arXiv cs.AI· Weizhe Xu, Jialiang Fan, Mengyu Liu, Fanxin Kong·· 9 天前AI 评分24
SafeInferCom：面向机器人任务规划的安全推理时计算框架
SafeInferCom: Safe Inference-Time Compute via Verifier-Guided Mid-Generation Intervention for Robotic Task Planning
AI 导读
SafeInferCom 是一个由形式化验证器引导的推理时框架，通过监控并验证中间规划、在生成过程中保留有效中间计划并引导纠错，解决大推理语言模型在机器人任务规划中持续推理覆盖有效计划或约束违规未解决的问题。
正文 · 原文
Abstract:Large Reasoning Language Models (LRLMs) enable multi-step reasoning for robotic task planning, but continued reasoning can overwrite valid intermediate plans or leave constraint violations unresolved, reducing planning reliability and wasting inference-time computation. We develop an inference-time monitor that exposes and verifies intermediate plans without disrupting the original decoding trajectory. Building on this monitor, we propose SafeInferCom, a formal verifier-guided framework that preserves valid intermediate plans and directs error correction during generation. Experiments across multiple LRLMs and planning domains reveal reasoning-response inconsistency and limited self-correction under one-shot inference. SafeInferCom improves planning success and accelerates error correction relative to one-shot inference. When combined with iterative refinement, it further improves success while reducing token usage compared with refinement alone. We additionally evaluate SafeInferCom in VirtualHome and provide a real-world robotic-arm demonstration.
| Comments: | Video: this https URL |
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Logic in Computer Science (cs.LO) |
| Cite as: | arXiv:2610.11223 [cs.RO] |
| (or arXiv:2610.11223v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11223 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Weizhe Xu [view email]
[v1]
Thu, 8 Oct 2026 04:19:47 UTC (409 KB)
来源：arXiv cs.AI · arxiv.org