arXiv (All)
open access
OptSkills: Learning Generalizable Optimization Skills from Problem Archetypes via Cluster-Based Distillation
Leveraging Large Language Models (LLMs) to automatically formulate and solve optimization problems from natural language has emerged as an efficient paradigm for automated optimization. However, existing methods still exhibit limited generalization: they are sensitive to superficial narrative variat
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
- OptSkills: Learning Generalizable Optimization Skills from Problem Archetypes via Cluster-Based Distillation#919860
- WordPolo: Evaluating Language Models Through Iterative Semantic Feedback#1013219
- FlexMoE: One-for-All Nested Intra-Expert Pruning for MoE Language Models#319698
- Human-like Knowledge Invention: A Non Monotonic Reasoning framework#699174
- PCoMoE: Shifting MoE Inference from Monolithic Expert Selection to Fine-Grained Path Composition#821013
Record · ID 924089
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