arXiv (All)
open access
SciWalker: Synthesizing Scientific Coding Problems with Operator Graphs and Execution Feedback
Improving the scientific coding capabilities of large language models (LLMs) requires high-quality training data. However, such data remain scarce because manually authoring realistic problems is costly and time-consuming, while systematically covering diverse scientific domains and algorithmic comb
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
- Twelve quick tips for AI-assisted coding in science.#412256
- An Agentic Evaluation Framework for AI-Generated Scientific Code in PETSc#998144
- Skill Issue: Lessons from Optimizing Repository SKILLs for Coding Agents#1070213
- AutoOR: Scalably Post-training LLMs to Autoformulate Operations Research Problems#652276
- AutoOR: Scalably Post-training LLMs to Autoformulate Operations Research Problems#650823
Record · ID 1078125
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