We introduce PKR-QA (Procedural Knowledge Reasoning Question Answering), a new benchmark for question answering over procedural tasks that require structured reasoning. PKR-QA is constructed semi-automatically using a procedural knowledge graph (PKG), which encodes task-specific knowledge across diverse domains. The PKG is built by curating and linking information from the COIN instructional video dataset and the ontology, enriched with commonsense knowledge from ConceptNet and structured outputs from Large Language Models (LLMs), followed by manual verification. To generate question-answer pairs, we design graph traversal templates where each template is applied systematically over PKG. To enable interpretable reasoning, we propose a neurosymbolic approach called Knowledge Module Learning (KML), which learns procedural relations via neural modules and composes them for structured reasoning with LLMs. Experiments demonstrate that this paradigm improves reasoning performance on PKR-QA and enables step-by-step reasoning traces that facilitate interpretability.

We introduce ViDSE, a Video framework that Deduce and Selects visual Evidence for training-free video goal inference using language models. Unlike approaches that directly apply vision-language models (VLM) or combine VLM+LLM to process dense video visuals, ViDSE explicitly selects relevant visual evidence (e.g., frames) based on the hypothesis deduced by the LLM. This approach not only im-proves accuracy but also reveals the logical process behind the model's decisions, enhancing explainability. Our exper-iments demonstrate that this selection process significantly reduces ambiguity in the subsequent inference reasoning stage and outperforms VLM-only and VLM+LLM models on goal inference tasks such as CrossTask and COIN. We further validate ViDSE’ s generalizability and robustness on action recognition tasks, such as ActivityNet and UCF-101, under training-free and open-vocabulary conditions. We observe that ViDSE easily generalizes to other video tasks (e.g., action recognition) requiring filtering of redundant and irrelevant information.