AMTFV: Agentic Mathematical Tool-Flow Verification for LLM Self-Correction

LLMs are good at solving math problems but poor at reliably verifying their own answers, since natural-language self-reflection lacks precision and code-generation approaches couple reasoning too tightly to implementation. AMTFV resolves this with a Mathematical Tool Flow interface that separates verification logic from execution: a verification agent builds a workflow and sends structured requests to a toolbox agent for exact computation. Tested across five datasets and multiple model families, it improves accuracy notably on complex problems. This has applications in automated tutoring, scientific computing assistants, and any system needing trustworthy LLM-based mathematical verification. Authors: Rui Zou, Yutao Zhu, Mengqi Wei, Ji-Rong Wen Paper: https://arxiv.org/abs/2607.29549v1