CelloAI Benchmarks: Toward Repeatable Evaluation of AI Assistants

• URL: http://arxiv.org/abs/2603.01051v1
• Date: Sun, 01 Mar 2026 11:16:50 GMT
• Title: CelloAI Benchmarks: Toward Repeatable Evaluation of AI Assistants
• Authors: Mohammad Atif, Kriti Chopra, Fang-Ying Tsai, Ozgur O. Kilic, Tianle Wang, Zhihua Dong, Douglas Benjamin, Charles Leggett, Meifeng Lin, Paolo Calafiura, Salman Habib,
• Abstract summary: 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.
• Score: 2.2811622267552014
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLM) are increasingly used for software development, yet existing benchmarks for LLM-based coding assistance do not reflect the constraints of High Energy Physics (HEP) and High Performance Computing (HPC) software. Code correctness must respect science constraints and changes must integrate into large, performance-critical codebases with complex dependencies and build systems. The primary contribution of this paper is the development of practical, repeatable benchmarks that quantify LLM performance on HEP/HPC-relevant tasks. We introduce three evaluation tracks -- code documentation benchmarks measure the ability of an LLM to generate Doxygen-style comments, code generation benchmarks evaluate end-to-end usability on representative GPU kernels, and graphical data analysis benchmarks evaluate vision-enabled LLMs. These benchmarks provide a unified framework for measuring progress in scientific coding assistance across documentation quality, code generation robustness, and multimodal validation analysis. By emphasizing repeatability, automated scoring, and domain-relevant failure modes, the suite enables fair comparisons of models and settings while supporting future work on methods that improve reliability for HEP/HPC software development.

Related papers

• Evaluating and Achieving Controllable Code Completion in Code LLM [89.64782747840225]
  We present the first instruction-guided code completion benchmark, Controllable Code Completion Benchmark (C3-Bench)<n>We reveal substantial gaps in instruction-following capabilities between open-source and advanced proprietary models during code completion tasks.<n>The resulting model, Qwen2.5-Coder-C3, achieves state-of-the-art performance on C3-Bench.
  arXiv  Detail & Related papers  (2026-01-22T11:40:04Z)
• PACIFIC: a framework for generating benchmarks to check Precise Automatically Checked Instruction Following In Code [1.1164117387254457]
  Large Language Model (LLM)-based code assistants have emerged as a powerful application of generative AI.<n>Key requirement for these systems is their ability to accurately follow user instructions.<n>We present PACIFIC, a novel framework designed to automatically generate benchmarks that rigorously assess sequential instruction-following and code dry-running capabilities.
  arXiv  Detail & Related papers  (2025-12-11T14:49:56Z)
• From Code Foundation Models to Agents and Applications: A Practical Guide to Code Intelligence [150.3696990310269]
  Large language models (LLMs) have transformed automated software development by enabling direct translation of natural language descriptions into functional code.<n>We provide a comprehensive synthesis and practical guide (a series of analytic and probing experiments) about code LLMs.<n>We analyze the code capability of the general LLMs (GPT-4, Claude, LLaMA) and code-specialized LLMs (StarCoder, Code LLaMA, DeepSeek-Coder, and QwenCoder)
  arXiv  Detail & Related papers  (2025-11-23T17:09:34Z)
• MERA Code: A Unified Framework for Evaluating Code Generation Across Tasks [56.34018316319873]
  We propose MERA Code, a benchmark for evaluating code for the latest code generation LLMs in Russian.<n>This benchmark includes 11 evaluation tasks that span 8 programming languages.<n>We evaluate open LLMs and frontier API models, analyzing their limitations in terms of practical coding tasks in non-English languages.
  arXiv  Detail & Related papers  (2025-07-16T14:31:33Z)
• Training Language Models to Generate Quality Code with Program Analysis Feedback [66.0854002147103]
  Code generation with large language models (LLMs) is increasingly adopted in production but fails to ensure code quality.<n>We propose REAL, a reinforcement learning framework that incentivizes LLMs to generate production-quality code.
  arXiv  Detail & Related papers  (2025-05-28T17:57:47Z)
• 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)
• TuRTLe: A Unified Evaluation of LLMs for RTL Generation [0.6010802600885173]
  We propose TuRTLe, a unified evaluation framework designed to assess LLMs across key RTL generation tasks.<n>We benchmark a diverse set of open LLMs and analyze their strengths and weaknesses in EDA-specific tasks.<n>Our results show that reasoning-based models, such as DeepSeek R1, consistently outperform others across multiple evaluation criteria.
  arXiv  Detail & Related papers  (2025-03-31T07:43:12Z)
• 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)
• CodeIF: Benchmarking the Instruction-Following Capabilities of Large Language Models for Code Generation [20.013757490442064]
  We introduce CodeIF, the first benchmark designed to assess the abilities of Large Language Models (LLMs) to adhere to task-oriented instructions.<n>CodeIF encompasses a broad range of tasks, including function synthesis, algorithmic instructions, and code explanation.<n>We conduct extensive experiments with LLMs, analyzing their strengths and limitations in meeting the demands of these tasks.
  arXiv  Detail & Related papers  (2025-02-26T14:19:49Z)
• A Survey on Evaluating Large Language Models in Code Generation Tasks [30.256255254277914]
  This paper provides a comprehensive review of the current methods and metrics used to evaluate the performance of Large Language Models (LLMs) in code generation tasks.<n>With the rapid growth in demand for automated software development, LLMs have demonstrated significant potential in the field of code generation.
  arXiv  Detail & Related papers  (2024-08-29T12:56:06Z)
• What's Wrong with Your Code Generated by Large Language Models? An Extensive Study [92.62952504133926]
  This study evaluated the performance of three leading closed-source LLMs and six popular open-source LLMs on three commonly used benchmarks.<n>We developed a taxonomy of bugs for incorrect codes and analyzed the root cause for common bug types.<n>We propose a novel training-free iterative method that introduces self-critique, enabling LLMs to critique and correct their generated code.
  arXiv  Detail & Related papers  (2024-07-08T17:27:17Z)
• Don't Make Your LLM an Evaluation Benchmark Cheater [142.24553056600627]
  Large language models(LLMs) have greatly advanced the frontiers of artificial intelligence, attaining remarkable improvement in model capacity. To assess the model performance, a typical approach is to construct evaluation benchmarks for measuring the ability level of LLMs. We discuss the potential risk and impact of inappropriately using evaluation benchmarks and misleadingly interpreting the evaluation results.
  arXiv  Detail & Related papers  (2023-11-03T14:59:54Z)

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.