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.
Vehicle Routing Problems (VRPs) are fundamental to many real-world applications. Recent advances in large language models (LLMs) have spurred progress in solving these NP-hard problems. However, most...
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:2511. 09373v2 Announce Type: replace-cross Abstract: LLMs now tackle a wide range of software-related tasks, yet we show that their performance varies markedly both across and within these tasks.
AgentCollab introduces a self-evaluation-driven collaboration paradigm for large language model agents, dynamically coordinating models of varying reasoning capacities during task execution. By using the agent’s own self-reflection to decide when to elevate to a stronger model, it avoids reliance on external routing modules and allocates additional reasoning budget only when recent failures indicate difficulty. Experiments on multi-step agent benchmarks demonstrate that this two-level small-large model setup consistently improves the accuracy-efficiency Pareto frontier of LLM agents.
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: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:2604. 16694v2 Announce Type: replace Abstract: Large reasoning models (LRMs) enhance problem-solving capabilities by generating explicit multi-step chains of thought (CoT) reasoning; however, they incur substantial inference latency and computational overhead.
arXiv:2509. 23413v3 Announce Type: replace Abstract: Multi-task neural routing solvers have emerged as a promising paradigm for their ability to solve multiple vehicle routing problems (VRPs) using a single model.
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...
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.
arXiv:2606. 04816v1 Announce Type: new Abstract: Large language models (LLMs) increasingly translate natural-language optimization problems into executable solver code.
arXiv:2606. 06635v1 Announce Type: cross Abstract: Failures in language model reasoning emerge through distinct processes that leave identifiable signatures in the reasoning trace.