Skip to main content

King Fahd University of Petroleum and Minerals

2 related articles

Subscribe to updates

researchsquare.com Oct 29, 2026
Freight planners can skip per-scenario solves: perturbing single-scenario solutions cuts cost-estimate error to 1.33%
Two-stage stochastic service network design must fix how many vehicles run on each arc before demand is known, and the recourse cost of adding spot vehicles makes the problem hard to solve. A feedforward neural network replaces that recourse subproblem and is embedded in a mixed-integer program; because first-stage variables are general integers, training samples come from perturbing solutions of single-scenario problems. Across 18 instances, designs from the perturbation approach deviate 1.33% on average from their full-scenario evaluated cost, versus 10.81% for random sampling.
Two-stage stochastic service network design must fix how many vehicles run on each arc before demand is known, and the recourse cost of adding spot vehicles makes the problem hard to solve. A feedforward neural network replaces that recourse subproblem and is embedded in a mixed-integer program; because first-stage variables are general integers, training samples come from perturbing solutions of single-scenario problems. Across 18 instances, designs from the perturbation approach deviate 1.33% on average from their full-scenario evaluated cost, versus 10.81% for random sampling.
King Fahd University of Petroleum and Minerals
Ahmad Baubaid（First author, Corresponding author）
Stochastic Service Network Design Feedforward neural network Data Generation Sample Average Approximation
arXiv Oct 1, 2026
Self-evolving search agents develop "co-cheating": internal reward rises while real correctness stalls, and CrossFit cuts false-agreement mass from 6.1%/8.8% to 3.0%/3.7%
The work diagnoses a failure mode it calls co-cheating in self-evolving search agents, where a proposer and solver increasingly agree on shared errors so internal reward improves without matching external correctness, and introduces multi-sample verification (MSV) plus the main method CrossFit (splitting the proposer's source documents into folds and scoring each fold's questions with an auxiliary solver trained only on the other fold), reducing false-agreement mass from 6.1%/8.8% to 3.0%/3.7% on Qwen3.5-4B/9B and improving seven-benchmark average Cover-EM over standard coupled self-evolution by 8.8/8.4 points.
The work diagnoses a failure mode it calls co-cheating in self-evolving search agents, where a proposer and solver increasingly agree on shared errors so internal reward improves without matching external correctness, and introduces multi-sample verification (MSV) plus the main method CrossFit (splitting the proposer's source documents into folds and scoring each fold's questions with an auxiliary solver trained only on the other fold), reducing false-agreement mass from 6.1%/8.8% to 3.0%/3.7% on Qwen3.5-4B/9B and improving seven-benchmark average Cover-EM over standard coupled self-evolution by 8.8/8.4 points.
University of Michigan University of California San Diego McGill University King Fahd University of Petroleum and Minerals Rutgers, The State University of New Jersey
Meijia Chen（First author）
Co-Cheating Cross-Fitted Proposer Feedback Multi-Sample Verification Self-Evolving Search Agent

You’re all caught up