MetaSG-SAEA is a bi-level meta-BBO framework that uses a meta-policy for search guidance via the MM-CCI constraint abstraction and diffusion-based population initialization to outperform baselines on expensive constrained multi-objective optimization problems.
Llamoco: Instruction tuning of large language models for optimization code generation
9 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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ORPilot is the first agentic LLM system built specifically for production optimization modeling, using interview, data collection, parameter computation agents and a solver-agnostic intermediate representation to handle real-world ambiguous problems and large raw datasets.
PerfCoder is a family of LLMs trained on optimization trajectories with human annotations and runtime-based preference alignment that achieves higher runtime speedups and optimization rates on the PIE benchmark than prior models while producing interpretable feedback.
ERFSL generates and optimizes LLM-based reward functions for custom multi-objective RL, correcting codes in one iteration and converging weights in 5.2 iterations on average even from 500x errors.
APF automates solver-independent formulation of optimization problems from natural language via LLMs fine-tuned on synthetically generated high-quality data, outperforming prior methods on antenna radiation efficiency tasks.
ERFSL uses LLMs to create per-requirement reward components, correct their code via a critic, and optimize weights with genetic-algorithm-style mutation and crossover driven by training logs, succeeding in a zero-shot data collection task.
MiniOpt trains LLMs under 10B parameters via RL with OptReward to model and solve general optimization problems, reporting highest average solving accuracy among comparable models.
Survey organizing LLM uses for VRP into modeler, designer, and coordinator roles, covering variants, solvers, benchmarks, and two experiments.
Empirical study on five LLMs finds pretrained-to-aligned paths yield bigger gains over baseline than finetuned-to-aligned paths, though absolute accuracy remains lower for pretrained starts.
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Vehicle Routing Problem Meets Large Language Models: An Overview and Perspectives
Survey organizing LLM uses for VRP into modeler, designer, and coordinator roles, covering variants, solvers, benchmarks, and two experiments.