智能体工具调用中的泛化：CoreThink 智能体推理器与 MAVEN 数据集
On Generalization in Agentic Tool Calling: CoreThink Agentic Reasoner and MAVEN Dataset
AI总结：
针对智能体工具调用泛化难题，本文提出 CoreThink 推理器，以轻量符号推理层实现结构化分解与工具编排，无需训练即在多基准上以十分之一成本取得530%提升，并引入 MAVEN 基准揭示泛化差距。
摘要：
在开发可靠的智能体推理系统时，跨智能体工具调用环境的泛化仍是一个关键且尚未解决的挑战。尽管大型语言模型（LLMs）在孤立基准测试中展现出强劲性能，但其在不同领域间迁移推理策略并协调工具的能力尚不明确。在本工作中，我们对多个最先进的LLMs在多个工具调用基准（BFCL v3、TauBench、Tau2Bench 和 AceBench）上进行了大规模评估，并引入了 MAVEN（数学与物理对抗性验证与评估网络），这是一个新的分布外（OOD）基准，旨在通过显式验证和对抗性任务组合来压力测试多步推理。我们的结果表明，大多数当前模型在 MAVEN 上的准确率低于 50%，揭示了跨工具使用场景中的显著泛化差距。为解决这一问题，我们提出了 CoreThink 智能体推理器，这是一个通过轻量级符号推理层增强 LLMs 的框架，用于结构化分解和自适应工具编排。在无需额外训练的情况下，它能在所有基准上实现泛化，以约十分之一的计算成本，在现有基线上取得 530% 的提升，达到最先进的性能。
英文摘要：
Generalization across Agentic tool-calling environments remains a key unsolved challenge in developing reliable agentic reasoning systems. While large language models (LLMs) demonstrate strong performance on isolated benchmarks, their ability to transfer reasoning strategies and co-ordinate tools across diverse domains is poorly understood. In this work, we conduct a large-scale evaluation of state-of-the-art LLMs on multiple tool-calling benchmarksBFCL v3, TauBench, Tau2Bench, and AceBenchand introduce MAVEN (Math & Physics Adversarial Verification & Evaluation Network), a new out of distribution (OOD) benchmark designed to stress-test multi-step reasoning through explicit verification and adversarial task composition. Our results show that most current models achieve below 50% accuracy on MAVEN, revealing a significant generalization gap across tool-use settings. To address this, we present the CoreThink Agentic Reasoner, a framework that augments LLMs with a lightweight symbolic reasoning layer for structured decomposition and adaptive tool orchestration. Without additional training, it generalizes across all benchmarks, achieving state-of-the-art performance with 530% improvements over existing baselines at roughly one-tenth the computational cost.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
