Papers by Boxuan Zhang
OptiVerse: A Comprehensive Benchmark towards Optimization Problem Solving (2026.findings-acl)
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| Challenge: | Existing benchmarks focus on Mathematical Programming and Combinatorial Optimization, hindering comprehensive evaluation. |
| Approach: | They propose a benchmarking tool that compares 1,000 curated optimization problems across three difficulty levels. |
| Outcome: | The proposed model improves performance on hard problems while maintaining 27% accuracy. |
CoT-UQ: Improving Response-wise Uncertainty Quantification in LLMs with Chain-of-Thought (2025.findings-acl)
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| Challenge: | Existing uncertainty quantification methods for Large language models are primarily prompt-wise rather than response-wise, which leads to inefficiency. |
| Approach: | They propose a new approach to quantify response-wise uncertainty by integrating LLMs’ inherent reasoning capabilities through Chain-of-Thought (CoT) into the UQ process. |
| Outcome: | The proposed framework outperforms existing uncertainty quantification methods and achieves an average improvement of 5.9% AUROC compared to existing methods. |
Dual-Cluster Memory Agent: Resolving Multi-Paradigm Ambiguity in Optimization Problem Solving (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) struggle with structural ambiguity in optimization problems, where a single problem admits multiple related but conflicting modeling paradigms, hindering effective solution generation. |
| Approach: | They propose to use memory to leverage historical solutions in a training-free manner to enhance performance by leveraging generalizable guidance knowledge. |
| Outcome: | The proposed agent achieves an average performance improvement of 11%-21% over previous agents. |