Video question answering (VideoQA) necessitates simultaneous understanding of visual and linguistic information, requiring both in-depth analysis of individual modality features and the establishment of cross-modal correlations to achieve precise reasoning. However, VideoQA models often struggle with irrelevant temporal and spatial noise due to the dense events and concepts in real-world complex video contents. Previous works reduce noise by only sampling a fixed number of visual tokens at the patch level, overlooking the variation in the required granularities of features and quantities of visual cues across different question conditions. To address these, we propose an Aggregating-then-Pruning Sampler (APSam), which diversifies feature granularities and adaptively denoises on a per-question basis. Specifically, we propose a conditional token aggregator to obtain multi-granularity visual semantics by merging similar question-relevant tokens. Then, we propose a conditional token pruner, which restricts noise tokens through a variable-capacity receptive field determined by the inputs. Experimental results show that APSam achieves significant performance on three challenging complex VideoQA datasets,i.e., AGQAv2, NExT-QA, and STAR. Further analyses reveal that the APSam also exhibits high reasoning capability and interpretability.

Multi-hop Knowledge Graph Reasoning (KGR) seeks to identify accurate answers within Knowledge Graphs (KGs) via multi-step reasoning, predominantly utilizing reinforcement learning (RL) to enhance the efficiency of the reasoning process. Unlike traditional Knowledge Graph Embedding (KGE) methods, RL-based approaches offer superior interpretability. However, these methods often underperform due to two critical limitations: (1) their over-reliance on Horn rules for reasoning paths, which restricts their expressive power; and (2) inadequate utilization of reasoning states during the process. To address these issues, we propose a novel RL-based framework, RAR, which shifts focus from individual paths to subgraph structures for more robust predictions. RAR frames the retrieval of reasoning subgraphs from the KG as a Markov Decision Process (MDP) and incorporates a subgraph retriever. To efficiently explore the extensive subgraph space, we integrate multi-agent RL to enhance the retriever's capabilities. Additionally, RAR features an advanced analyst module that meticulously examines reasoning states. These modules function iteratively: the retriever expands the subgraph, followed by the analyst module's in-depth analysis. The insights gained are then used to inform subsequent retrieval steps. Ultimately, the predicted scores from both modules are synthesized to produce more precise posterior scores. Experimental results across multiple datasets demonstrate RAR's efficacy, showcasing a notable improvement over existing state-of-the-art RL-based KGR methods.