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Research questionHow can LLM optimization systems generalize beyond surface narratives to shifted or emerging problem types?LLM systems that translate natural-language descriptions into optimization models and solutions can depend on superficial wording and individual prior cases. Their performance can therefore become brittle when equivalent structures are described differently or when new problem types emerge.
AI
AI Agents
Evaluation & Benchmarks
Machine Learning
Natural Language Processing
Reasoning
Latest papersRecent research connected to this question, newest first.OptSkills: Learning Generalizable Optimization Skills from Problem Archetypes via Cluster-Based DistillationThe source presents an archetype-based optimization agent that explores modeling paradigms and solver configurations, distills successful workflows into reusable skills, and updates its skill library for new trajectories. Evidence is reported on diverse problem-type datasets, MIPLIB-NL, and the out-of-distribution NLCO benchmark using accuracy measurements.research paper · Sep 2, 2026
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