arXiv (All)
open access
Teaching LLMs to Generate Challenging MILP Instances via Solver Feedback
Generating optimization instances that are both feasible and computationally challenging is crucial for benchmarking solvers and training learning-based optimization algorithms. Existing non-LLM generators rely on seed instances or parameter tuning, resulting in high test-time computational cost, wh
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
- LLM-Assisted Repository-Level Generation with Structured Spec-Driven Engineering#155384
- CodeFlowBench: A Multi-turn, Iterative Benchmark for Complex Code Generation#144281
- Benchmarking the Titans: A Multi-Dimensional Empirical Evaluation of LLM Code Generation Quality in the .NET Ecosystem#482210
- StarOR: Synergizing Tree Search and Test-Time Reinforcement Learning for Optimization Modeling#1116335
- Knowledge-Guided Synthetic Bug Feedback for LLM-Based Unit Test Generation#363316
Record · ID 1141679
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