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版本更新 · v4arXiv:2605.25246

FrontierOR: Benchmarking LLMs' Capacity for Efficient Algorithm Design in Large-Scale Optimization

Minwei Kong, Chonghe Jiang, Ao Qu, Wenbin Ouyang, Zhaoming Zeng, Xiaotong Guo, Zhekai Li, Junyi Li 等 27 位作者

原文摘要

Large language models (LLMs) are increasingly used for optimization modeling and solver-code generation, yet practical operations research and optimization problems often require a harder capability: designing scalable algorithms that exploit problem structure and outperform direct formulation-and-solve baselines. Existing benchmarks are limited to small or simplified examples far below real-world scale and complexity. We introduce FrontierOR, the first literature-grounded benchmark targeting LLM-generated algorithm efficiency on realistic large-scale optimization problems. FrontierOR includes 180 diverse tasks, each derived from a paper published in a top-tier operations research venue, with standardized instances and a hidden, expert-verified evaluation suite. We evaluate recent LLMs' one-shot algorithm generation and improvements achieved through several self-evolving frameworks. The results show that current models still struggle to move from executable formulations to efficient optimization algorithms. Even the strongest LLM succeeds on only 39% of Full-set task instances, where success is defined as returning a feasible solution with an objective gap of at most 1% relative to Gurobi within its runtime. On the Hard subset, test-time evolution raises this proportion from 51% for the strongest one-shot model to 78%. FrontierOR provides a continuously maintained testbed for evaluating LLM-designed optimization algorithms under a shared protocol and controlled computational settings. Code and data are publicly released at https://github.com/Minw913/FrontierOR.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2509.22979

OptiMind: Improving Small Language Models for Optimization Modeling through Formulation Guidance and Multi-Turn Learning

Xinzhi Zhang, Zeyi Chen, Humishka Zope, Hugo Barbalho, Konstantina Mellou, Marco Molinaro, Janardhan Kulkarni, Ishai Menache 等 9 位作者

原文摘要

Mathematical optimization drives decisions in supply chains, logistics, energy, and scheduling, but translating natural-language problems into solver-executable formulations remains a bottleneck. While frontier models show promise for this task, many deployments require small language models (SLMs) that run locally under privacy, latency, and cost constraints. Existing SLMs are limited by noisy data, ambiguous benchmarks, and one-shot inference that ignores the iterative, feedback-driven nature of optimization modeling. To address these challenges, we introduce OptiMind, a system that combines data cleaning and class-specific hints with training using solver feedback. OptiMind distills recurring formulation errors into class-specific hints that guide the repair of ambiguous problems and the regeneration of solution trajectories. This process provides reliable training data, which is then used for multi-turn RL to teach models to revise their formulations in response to solver feedback. At inference time, the same hint library guides further refinement as models revise their solutions over successive turns. To support reliable evaluation, we clean three challenging benchmarks from the literature. On these cleaned benchmarks, our 20B OptiMind-RL model outperforms strong open-source baselines and reaches frontier-model performance under the same protocol. The full hint library, cleaned test data, cleaning logs, and evaluation code are publicly available on Github (https://github.com/microsoft/OptiGuide/tree/main/optimind), and our trained OptiMind-RL model is available on Hugging Face (https://huggingface.co/microsoft/OptiMind-RL).

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.19741

Interpreting Neural Combinatorial Optimization via Evolving Programmatic Bottlenecks

Haocheng Duan, Yuxin Guo, Jieyi Bi, Anqi Xie, Sirui Li, Yining Ma, Cathy Wu

原文摘要

While Neural Combinatorial Optimization (NCO) achieves strong performance, its interpretability remains an open question. In this paper, we introduce Evolving Programmatic Bottlenecks (EPB), the first post-hoc, surrogate-based interpretability method for NCO, which explains a black-box policy by distilling it into an interpretable behavior surrogate. A natural surrogate is a Concept Bottleneck Model (CBM), yet standard concepts are input attributes, whereas NCO is better explained by decision rules that are hard to predefine. EPB thus uses LLMs to evolve a bank of programmatic decision rules as concepts, each producing an action distribution, where a learned state-dependent router then mixes them to form the surrogate action. We optimize EPB through an iterative framework: Block I fixes the bank size and introduces the hybrid textual-numerical gradient descent to jointly optimize the textual programs and the numerical router; Block II adapts the bank size via failure-targeted expansion and redundancy pruning. Extensive experiments demonstrate that the EPB surrogate closely matches the teacher's behavior and solution quality. We then analyze the distilled rules, reveal several NCO decision patterns, and verify the rules' faithfulness to the surrogate's decisions. Moreover, the surrogate allows targeted program edits that improve its generalization beyond the teacher. Our work advances interpretable NCO and establishes EPB as a promising direction for interpreting complex learned decision-making policies.

cs.AI · cs.LG

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.542ACL Anthology · ACL 2026

Exploring Layer Activation Dynamic of CoT via Knowledge Probe

Chuanxin Zhang, Jiajun Liu, Yao He, Wenjun Ke Southeast University, Peng Wang, Yankun Le, Sirui Liu, Zhaoyu Yang

原文摘要

Chain-of-thought (CoT) reasoning has emerged as a crucial paradigm for enhancing large language model (LLM) performance on multi-step reasoning tasks.However, the internal mechanisms by which LLMs invoke knowledge and propagate information across different steps of the CoT are poorly understood.To fill this gap, we propose a multi-stage probing framework that enforces structured reasoning with three explicit stages: keyword extraction, theorem generation, and computation execution.The framework integrates attention knockout to trace cross-layer information flow and theorem probing to examine how specific contents are encoded within representations.To enable controlled and stage-aligned analysis, we construct a structured CoT dataset that covers the mathematics and physics domains. Experiments on four instruction-tuned LLMs reveal distinct stage-specific patterns.First, keyword information is progressively aggregated into the final token in later layers.Second, theorem semantics are encoded in the mid-to-late layers and undergo two stages of propagation.Finally, parameter substitution is achieved through joint extraction by the final token and other tokens.The first parameter predominantly relies on the final token, whereas later parameters increasingly depend on information extracted by other tokens.Overall, our findings shed light on the neural implementation of CoT reasoning and provide actionable insights for developing more interpretable and reasoning-capable LLMs.We further evaluate a free-form prompting setting without labeled fields and observe consistent qualitative trends.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表