arXiv:2603.08345v1 Announce Type: cross Abstract: Phylodynamics is used to estimate epidemic dynamics from phylogenetic trees or genomic sequences of pathogens, but the likelihood calculations needed can be challenging for complex models. We present a neural Bayes estimator (NBE) for key epidemic quantities: the reproduction number, prevalence, and cumulative infections through time. By performing quantile regression […]
Visual Self-Fulfilling Alignment: Shaping Safety-Oriented Personas via Threat-Related Images
arXiv:2603.08486v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) face safety misalignment, where visual inputs enable harmful outputs. To address this, existing methods require explicit safety labels or contrastive data; yet, threat-related concepts are concrete and visually depictable, while safety concepts, like helpfulness, are abstract and lack visual referents. Inspired by the Self-Fulfilling mechanism […]
Towards Batch-to-Streaming Deep Reinforcement Learning for Continuous Control
arXiv:2603.08588v1 Announce Type: cross Abstract: State-of-the-art deep reinforcement learning (RL) methods have achieved remarkable performance in continuous control tasks, yet their computational complexity is often incompatible with the constraints of resource-limited hardware, due to their reliance on replay buffers, batch updates, and target networks. The emerging paradigm of streaming deep RL addresses this limitation through […]
Scale Space Diffusion
arXiv:2603.08709v1 Announce Type: cross Abstract: Diffusion models degrade images through noise, and reversing this process reveals an information hierarchy across timesteps. Scale-space theory exhibits a similar hierarchy via low-pass filtering. We formalize this connection and show that highly noisy diffusion states contain no more information than small, downsampled images – raising the question of why […]
CAN-STRESS: A Real-World Multimodal Dataset for Understanding Cannabis Use, Stress, and Physiological Responses
arXiv:2503.19935v2 Announce Type: replace Abstract: Coping with stress is one of the most frequently cited reasons for chronic cannabis use. Therefore, it is hypothesized that cannabis users exhibit distinct physiological stress responses compared to non-users, and these differences would be more pronounced during moments of consumption. However, there is a scarcity of publicly available datasets […]
Let’s Think in Two Steps: Mitigating Agreement Bias in MLLMs with Self-Grounded Verification
arXiv:2507.11662v3 Announce Type: replace Abstract: Verifiers–functions assigning rewards to agent behavior–have been key to AI progress in math, code, and games. However, extending gains to domains without clear-cut success criteria remains a challenge: while humans can recognize desired outcomes, translating this intuition into scalable rules is nontrivial. Multimodal LLMs (MLLMs) offer a promising solution, given […]
Listen to the Layers: Mitigating Hallucinations with Inter-Layer Disagreement
arXiv:2602.09486v2 Announce Type: replace-cross Abstract: Pretrained Large Language Models (LLMs) are prone to generating fluent yet factually incorrect text-a phenomenon known as hallucinations, undermining their reliability and utility in downstream tasks. We hypothesize that a generated text span’s factuality is correlated with its representational instability across the model’s internal layers. Based on this, we propose […]
Mining Beyond the Bools: Learning Data Transformations and Temporal Specifications
arXiv:2603.06710v1 Announce Type: cross Abstract: Mining specifications from execution traces presents an automated way of capturing characteristic system behaviors. However, existing approaches are largely restricted to Boolean abstractions of events, limiting their ability to express data-aware properties. In this paper, we extend mining procedures to operate over richer datatypes. We first establish candidate functions in […]
Mean Flow Policy with Instantaneous Velocity Constraint for One-step Action Generation
arXiv:2602.13810v2 Announce Type: replace-cross Abstract: Learning expressive and efficient policy functions is a promising direction in reinforcement learning (RL). While flow-based policies have recently proven effective in modeling complex action distributions with a fast deterministic sampling process, they still face a trade-off between expressiveness and computational burden, which is typically controlled by the number of […]
GWAS Summary Statistic Tool: A Meta-Analysis and Parsing Tool for Polygenic Risk Score Calculation
arXiv:2603.06756v1 Announce Type: new Abstract: Motivation: GWAS (genome-wide association study) summary statistic files are essential inputs for polygenic risk score (PRS) calculation. However, identifying suitable files across thousands of catalog entries typically requires downloading large datasets and manually inspecting their column structures, a process that is both time-consuming and storage-intensive. Results: We present GWASPoker, a […]
Benchmarking 80 binary phenotypes from the openSNP dataset using deep learning algorithms and polygenic risk score tools
arXiv:2603.06768v1 Announce Type: new Abstract: Genotype-phenotype prediction plays a crucial role in identifying disease-causing single nucleotide polymorphisms and precision medicine. In this manuscript, we benchmark the performance of various machine/deep learning algorithms and polygenic risk score tools on 80 binary phenotypes extracted from the openSNP dataset. After cleaning and extraction, the genotype data for each […]
SplitAgent: A Privacy-Preserving Distributed Architecture for Enterprise-Cloud Agent Collaboration
arXiv:2603.08221v1 Announce Type: cross Abstract: Enterprise adoption of cloud-based AI agents faces a fundamental privacy dilemma: leveraging powerful cloud models requires sharing sensitive data, while local processing limits capability. Current agent frameworks like MCP and A2A assume complete data sharing, making them unsuitable for enterprise environments with confidential information. We present SplitAgent, a novel distributed […]