arXiv AI By Junbo Jacob Lian, Yujun Sun, Huiling Chen, Chaoyu Zhang, Hanzhang Qin, Chung-Piaw Teo

ReLoop: Structured Modeling and Behavioral Verification for Reliable LLM-Based Optimization

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arXiv:2602. 15983v3 Announce Type: replace-cross Abstract: Large language models (LLMs) can translate natural language into optimization code, but silent failures pose a critical risk: code that executes and returns solver-feasible solutions may encode semantically incorrect formulations---a feasibility--correctness gap reaching 90 percentage points on compositional problems.

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arXiv AI
Aug 5

IR2Solve: Structured Intermediate Representations for Cost-Efficient Optimization Autoformulation

arXiv:2608. 02641v1 Announce Type: cross Abstract: Large language models (LLMs) can translate natural-language optimization problems into solver-ready formulations, but direct code generation is brittle: schema, indexing, and semantic errors can cause compilation failures, infeasible models, or incorrect objectives, while iterative repair, search, and multi-agent workflows increase inference cost.

By Penglin Zhu, Linhai Zhang, Jungang Xu, Xinchi Wei, Xiuqi Wu
arXiv Computation and Language
Sep 22

CCTU: A Benchmark for Tool Use under Complex Constraints

CCTU is a new benchmark designed to evaluate large language models (LLMs) on their ability to use tools under complex constraints. It includes 200 test cases that average seven constraint types and 4,700‑token prompts, covering resource, behavior, toolset, and response dimensions. An executable validation module performs step‑level checks, and nine state‑of‑the‑art LLMs were tested, revealing that none exceed a 20% task completion rate when strict constraints are enforced, with frequent violations and limited self‑refinement.

By Junjie Ye, Guoqiang Zhang, Wenjie Fu, Zelin Li, Tao Gui, Qi Zhang, Xuanjing Huang
arXiv AI
Jul 24

Representation Robustness Under Executable Reasoning Constraints in Large Language Models for Mathematical Problem Solving

arXiv:2607. 20520v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly evaluated on mathematical problem solving, yet prior work often treats representationally equivalent formulations as interchangeable and conflates reasoning errors with interface failures.

By Sagnik Nath, Edith Aurora Graf, Liang Zhang, Diego Zapata-Rivera