arXiv:2511.02217v1 Announce Type: cross Abstract: One of the main challenges in managing traffic at multilane intersections is ensuring smooth coordination between human-driven vehicles (HDVs) and connected autonomous vehicles (CAVs). This paper presents a novel traffic signal control framework that combines Graph Attention Networks (GAT) with Soft Actor-Critic (SAC) reinforcement learning to address this challenge. GATs […]
Re-FORC: Adaptive Reward Prediction for Efficient Chain-of-Thought Reasoning
arXiv:2511.02130v1 Announce Type: new Abstract: We propose Re-FORC, an adaptive reward prediction method that, given a context, enables prediction of the expected future rewards as a function of the number of future thinking tokens. Re-FORC trains a lightweight adapter on reasoning models, demonstrating improved prediction with longer reasoning and larger models. Re-FORC enables: 1) early […]
Structural Plasticity as Active Inference: A Biologically-Inspired Architecture for Homeostatic Control
arXiv:2511.02241v1 Announce Type: cross Abstract: Traditional neural networks, while powerful, rely on biologically implausible learning mechanisms such as global backpropagation. This paper introduces the Structurally Adaptive Predictive Inference Network (SAPIN), a novel computational model inspired by the principles of active inference and the morphological plasticity observed in biological neural cultures. SAPIN operates on a 2D […]
Not All Clients Are Equal: Collaborative Model Personalization on Heterogeneous Multi-Modal Clients
arXiv:2506.11024v3 Announce Type: replace-cross Abstract: As AI becomes more personal, e.g., Agentic AI, there is an increasing need for personalizing models for various use cases. Personalized federated learning (PFL) enables each client to collaboratively leverage other clients’ knowledge for better adaptation to the task of interest, without privacy risks. Despite its potential, existing PFL methods […]
Federated Quantum Kernel Learning for Anomaly Detection in Multivariate IoT Time-Series
arXiv:2511.02301v1 Announce Type: cross Abstract: The rapid growth of industrial Internet of Things (IIoT) systems has created new challenges for anomaly detection in high-dimensional, multivariate time-series, where privacy, scalability, and communication efficiency are critical. Classical federated learning approaches mitigate privacy concerns by enabling decentralized training, but they often struggle with highly non-linear decision boundaries and […]
Personalized Decision Modeling: Utility Optimization or Textualized-Symbolic Reasoning
arXiv:2511.02194v1 Announce Type: new Abstract: Decision-making models for individuals, particularly in high-stakes scenarios like vaccine uptake, often diverge from population optimal predictions. This gap arises from the uniqueness of the individual decision-making process, shaped by numerical attributes (e.g., cost, time) and linguistic influences (e.g., personal preferences and constraints). Developing upon Utility Theory and leveraging the […]
Human-Machine Ritual: Synergic Performance through Real-Time Motion Recognition
arXiv:2511.02351v1 Announce Type: cross Abstract: We introduce a lightweight, real-time motion recognition system that enables synergic human-machine performance through wearable IMU sensor data, MiniRocket time-series classification, and responsive multimedia control. By mapping dancer-specific movement to sound through somatic memory and association, we propose an alternative approach to human-machine collaboration, one that preserves the expressive depth […]
3DViT-GAT: A Unified Atlas-Based 3D Vision Transformer and Graph Learning Framework for Major Depressive Disorder Detection Using Structural MRI Data
arXiv:2509.12143v3 Announce Type: replace-cross Abstract: Major depressive disorder (MDD) is a prevalent mental health condition that negatively impacts both individual well-being and global public health. Automated detection of MDD using structural magnetic resonance imaging (sMRI) and deep learning (DL) methods holds increasing promise for improving diagnostic accuracy and enabling early intervention. Most existing methods employ […]
AutoAdv: Automated Adversarial Prompting for Multi-Turn Jailbreaking of Large Language Models
arXiv:2511.02376v1 Announce Type: cross Abstract: Large Language Models (LLMs) remain vulnerable to jailbreaking attacks where adversarial prompts elicit harmful outputs, yet most evaluations focus on single-turn interactions while real-world attacks unfold through adaptive multi-turn conversations. We present AutoAdv, a training-free framework for automated multi-turn jailbreaking that achieves up to 95% attack success rate on Llama-3.1-8B […]
Optimal-Agent-Selection: State-Aware Routing Framework for Efficient Multi-Agent Collaboration
arXiv:2511.02200v1 Announce Type: new Abstract: The emergence of multi-agent systems powered by large language models (LLMs) has unlocked new frontiers in complex task-solving, enabling diverse agents to integrate unique expertise, collaborate flexibly, and address challenges unattainable for individual models. However, the full potential of such systems is hindered by rigid agent scheduling and inefficient coordination […]