Perceptual Self-Reflection in Agentic Physics Simulation Code Generation

• URL: http://arxiv.org/abs/2602.12311v1
• Date: Thu, 12 Feb 2026 15:48:33 GMT
• Title: Perceptual Self-Reflection in Agentic Physics Simulation Code Generation
• Authors: Prashant Shende, Bradley Camburn,
• Abstract summary: We present a framework for generating physics simulation code from natural language descriptions.<n>Key innovation is perceptual validation, which analyzes rendered animation frames using a vision-capable language model.<n>We evaluate the system across seven domains including classical mechanics, fluid dynamics, thermodynamics, electromagnetics, wave physics, reaction-diffusion systems, and non-physics data visualization.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a multi-agent framework for generating physics simulation code from natural language descriptions, featuring a novel perceptual self-reflection mechanism for validation. The system employs four specialized agents: a natural language interpreter that converts user requests into physics-based descriptions; a technical requirements generator that produces scaled simulation parameters; a physics code generator with automated self-correction; and a physics validator that implements perceptual self-reflection. The key innovation is perceptual validation, which analyzes rendered animation frames using a vision-capable language model rather than inspecting code structure directly. This approach addresses the ``oracle gap'' where syntactically correct code produces physically incorrect behavior--a limitation that conventional testing cannot detect. We evaluate the system across seven domains including classical mechanics, fluid dynamics, thermodynamics, electromagnetics, wave physics, reaction-diffusion systems, and non-physics data visualization. The perceptual self-reflection architecture demonstrates substantial improvement over single-shot generation baselines, with the majority of tested scenarios achieving target physics accuracy thresholds. The system exhibits robust pipeline stability with consistent code self-correction capability, operating at approximately \$0.20 per animation. These results validate our hypothesis that feeding visual simulation outputs back to a vision-language model for iterative refinement significantly outperforms single-shot code generation for physics simulation tasks and highlights the potential of agentic AI to support engineering workflows and physics data generation pipelines.

Related papers

• Learning Hamiltonians for solid-state quantum simulators [0.0]
  We introduce a generalizable framework for learning to identify effective Hamiltonians directly from experimental data in solid-state quantum systems.<n>Our approach is based on a physics-informed neural network architecture that embeds physical constraints directly into the model structure.
  arXiv  Detail & Related papers  (2026-03-03T11:37:43Z)
• D-REX: Differentiable Real-to-Sim-to-Real Engine for Learning Dexterous Grasping [66.22412592525369]
  We introduce a real-to-sim-to-real engine that leverages the Gaussian Splat representations to build a differentiable engine.<n>We show that our engine achieves accurate and robust performance in mass identification across various object geometries and mass values.<n>Those optimized mass values facilitate force-aware policy learning, achieving superior and high performance in object grasping.
  arXiv  Detail & Related papers  (2026-03-01T15:32:04Z)
• GRACE: an Agentic AI for Particle Physics Experiment Design and Simulation [0.0]
  GRACE is a simulation-native agent for autonomous experimental design in high-energy and nuclear physics.<n>It autonomously explores design modifications using first-principles Monte Carlo methods.<n>It evaluates candidate designs through repeated simulation, physics-motivated utility functions, and budget-aware escalation.
  arXiv  Detail & Related papers  (2026-01-31T01:12:55Z)
• PhysRVG: Physics-Aware Unified Reinforcement Learning for Video Generative Models [100.65199317765608]
  Physical principles are fundamental to realistic visual simulation, but remain a significant oversight in transformer-based video generation.<n>We introduce a physics-aware reinforcement learning paradigm for video generation models that enforces physical collision rules directly in high-dimensional spaces.<n>We extend this paradigm to a unified framework, termed Mimicry-Discovery Cycle (MDcycle), which allows substantial fine-tuning.
  arXiv  Detail & Related papers  (2026-01-16T08:40:10Z)
• SIMPACT: Simulation-Enabled Action Planning using Vision-Language Models [60.80050275581661]
  Vision-Language Models (VLMs) exhibit remarkable common-sense and semantic reasoning capabilities.<n>They lack a grounded understanding of physical dynamics.<n>We present S, a test-time, SIMulation-enabled ACTion Planning framework.<n>Our method demonstrates state-of-the-art performance on five challenging, real-world rigid-body and deformable manipulation tasks.
  arXiv  Detail & Related papers  (2025-12-05T18:51:03Z)
• Adversarial Disentanglement by Backpropagation with Physics-Informed Variational Autoencoder [0.0]
  Inference and prediction under partial knowledge of a physical system is challenging.<n>We propose a physics-informed variational autoencoder architecture that combines the interpretability of physics-based models with the flexibility of data-driven models.
  arXiv  Detail & Related papers  (2025-06-16T16:18:25Z)
• Unreal Robotics Lab: A High-Fidelity Robotics Simulator with Advanced Physics and Rendering [4.760567755149477]
  This paper presents a novel simulation framework that integrates the Unreal Engine's advanced rendering capabilities with MuJoCo's high-precision physics simulation.<n>Our approach enables realistic robotic perception while maintaining accurate physical interactions.<n>We benchmark visual navigation and SLAM methods within our framework, demonstrating its utility for testing real-world robustness in controlled yet diverse scenarios.
  arXiv  Detail & Related papers  (2025-04-19T01:54:45Z)
• Automating Physics-Based Reasoning for SysML Model Validation [2.8994675888853516]
  Current methods excel at checking information flow and component interactions, ensuring consistency, and identifying dependencies within Systems Modeling Language (SysML) models.<n>This paper presents an approach that leverages existing research on function representation, including formal languages, graphical representations, and reasoning algorithms, and integrates them with physics-based verification techniques.<n>Four case studies are inspected to illustrate the model's practicality and effectiveness in performing physics-based reasoning on systems modeled in SysML.
  arXiv  Detail & Related papers  (2025-01-30T17:24:38Z)
• GausSim: Foreseeing Reality by Gaussian Simulator for Elastic Objects [55.02281855589641]
  GausSim is a novel neural network-based simulator designed to capture the dynamic behaviors of real-world elastic objects represented through Gaussian kernels.<n>We leverage continuum mechanics and treat each kernel as a Center of Mass System (CMS) that represents continuous piece of matter.<n>In addition, GausSim incorporates explicit physics constraints, such as mass and momentum conservation, ensuring interpretable results and robust, physically plausible simulations.
  arXiv  Detail & Related papers  (2024-12-23T18:58:17Z)
• DeepSimHO: Stable Pose Estimation for Hand-Object Interaction via Physics Simulation [81.11585774044848]
  We present DeepSimHO, a novel deep-learning pipeline that combines forward physics simulation and backward gradient approximation with a neural network. Our method noticeably improves the stability of the estimation and achieves superior efficiency over test-time optimization.
  arXiv  Detail & Related papers  (2023-10-11T05:34:36Z)
• Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling [86.9726984929758]
  We focus on the integration of incomplete physics models into deep generative models. We propose a VAE architecture in which a part of the latent space is grounded by physics. We demonstrate generative performance improvements over a set of synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2021-02-25T20:28:52Z)

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.