Just-In-Time Scene Graph Growth: Combating Perceptual Saturation in Long-Horizon Robotics

While 3D Scene Graphs (3DSGs) provide crucial structured representations for embodied agents, conventional Ahead-of-Time, build-everything-then-filter pipelines conflict with the real-time, low-latency demands of edge platforms, inducing a perceptual saturation effect via severe observation redundancy. To resolve this, we present JITOMA (Just-In-Time On-demand Memory Activation), a closed-loop framework that unifies task reasoning, perception, and memory into a just-in-time growth process. Instead of exhaustively mapping the entire environment, JITOMA leverages a top-down task heatmap at the frontend to filter continuous observations, routing minimal streams to maintain a global foundation of low-cost, dormant anchors. Upon a cognitive query, the backend Large Language Model (LLM) parses the robotic intent to dynamically awaken task-relevant anchors, triggering resource-intensive operations -- such as dense node captioning and functional inference -- exclusively within the activated local subgraph. To evaluate these dynamic capabilities and study perceptual saturation trade-offs, we introduce JITOMA-Bench, a comprehensive suite for long-horizon multi-tasking and complex multi-step reasoning. Extensive experiments demonstrate that JITOMA substantially reduces active graph size and captioning latency, while maintaining stable processing time under long-horizon task switching.

SAM-Sode: Towards Faithful Explanations for Tiny Bacteria Detection

Interpretability in object detection provides crucial confidence support for clinical auxiliary diagnosis. However, in tiny bacteria detection, traditional explanation methods often suffer from blurred foreground boundaries and diffuse feature attribution due to the extreme sparsity of target morphological features and severe interference from complex backgrounds. Such limitations hinder the provision of logically coherent morphological evidence. To bridge this gap, we propose a novel eXplainable AI (XAI) framework, SAM-Sode. The framework innovatively transforms initial feature attribution maps into geometry-aware prompts, leveraging the prior knowledge of the foundation model (SAM3) to achieve spatial refinement and morphological reconstruction of the explanatory mappings. Furthermore, we introduce a dual-constraint mechanism based on physical significance and geometric alignment to perform instance-level denoising, generating coherent explanations that better align with human expert intuition. Experimental results on our self-constructed bacteria dataset with complex circuit backgrounds (containing 2,524 images) and other public datasets demonstrate that the proposed method effectively suppresses background redundancy and significantly enhances the decision-making transparency of tiny object detection.

Topology-Aware CLIP Few-Shot Learning

Efficiently adapting large Vision-Language Models (VLMs) like CLIP for few-shot learning poses challenges in balancing pre-trained knowledge retention and task-specific adaptation. Existing methods often overlook valuable structural information within the VLM's latent space. We introduce a topology-aware tuning approach integrating Representation Topology Divergence (RTD) into the Task Residual (TR) framework. By explicitly aligning the topological structures of visual and text representations using a combined RTD and Cross-Entropy loss, while freezing base VLM encoders, our method enhances few-shot performance. We optimize only lightweight Task Residual parameters, effectively leveraging topological information. Across 6 diverse benchmark datasets, our approach demonstrates significant gains, achieving an average accuracy improvement of 1-2\% over relevant baseline methods in few-shot settings. This work presents an effective strategy to boost VLM few-shot capabilities by incorporating topological alignment.

Exploring applications of topological data analysis in stock index movement prediction

Topological Data Analysis (TDA) has recently gained significant attention in the field of financial prediction. However, the choice of point cloud construction methods, topological feature representations, and classification models has a substantial impact on prediction results. This paper addresses the classification problem of stock index movement. First, we construct point clouds for stock indices using three different methods. Next, we apply TDA to extract topological structures from the point clouds. Four distinct topological features are computed to represent the patterns in the data, and 15 combinations of these features are enumerated and input into six different machine learning models. We evaluate the predictive performance of various TDA configurations by conducting index movement classification tasks on datasets such as CSI, DAX, HSI and FTSE providing insights into the efficiency of different TDA setups.