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.