Can Multi-Modal LLMs Provide Live Step-by-Step Task Guidance?

Roland Memisevic (Qualcomm) · Apratim Bhattacharyya (MPI Informatics) · Bicheng Xu (University of British Columbia) · Sanjay Haresh (Qualcomm AI Research) · Reza Pourreza (Qualcomm AI Research) · Litian Liu · Sunny Panchal (Qualcomm AI Research) · Leonid Sigal (University of British Columbia)

asynchronous reactionbenchmark evaluationdensely annotated datasetfeedback messaginginstructional coachinginteractive guidancelivemambamulti-modal large language modelsqualcomm interactive cookingreal-time execution detectionsituated coachingtask execution errorstimestamped instructionsuser mistake identificationvideo stream analysis

Multi-modal Large Language Models (LLM) have advanced conversational abilities but struggle with providing live, interactive step-by-step guidance, a key capability for future AI assistants. Effective guidance requires not only delivering instructions but also detecting their successful execution, as well as identifying and alerting users to mistakes, all of which has to happen in real-time. This requires models that are not turn-based, but that can react asynchronously to a video stream, as well as video data showing users performing tasks including mistakes and their corrections. To this end, we introduce Qualcomm Interactive Cooking, a new benchmark and dataset built upon CaptainCook4D, which contains user mistakes during task execution. Our dataset and benchmark features densely annotated, timed instructions and feedback messages, specifically including mistake alerts precisely timestamped to their visual occurrence in the video. We evaluate state-of-the-art multi-modal LLMs on the Qualcomm Interactive Cooking benchmark and introduce LiveMamba, a streaming multi-modal LLM designed for interactive instructional guidance. This work provides the first dedicated benchmark and a strong baseline for developing and evaluating on live, situated coaching.