PCEval: A Benchmark for Evaluating Physical Computing Capabilities of Large Language Models

• URL: http://arxiv.org/abs/2601.02404v1
• Date: Wed, 31 Dec 2025 22:34:27 GMT
• Title: PCEval: A Benchmark for Evaluating Physical Computing Capabilities of Large Language Models
• Authors: Inpyo Song, Eunji Jeon, Jangwon Lee,
• Abstract summary: Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains.<n>textscPCEval is the first benchmark in physical computing that enables a fully automatic evaluation of the capabilities of LLM.
• Score: 2.99040583186815
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains, including software development, education, and technical assistance. Among these, software development is one of the key areas where LLMs are increasingly adopted. However, when hardware constraints are considered-for instance, in physical computing, where software must interact with and control physical hardware -their effectiveness has not been fully explored. To address this gap, we introduce \textsc{PCEval} (Physical Computing Evaluation), the first benchmark in physical computing that enables a fully automatic evaluation of the capabilities of LLM in both the logical and physical aspects of the projects, without requiring human assessment. Our evaluation framework assesses LLMs in generating circuits and producing compatible code across varying levels of project complexity. Through comprehensive testing of 13 leading models, \textsc{PCEval} provides the first reproducible and automatically validated empirical assessment of LLMs' ability to reason about fundamental hardware implementation constraints within a simulation environment. Our findings reveal that while LLMs perform well in code generation and logical circuit design, they struggle significantly with physical breadboard layout creation, particularly in managing proper pin connections and avoiding circuit errors. \textsc{PCEval} advances our understanding of AI assistance in hardware-dependent computing environments and establishes a foundation for developing more effective tools to support physical computing education.

Related papers

• CelloAI Benchmarks: Toward Repeatable Evaluation of AI Assistants [2.2811622267552014]
  Large Language Models (LLM) are increasingly used for software development.<n>Existing benchmarks for LLM-based coding assistance do not reflect the constraints of High Energy Physics and High Performance Computing software.<n>This paper develops practical, repeatable benchmarks that quantify LLM performance on HEP/ HPC-relevant tasks.
  arXiv  Detail & Related papers  (2026-03-01T11:16:50Z)
• An Agentic Framework for Autonomous Materials Computation [70.24472585135929]
  Large Language Models (LLMs) have emerged as powerful tools for accelerating scientific discovery.<n>Recent advances integrate LLMs into agentic frameworks, enabling retrieval, reasoning, and tool use for complex scientific experiments.<n>Here, we present a domain-specialized agent designed for reliable automation of first-principles materials computations.
  arXiv  Detail & Related papers  (2025-12-22T15:03:57Z)
• LLMs for Analog Circuit Design Continuum (ACDC) [10.15990970510281]
  Large Language Models (LLMs) and transformer architectures have shown impressive reasoning and generation capabilities.<n>This work focuses on AI-assisted design where humans remain in the loop.<n>Our results highlight key reliability challenges, including sensitivity to data format, instability in generated designs, and limited generalization to unseen circuit configurations.
  arXiv  Detail & Related papers  (2025-12-09T23:57:28Z)
• Testing the Untestable? An Empirical Study on the Testing Process of LLM-Powered Software Systems [0.0]
  This study explores how large language models are tested in the context of real-world application development.<n>Case study was conducted using 99 individual reports written by students who built and deployed LLM-powered applications as part of a university course.<n>Results: Testing LLM-powered systems required adaptations to traditional verification methods, blending source-level reasoning with behavior-aware evaluations.
  arXiv  Detail & Related papers  (2025-07-31T22:39:24Z)
• Computational Thinking Reasoning in Large Language Models [69.28428524878885]
  Computational Thinking Model (CTM) is a novel framework that incorporates computational thinking paradigms into large language models (LLMs)<n>Live code execution is seamlessly integrated into the reasoning process, allowing CTM to think by computing.<n>CTM outperforms conventional reasoning models and tool-augmented baselines in terms of accuracy, interpretability, and generalizability.
  arXiv  Detail & Related papers  (2025-06-03T09:11:15Z)
• SIMCOPILOT: Evaluating Large Language Models for Copilot-Style Code Generation [5.880496520248658]
  SIMCOPILOT is a benchmark that simulates the role of large language models (LLMs) as interactive, "copilot"-style coding assistants.<n>The benchmark comprises dedicated sub-benchmarks for Java (SIMCOPILOTJ) and Python.
  arXiv  Detail & Related papers  (2025-05-21T04:59:44Z)
• Evaluating Large Language Models for Real-World Engineering Tasks [75.97299249823972]
  This paper introduces a curated database comprising over 100 questions derived from authentic, production-oriented engineering scenarios.<n>Using this dataset, we evaluate four state-of-the-art Large Language Models (LLMs)<n>Our results show that LLMs demonstrate strengths in basic temporal and structural reasoning but struggle significantly with abstract reasoning, formal modeling, and context-sensitive engineering logic.
  arXiv  Detail & Related papers  (2025-05-12T14:05:23Z)
• Computational Reasoning of Large Language Models [51.629694188014064]
  We introduce textbfTuring Machine Bench, a benchmark to assess the ability of Large Language Models (LLMs) to execute reasoning processes.<n> TMBench incorporates four key features: self-contained and knowledge-agnostic reasoning, a minimalistic multi-step structure, controllable difficulty, and a theoretical foundation based on Turing machine.
  arXiv  Detail & Related papers  (2025-04-29T13:52:47Z)
• Do Large Language Models Understand Performance Optimization? [0.9320657506524149]
  Large Language Models (LLMs) have emerged as powerful tools for software development tasks such as code completion, translation, and optimization.<n>This paper presents a benchmark suite encompassing multiple critical HPC computational motifs to evaluate the performance of code optimized by state-of-the-art LLMs.
  arXiv  Detail & Related papers  (2025-03-17T23:30:23Z)
• Exploring Code Language Models for Automated HLS-based Hardware Generation: Benchmark, Infrastructure and Analysis [14.458529723566379]
  Large language models (LLMs) can be employed for programming languages such as Python and C++.<n>This paper explores leveraging LLMs to generate High-Level Synthesis (HLS)-based hardware design.
  arXiv  Detail & Related papers  (2025-02-19T17:53:59Z)
• Specifications: The missing link to making the development of LLM systems an engineering discipline [65.10077876035417]
  We discuss the progress the field has made so far-through advances like structured outputs, process supervision, and test-time compute.<n>We outline several future directions for research to enable the development of modular and reliable LLM-based systems.
  arXiv  Detail & Related papers  (2024-11-25T07:48:31Z)
• RITFIS: Robust input testing framework for LLMs-based intelligent software [6.439196068684973]
  RITFIS is the first framework designed to assess the robustness of intelligent software against natural language inputs. RITFIS adapts 17 automated testing methods, originally designed for Deep Neural Network (DNN)-based intelligent software. It demonstrates the effectiveness of RITFIS in evaluating LLM-based intelligent software through empirical validation.
  arXiv  Detail & Related papers  (2024-02-21T04:00:54Z)
• Technology Readiness Levels for Machine Learning Systems [107.56979560568232]
  Development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. We have developed a proven systems engineering approach for machine learning development and deployment. Our "Machine Learning Technology Readiness Levels" framework defines a principled process to ensure robust, reliable, and responsible systems.
  arXiv  Detail & Related papers  (2021-01-11T15:54:48Z)

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.