CoupleEvo finds that sequential and iterative strategies for evolving LLM-based heuristics yield more stable and higher-quality solutions than an integrated strategy on coupled optimization problems.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
method 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
method 1polarities
use method 1representative citing papers
OracleTSC introduces a reward hurdle and uncertainty regularization to stabilize LLM-based reinforcement learning for traffic signal control, delivering 75% lower travel time and 67% lower queue length on benchmarks plus cross-intersection generalization.
citing papers explorer
-
CoupleEvo: Evolving Heuristics for Coupled Optimization Problems Using Large Language Models
CoupleEvo finds that sequential and iterative strategies for evolving LLM-based heuristics yield more stable and higher-quality solutions than an integrated strategy on coupled optimization problems.
-
OracleTSC: Oracle-Informed Reward Hurdle and Uncertainty Regularization for Traffic Signal Control
OracleTSC introduces a reward hurdle and uncertainty regularization to stabilize LLM-based reinforcement learning for traffic signal control, delivering 75% lower travel time and 67% lower queue length on benchmarks plus cross-intersection generalization.