arXiv (All)
open access
Robustness Starts with the Objective: Designing and Certifying Robust LLM-Generated Heuristics
LLMs are increasingly used to design heuristics for computer systems. However, most synthesis frameworks evaluate candidates by their average performance, which can mask severe regressions on specific workloads. Even adding adversarial training inputs doesn't eliminate these regressions, and percent
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
- Sustainability of Open-Source Machine Learning Robustness Assessment Tools: A Repository Mining Study#792404
- Sustainability of Open-Source Machine Learning Robustness Assessment Tools: A Repository Mining Study#788404
- Constructing Data-Driven Budgeted Uncertainty Sets for Robust Optimization#1469207
- A Counterfactual Account of Algorithmic Robustness#81072
- Beyond the Traceback: Using LLMs for Adaptive Explanations of Programming Errors#662088
Record · ID 1536334
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