Hugging Face Trending Papers
Self-Guided LLM Reasoning for Error Diagnosis in Vehicle Routing Problems
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arXiv AI
Oct 16
Self-Guided LLM Reasoning for Error Diagnosis in Vehicle Routing Problems
The paper introduces a self‑guided reasoning framework that enables large language models (LLMs) to detect and correct errors in Vehicle Routing Problem (VRP) instances. By autonomously generating code that offers structured insights, the approach addresses limitations of existing methods, especially for complex instances or errors that increase reasoning load. Experiments across various problem settings and LLMs show that this framework markedly improves error detection, achieving near‑perfect accuracy.
arXiv AI
Oct 21
VRPAgent: LLM-Driven Discovery of Heuristic Operators for Vehicle Routing Problems
arXiv:2510.07073v2 Announce Type: replace Abstract: Designing high-performing heuristics for vehicle routing problems (VRPs) is a complex task that requires both intuition and deep domain knowledge....
arXiv AI
Jun 29
Towards Reliable and Robust LLM Planning: Symbolic Feedback-Driven Iterative Self-Refinement Framework
arXiv:2606. 27757v1 Announce Type: new Abstract: Large language models (LLMs) have attracted widespread attention from academia and industry, yet their deployment raises critical security concerns regarding robustness and reliability.
arXiv AI
Jun 18
Enhancing CVRP Solver through LLM-driven Automatic Heuristic Design
arXiv:2602. 23092v2 Announce Type: replace Abstract: The Capacitated Vehicle Routing Problem (CVRP), a fundamental combinatorial optimization challenge, focuses on optimizing fleet operations under vehicle capacity constraints.
arXiv Machine Learning
3d ago
Verifier-Guided Self-Consistency: Test-Time Reasoning with Imperfect Generators and Verifiers
arXiv:2611.06606v1 Announce Type: new Abstract: Large language models can solve many multi-step reasoning problems, but their reasoning remains brittle: plausible intermediate steps often lead to inc...
arXiv AI
Aug 25
AdaR: A Framework for Equipping LLMs with Adaptive Reasoning
AdaR is a framework designed to enhance large language models (LLMs) with adaptive reasoning for mathematical tasks. It identifies and mitigates spurious reasoning—where models rely on superficial correlations—by generating logically equivalent queries and training with Reinforcement Learning with Verifiable Rewards (RLVR) to penalize incorrect logic and promote adaptive logic. The approach includes extracting problem‑solving logic, executing code to verify answers, and applying sanity checks, resulting in significant gains in mathematical reasoning performance and improved data efficiency.