increasingly deployed in manufacturing environments for analytics, quality management, and decision support.<n>These agents demonstrate statistical fluency with domain terminology but lack grounded understanding of operational semantics.<n>This paper identifies and formalizes the semantic training gap: a structural disconnect between how AI systems acquire domain vocabulary through training and how manufacturing operations define meaning through ontological relationships.
  arXiv  Detail & Related papers  (2026-05-11T20:48:18Z)
• From Agent Failure Paths to Quantified Residual Risk: A Compositional Framework for Resilient Agentic AI [8.947753534117298]
  Agentic AI is crossing trust boundaries faster than current risk models can represent.<n>We propose a mechanism-to-magnitude pipeline for resilient agentic and embodied AI.<n>We show the framework on two contrasting scenarios a real-time warehouse robot and a governance-instrumented financial-services agent.
  arXiv  Detail & Related papers  (2026-04-28T23:40:24Z)
• Dynamic analysis enhances issue resolution [53.50448142467294]
  DAIRA (Dynamic Analysis-enhanced Issue Resolution Agent) is an automated repair framework that embeds dynamic analysis into the agent's reasoning cycle.<n> Driven by a Test Tracing-Driven methodology, DAIRA utilizes lightweight monitors to extract critical runtime data.<n>When powered by Gemini 3 Flash Preview, DAIRA establishes a new state-of-the-art (SOTA) performance, achieving a 79.4% resolution rate on the SWE-bench Verified dataset.
  arXiv  Detail & Related papers  (2026-03-23T14:48:54Z)
• Beyond Benchmark Islands: Toward Representative Trustworthiness Evaluation for Agentic AI [45.21562889170875]
  We propose a systematic evaluation paradigm that characterizes agent trustworthiness over a scenario manifold.<n>The framework integrates four complementary components: static cognitive and policy analysis, (ii) interactive sandbox simulation, (iii) social-ethical alignment assessment, and (iv) a distribution-aware representative sampling engine.
  arXiv  Detail & Related papers  (2026-03-16T08:51:33Z)
• Agentic Problem Frames: A Systematic Approach to Engineering Reliable Domain Agents [0.0]
  Large Language Models (LLMs) are evolving into autonomous agents, yet current "frameless" development--relying on ambiguous natural language--leads to critical risks such as scope creep and open-loop failures.<n>This study proposes Agentic Problem Frames (APF), a systematic engineering framework that shifts focus from internal model intelligence to the structured interaction between the agent and its environment.
  arXiv  Detail & Related papers  (2026-02-22T06:32:32Z)
• Multi-Agent Procedural Graph Extraction with Structural and Logical Refinement [66.51979814832332]
  model formulates procedural graph extraction as a multi-round reasoning process with dedicated structural and logical refinement.<n>Experiments demonstrate that model achieves substantial improvements in both structural correctness and logical consistency over strong baselines.
  arXiv  Detail & Related papers  (2026-01-27T04:00:48Z)
• Automatic Building Code Review: A Case Study [6.530899637501737]
  Building officials face labor-intensive, error-prone, and costly manual reviews of design documents as projects increase in size and complexity.<n>This study introduces a novel agent-driven framework that integrates BIM-based data extraction with automated verification.
  arXiv  Detail & Related papers  (2025-10-03T00:30:14Z)
• AGENT-X: Adaptive Guideline-based Expert Network for Threshold-free AI-generated teXt detection [44.66668435489055]
  AGENT-X is a zero-shot multi-agent framework for AI-generated text detection.<n>We organize detection guidelines into semantic, stylistic, and structural dimensions, each independently evaluated by specialized linguistic agents.<n>A meta agent integrates these assessments through confidence-aware aggregation, enabling threshold-free, interpretable classification.<n>Experiments on diverse datasets demonstrate that AGENT-X substantially surpasses state-of-the-art supervised and zero-shot approaches in accuracy, interpretability, and generalization.
  arXiv  Detail & Related papers  (2025-05-21T08:39:18Z)
• Graph Exploration for Effective Multi-agent Q-Learning [46.723361065955544]
  This paper proposes an exploration technique for multi-agent reinforcement learning (MARL) with graph-based communication among agents. We assume the individual rewards received by the agents are independent of the actions by the other agents, while their policies are coupled. In the proposed framework, neighbouring agents collaborate to estimate the uncertainty about the state-action space in order to execute more efficient explorative behaviour.
  arXiv  Detail & Related papers  (2023-04-19T10:28:28Z)
• Self-Supervised Likelihood Estimation with Energy Guidance for Anomaly Segmentation in Urban Scenes [40.63116482332324]
  We design an energy-guided self-supervised framework for anomaly segmentation.<n>We exploit the strong context-dependent nature of the segmentation task.<n>Based on the proposed estimators, we devise an adaptive self-supervised training framework.
  arXiv  Detail & Related papers  (2023-02-14T03:54:32Z)
• Reinforcement Learning with a Terminator [80.34572413850186]
  We learn the parameters of the TerMDP and leverage the structure of the estimation problem to provide state-wise confidence bounds. We use these to construct a provably-efficient algorithm, which accounts for termination, and bound its regret.
  arXiv  Detail & Related papers  (2022-05-30T18:40:28Z)
• Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Models [77.34726150561087]
  We define explainability through the interpretability of the explanations and the faithfulness of the explainability model in the field of process outcome prediction. This paper contributes a set of guidelines named X-MOP which allows selecting the appropriate model based on the event log specifications.
  arXiv  Detail & Related papers  (2022-03-30T05:59:50Z)
• A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings [113.15416137912399]
  We propose and analyze a novel theoretical and algorithmic framework for structured prediction. We study a large class of loss functions that implicitly defines a suitable geometry on the problem. When dealing with output spaces with infinite cardinality, a suitable implicit formulation of the estimator is shown to be crucial.
  arXiv  Detail & Related papers  (2020-02-13T10:30:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.