arXiv subjects

Yuchen Wan

Publications and source records attributed to Yuchen Wan.

4 recordsLinked to original sources

arXiv2026

OptiVerse: A Comprehensive Benchmark towards Optimization Problem Solving

While Large Language Models (LLMs) demonstrate remarkable reasoning, complex optimization tasks remain challenging, requiring domain knowledge and robust implementation. However, existing benchmarks focus narrowly on Mathematical Programming and Combinatorial Optimization, hindering comprehensive evaluation. To address this, we introduce OptiVerse, a comprehensive benchmark of 1,000 curated problems spanning neglected domains, including Stochastic Optimization, Dynamic Optimization, Game Optimization, and Optimal Control, across three difficulty levels: Easy, Medium, and Hard. The experiments with 22 LLMs of different sizes reveal sharp performance degradation on hard problems, where even advanced models like GPT-5.2 and Gemini-3 struggle to exceed 27% accuracy. Through error analysis, we identify that modeling & logic errors remain the primary bottleneck. Consequently, we propose a Dual-View Auditor Agent that improves the accuracy of the LLM modeling process without introducing significant time overhead. OptiVerse will serve as a foundational platform for advancing LLMs in solving complex optimization challenges.

cs.CL↗

arXiv2026

Dual-Cluster Memory Agent: Resolving Multi-Paradigm Ambiguity in Optimization Problem Solving

Large Language Models (LLMs) often struggle with structural ambiguity in optimization problems, where a single problem admits multiple related but conflicting modeling paradigms, hindering effective solution generation. To address this, we propose Dual-Cluster Memory Agent (DCM-Agent) to enhance performance by leveraging historical solutions in a training-free manner. Central to this is Dual-Cluster Memory Construction. This agent assigns historical solutions to modeling and coding clusters, then distills each cluster's content into three structured types: Approach, Checklist, and Pitfall. This process derives generalizable guidance knowledge. Furthermore, this agent introduces Memory-augmented Inference to dynamically navigate solution paths, detect and repair errors, and adaptively switch reasoning paths with structured knowledge. The experiments across seven optimization benchmarks demonstrate that DCM-Agent achieves an average performance improvement of 11%- 21%. Notably, our analysis reveals a ``knowledge inheritance'' phenomenon: memory constructed by larger models can guide smaller models toward superior performance, highlighting the framework's scalability and efficiency.

cs.CL↗

arXiv2025

Sequential Additivity in Distributionally Robust Ranking and Selection

Ranking and selection (R&S) seeks to identify the alternative with the best mean performance from a finite collection of simulated alternatives. Its practical value depends on accurate simulation input modeling, which is often hindered by input uncertainty arising from limited data. Distributionally robust R&S (DRR&S) addresses this challenge by considering several plausible input distributions and selecting the alternative with the best worst-case mean performance, resulting in a multiplicative number of scenarios. Existing static and oracle analyses suggest that efficient sampling should instead be additive, concentrating on only a small number of critical scenarios. We introduce sequential additivity, which characterizes how this structure emerges from adaptive sequential procedures. We first establish an algorithm-independent sampling lower bound: any consistent DRR&S procedure must sample at least this additive number of scenarios infinitely often. We then study a simplified additive allocation (AA) procedure abstracted from practical sequential designs. Using boundary-crossing arguments, we derive a finite-budget upper bound on its probability of incorrect selection and show that this probability decays exponentially as the budget grows. Moreover, AA attains the necessary sampling lower bound exactly, showing that additivity can be achieved in the strongest possible sense. Surprisingly, the scenarios sampled infinitely often need not be the true worst-case scenarios, showing that worst-case scenario identification may not be necessary for sufficient exploration in DRR&S. To generalize these insights, we introduce a general additive allocation (GAA) framework that incorporates sampling rules from traditional R&S in a modular fashion. Under suitable exploration conditions, GAA procedures retain the key properties of AA.

stat.ML↗

arXiv2024

New Additive OCBA Procedures for Robust Ranking and Selection

Robust ranking and selection (R&S) is an important and challenging variation of conventional R&S that seeks to select the best alternative among a finite set of alternatives. It captures the common input uncertainty in the simulation model by using an ambiguity set to include multiple possible input distributions and shifts to select the best alternative with the smallest worst-case mean performance over the ambiguity set. In this paper, we aim at developing new fixed-budget robust R&S procedures to minimize the probability of incorrect selection (PICS) under a limited sampling budget. Inspired by an additive upper bound of the PICS, we derive a new asymptotically optimal solution to the budget allocation problem. Accordingly, we design a new sequential optimal computing budget allocation (OCBA) procedure to solve robust R&S problems efficiently. We then conduct a comprehensive numerical study to verify the superiority of our robust OCBA procedure over existing ones. The numerical study also provides insights on the budget allocation behaviors that lead to enhanced efficiency.

stat.ME↗