arXiv:2604.25750v1 Announce Type: new Abstract: Dengue virus transmission models commonly assume an exponential distribution for the mosquito extrinsic incubation period (EIP), potentially oversimplifying biological variability. We developed a stochastic mechanistic dengue transmission model comparing epidemic dynamics under commonly assumed exponential (EXP) versus experimentally derived (ED) EIP distributions. Our results show that using an experimentally derived […]
Semi-Markov Reinforcement Learning for City-Scale EV Ride-Hailing with Feasibility-Guaranteed Actions
arXiv:2604.25848v1 Announce Type: new Abstract: We study city-scale control of electric-vehicle (EV) ride-hailing fleets where dispatch, repositioning, and charging decisions must respect charger and feeder limits under uncertain, spatially correlated demand and travel times. We formulate the problem as a hex-grid semi-Markov decision process (semi-MDP) with mixed actions — discrete actions for serving, repositioning, and […]
GCA-BULF: A Bottom-Up Framework for Short-Term Load Forecasting Using Grouped Critical Appliances
arXiv:2604.24766v1 Announce Type: cross Abstract: With the rise of time-of-use and tiered electricity pricing, energy consumers are encouraged to adopt peak-shifting strategies by automatically controlling high-power appliances. These help lower energy costs while enhancing the power grid’s stability. To support such energy management with high resilience and responsiveness, reliable short-term load forecasting (STLF) plays a […]
Semantic Denial of Service in LLM-controlled robots
arXiv:2604.24790v1 Announce Type: cross Abstract: Safety-oriented instruction-following is supposed to keep LLM-controlled robots safe. We show it also creates an availability attack surface. By injecting short safety-plausible phrases (1-5 tokens) into a robots audio channel, an adversary can trigger the models safety reasoning to halt or disrupt execution without jailbreaking the model or overriding its […]
Versioned Late Materialization for Ultra-Long Sequence Training in Recommendation Systems at Scale
arXiv:2604.24806v1 Announce Type: cross Abstract: Modern Deep Learning Recommendation Models (DLRMs) follow scaling laws with sequence length, driving the frontier toward ultra-long User Interaction History (UIH). However, the industry-standard “Fat Row” paradigm, which pre-materializes these sequences into every training example, creates a storage and I/O wall where data infrastructure usage exceeds GPU training capacity due […]
PI-TTA: Physics-Informed Source-Free Test-Time Adaptation for Robust Human Activity Recognition on Mobile Devices
arXiv:2604.25435v1 Announce Type: new Abstract: Source-free test-time adaptation (TTA) is appealing for mobile and wearable sensing because it enables on-device personalization from unlabeled test streams without centralizing private data. However, sensor-based human activity recognition (HAR) poses challenges that are less pronounced in standard vision benchmarks: behavioral inertial streams are temporally correlated and often exhibit within-session […]
Improving Zero-Shot Offline RL via Behavioral Task Sampling
arXiv:2604.25496v1 Announce Type: new Abstract: Offline zero-shot reinforcement learning (RL) aims to learn agents that optimize unseen reward functions without additional environment interaction. The standard approach to this problem trains task-conditioned policies by sampling task vectors that define linear reward functions over learned state representations. In most existing algorithms, these task vectors are randomly sampled, […]
Automated Adversarial Collaboration for Advancing Theory Building in the Cognitive Sciences
arXiv:2604.25521v1 Announce Type: new Abstract: Cognitive science often evaluates theories through narrow paradigms and local model comparisons, limiting the integration of evidence across tasks and realizations. We introduce an automated adversarial collaboration framework for adjudicating among competing theories even when the candidate models and experiments must be discovered during the adjudication process. The system combines […]
DualFact+: A Multimodal Fact Verification Framework for Procedural Video Understanding
arXiv:2604.25584v1 Announce Type: new Abstract: We introduce DualFact, a dual-layer, multimodal factuality evaluation framework for procedural video captioning. DualFact separates factual correctness into conceptual facts, capturing abstract semantic roles (e.g., Action, Ingredient, Tool, Location), and contextual facts, capturing their grounded predicate-argument realizations in video. To support complete and role-consistent evaluation, DualFact incorporates implicit argument augmentation […]
OxyGent: Making Multi-Agent Systems Modular, Observable, and Evolvable via Oxy Abstraction
arXiv:2604.25602v1 Announce Type: new Abstract: Deploying production-ready multi-agent systems (MAS) in complex industrial environments remains challenging due to limitations in scalability, observability, and autonomous evolution. We present OxyGent, an open-source framework that enables modular, observable, and evolvable MAS via a unified Oxy abstraction, in which agents, tools, LLMs, and reasoning flows are encapsulated as pluggable […]
HotComment: A Benchmark for Evaluating Popularity of Online Comments
arXiv:2604.25614v1 Announce Type: new Abstract: Online comments play a crucial role in shaping public sentiment and opinion dynamics on social media. However, evaluating their popularity remains challenging, not only because it depends on linguistic quality, originality, and emotional resonance, but also because stylistic preferences vary widely across platforms and user groups, causing the same comment […]
RADD: Retrieval-Augmented Discrete Diffusion for Multi-Modal Knowledge Graph Completion
arXiv:2604.25693v1 Announce Type: new Abstract: Most multi-modal knowledge graph completion (MMKGC) models use one embedding scorer to do both retrieval over the full entity set and final decision making. We argue that this coupling is a core bottleneck: global high-recall search and local fine-grained disambiguation require different inductive biases. Therefore, we propose a Retrieval-Augmented Discrete […]