SMCEvolve applies Sequential Monte Carlo sampling to LLM program search with adaptive resampling, mutation mixtures, and convergence control, delivering finite-sample complexity bounds and benchmark gains over prior systems.
Optimization by simulated annealing
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
method 1
citation-polarity summary
years
2026 4verdicts
UNVERDICTED 4roles
method 1polarities
extend 1representative citing papers
FMQA optimizes MDR classification error rates to identify predefined high-order epistatic interactions in simulated genetic datasets within limited iterations.
Tunneling-augmented simulated annealing optimizes short-block LDPC parity-check matrices to achieve average 0.45 dB SNR gains over random constructions.
Proactive client selection in federated learning via differentially private mutual information and simulated annealing to optimize Potential Federation Loss for utility and fairness.
citing papers explorer
-
SMCEvolve: Principled Scientific Discovery via Sequential Monte Carlo Evolution
SMCEvolve applies Sequential Monte Carlo sampling to LLM program search with adaptive resampling, mutation mixtures, and convergence control, delivering finite-sample complexity bounds and benchmark gains over prior systems.
-
High-Order Epistasis Detection Using Factorization Machine with Quadratic Optimization Annealing and MDR-Based Evaluation
FMQA optimizes MDR classification error rates to identify predefined high-order epistatic interactions in simulated genetic datasets within limited iterations.
-
Tunneling-Augmented Simulated Annealing for Short-Block LDPC Code Construction
Tunneling-augmented simulated annealing optimizes short-block LDPC parity-check matrices to achieve average 0.45 dB SNR gains over random constructions.
-
Choose Wisely and Privately: Proactive Client Selection for Fair and Efficient Federated Learning
Proactive client selection in federated learning via differentially private mutual information and simulated annealing to optimize Potential Federation Loss for utility and fairness.