SafeInferCom: Safe Inference-Time Compute via Verifier-Guided Mid-Generation Intervention for Robotic Task Planning
Read the original on arXiv AI →SafeInferCom introduces an inference-time monitor that verifies intermediate plans generated by large reasoning language models (LRLMs) for robotic task planning, without disrupting the decoding trajectory. The framework uses a formal verifier to preserve valid intermediate plans and guide error correction during generation, improving planning success and speeding up error correction compared to one-shot inference. Experiments across multiple LRLMs and planning domains, including VirtualHome and a real-world robotic-arm demonstration, show that SafeInferCom enhances success rates while reducing token usage when combined with iterative refinement.
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