arXiv:2508.20125v2 Announce Type: replace-cross Abstract: Purpose: Spiking neural networks (SNNs) have recently gained attention as energy-efficient, biologically plausible alternatives to conventional deep learning models. Their application in high-stakes biomedical imaging remains almost entirely unexplored. Methods: This study introduces SNNDeep, the first tailored SNN specifically optimized for binary classification of liver health status from computed tomography […]
GUIDE: Resolving Domain Bias in GUI Agents through Real-Time Web Video Retrieval and Plug-and-Play Annotation
arXiv:2603.26266v2 Announce Type: replace Abstract: Large vision-language models have endowed GUI agents with strong general capabilities for interface understanding and interaction. However, due to insufficient exposure to domain-specific software operation data during training, these agents exhibit significant domain bias – they lack familiarity with the specific operation workflows (planning) and UI element layouts (grounding) of […]
FA-INR: Adaptive Implicit Neural Representations for Interpretable Exploration of Simulation Ensembles
arXiv:2506.06858v3 Announce Type: replace-cross Abstract: Surrogate models are essential for efficient exploration of large-scale ensemble simulations. Implicit neural representations (INRs) provide a compact and continuous framework for modeling spatially structured data, but they often struggle with learning complex localized structures within the scientific fields. Recent INR-based surrogates address this by augmenting INRs with explicit feature […]
Learning Inter-Atomic Potentials without Explicit Equivariance
arXiv:2510.00027v3 Announce Type: replace-cross Abstract: Accurate and scalable machine-learned inter-atomic potentials (MLIPs) are essential for molecular simulations ranging from drug discovery to new material design. Current state-of-the-art models enforce roto-translational symmetries through equivariant neural network architectures, a hard-wired inductive bias that can often lead to reduced flexibility, computational efficiency, and scalability. In this work, we […]
Towards High-Consistency Embodied World Model with Multi-View Trajectory Videos
arXiv:2511.12882v3 Announce Type: replace-cross Abstract: Embodied world models aim to predict and interact with the physical world through visual observations and actions. However, existing models struggle to accurately translate low-level actions (e.g., joint positions) into precise robotic movements in predicted frames, leading to inconsistencies with real-world physical interactions. To address these limitations, we propose MTV-World, […]
PAIR-Former: Budgeted Relational MIL for miRNA Target Prediction
arXiv:2602.00465v2 Announce Type: replace-cross Abstract: Functional miRNA–mRNA targeting is a large-bag prediction problem: each transcript yields a heavy-tailed pool of candidate target sites (CTSs), yet only a pair-level label is observed. We formalize this regime as emphBudgeted Relational Multi-Instance Learning (BR-MIL), where at most $K$ instances per bag may receive expensive encoding and relational processing […]
Sample-Efficient Hypergradient Estimation for Decentralized Bi-Level Reinforcement Learning
arXiv:2603.14867v3 Announce Type: replace-cross Abstract: Many strategic decision-making problems, such as environment design for warehouse robots, can be naturally formulated as bi-level reinforcement learning (RL), where a leader agent optimizes its objective while a follower solves a Markov decision process (MDP) conditioned on the leader’s decisions. In many situations, a fundamental challenge arises when the […]
Enes Causal Discovery
arXiv:2603.24436v3 Announce Type: replace-cross Abstract: Enes The proposed architecture is a mixture of experts, which allows for the model entities, such as the causal relationships, to be further parameterized. More specifically, an attempt is made to exploit a neural net as implementing neurons poses a great challenge for this dataset. To explain, a simple and […]
Concept frustration: Aligning human concepts and machine representations
arXiv:2603.29654v1 Announce Type: cross Abstract: Aligning human-interpretable concepts with the internal representations learned by modern machine learning systems remains a central challenge for interpretable AI. We introduce a geometric framework for comparing supervised human concepts with unsupervised intermediate representations extracted from foundation model embeddings. Motivated by the role of conceptual leaps in scientific discovery, we […]
From Skeletons to Semantics: Design and Deployment of a Hybrid Edge-Based Action Detection System for Public Safety
arXiv:2603.29777v1 Announce Type: cross Abstract: Public spaces such as transport hubs, city centres, and event venues require timely and reliable detection of potentially violent behaviour to support public safety. While automated video analysis has made significant progress, practical deployment remains constrained by latency, privacy, and resource limitations, particularly under edge-computing conditions. This paper presents the […]
UnWeaving the knots of GraphRAG — turns out VectorRAG is almost enough
arXiv:2603.29875v1 Announce Type: cross Abstract: One of the key problems in Retrieval-augmented generation (RAG) systems is that chunk-based retrieval pipelines represent the source chunks as atomic objects, mixing the information contained within such a chunk into a single vector. These vector representations are then fundamentally treated as isolated, independent and self-sufficient, with no attempt to […]
Training deep learning based dynamic MR image reconstruction using synthetic fractals
arXiv:2603.29922v1 Announce Type: cross Abstract: Purpose: To investigate whether synthetically generated fractal data can be used to train deep learning (DL) models for dynamic MRI reconstruction, thereby avoiding the privacy, licensing, and availability limitations associated with cardiac MR training datasets. Methods: A training dataset was generated using quaternion Julia fractals to produce 2D+time images. Multi-coil […]