arXiv:2506.06683v4 Announce Type: replace-cross Abstract: Dual-arm robots play a crucial role in improving efficiency and flexibility in complex multitasking scenarios. While existing methods have achieved promising results in task planning, they often fail to fully optimize task parallelism, limiting the potential of dual-arm collaboration. To address this issue, we propose RoboPARA, a novel large language […]
Video-EM: Event-Centric Episodic Memory for Long-Form Video Understanding
arXiv:2508.09486v2 Announce Type: replace-cross Abstract: Video Large Language Models (Video-LLMs) have shown strong video understanding, yet their application to long-form videos remains constrained by limited context windows. A common workaround is to compress long videos into a handful of representative frames via retrieval or summarization. However, most existing pipelines score frames in isolation, implicitly assuming […]
Membership Inference Attacks on Tokenizers of Large Language Models
arXiv:2510.05699v3 Announce Type: replace-cross Abstract: Membership inference attacks (MIAs) are widely used to assess the privacy risks associated with machine learning models. However, when these attacks are applied to pre-trained large language models (LLMs), they encounter significant challenges, including mislabeled samples, distribution shifts, and discrepancies in model size between experimental and real-world settings. To address […]
Towards Efficient Federated Learning of Networked Mixture-of-Experts for Mobile Edge Computing
arXiv:2511.01743v2 Announce Type: replace-cross Abstract: Recent advancements in large artificial intelligence models (LAMs) are driving significant innovations in mobile edge computing within next-generation wireless networks. However, the substantial demands for computational resources and larges-cale training data required to train LAMs conflict with the limited storage and computational capacity of edge devices, posing significant challenges to […]
Beyond Additivity: Sparse Isotonic Shapley Regression toward Nonlinear Explainability
arXiv:2512.03112v2 Announce Type: replace-cross Abstract: Shapley values, a gold standard for feature attribution in Explainable AI, face two key challenges. First, the canonical Shapley framework assumes that the worth function is additive, yet real-world payoff constructions–driven by non-Gaussian distributions, heavy tails, feature dependence, or domain-specific loss scales–often violate this assumption, leading to distorted attributions. Second, […]
Bitcoin Price Prediction using Machine Learning and Combinatorial Fusion Analysis
arXiv:2602.00037v2 Announce Type: replace-cross Abstract: In this work, we propose to apply a new model fusion and learning paradigm, known as Combinatorial Fusion Analysis (CFA), to the field of Bitcoin price prediction. Price prediction of financial product has always been a big topic in finance, as the successful prediction of the price can yield significant […]
LongAudio-RAG: Event-Grounded Question Answering over Multi-Hour Long Audio
arXiv:2602.14612v3 Announce Type: replace-cross Abstract: Long-duration audio is increasingly common in industrial and consumer settings, yet reviewing multi-hour recordings is impractical, motivating systems that answer natural-language queries with precise temporal grounding and minimal hallucination. Existing audio-language models show promise, but long-audio question answering remains difficult due to context-length limits. We introduce LongAudio-RAG (LA-RAG), a hybrid […]
Slurry-as-a-Service: A Modest Proposal on Scalable Pluralistic Alignment for Nutrient Optimization
arXiv:2603.02420v2 Announce Type: replace-cross Abstract: Pluralistic alignment has emerged as a promising approach for ensuring that large language models (LLMs) faithfully represent the diversity, nuance, and conflict inherent in human values. In this work, we study a high-stakes deployment context – mulching – where automated systems transform selected individuals into nutrient-rich slurry for the dual […]
Bridging Domains through Subspace-Aware Model Merging
arXiv:2603.05768v2 Announce Type: replace-cross Abstract: Model merging integrates multiple task-specific models into a single consolidated one. Recent research has made progress in improving merging performance for in-distribution or multi-task scenarios, but domain generalization in model merging remains underexplored. We investigate how merging models fine-tuned on distinct domains affects generalization to unseen domains. Through an analysis […]
Distributional Regression with Tabular Foundation Models: Evaluating Probabilistic Predictions via Proper Scoring Rules
arXiv:2603.08206v1 Announce Type: cross Abstract: Prior-Data Fitted Networks (PFNs), such as TabPFN and TabICL, have revolutionized tabular deep learning by leveraging in-context learning for tabular data. These models are meant as foundation models for classification and regression settings and promise to greatly simplify deployment in practical settings because their performance is unprecedented (in terms of […]
SCL-GNN: Towards Generalizable Graph Neural Networks via Spurious Correlation Learning
arXiv:2603.08270v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) have demonstrated remarkable success across diverse tasks. However, their generalization capability is often hindered by spurious correlations between node features and labels in the graph. Our analysis reveals that GNNs tend to exploit imperceptible statistical correlations in training data, even when such correlations are unreliable for […]
Graph-Instructed Neural Networks for parametric problems with varying boundary conditions
arXiv:2603.08304v1 Announce Type: cross Abstract: This work addresses the accurate and efficient simulation of physical phenomena governed by parametric Partial Differential Equations (PDEs) characterized by varying boundary conditions, where parametric instances modify not only the physics of the problem but also the imposition of boundary constraints on the computational domain. In such scenarios, classical Galerkin […]