arXiv (All)
open access
Reliable LLM-Generated Programs for High-Energy Physics Experiments through Graph-Grounded Software Knowledge
Extracting physics information from modern particle-physics experiments requires multistage analyses implemented on top of large and highly interconnected software ecosystems. General-purpose large language models (LLMs) often produce unreliable programs for such tasks because a user request alone r
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Reliable LLM-Generated Programs for High-Energy Physics Experiments through Graph-Grounded Software Knowledge#627752
- Energy-Efficient Code Generation Using Large Language Models: A Systematic Literature Review#1048583
- ChainSWE: Benchmarking Coding Agents on Multi-Bug Software Maintenance#819420
- ChainSWE: Benchmarking Coding Agents on Multi-Bug Software Maintenance#809427
- Supporting Industrial Test-Failure Analysis with LLM-Based Systems: An Experience Report#1036232
Record · ID 821073
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive