arXiv:2605.21634v1 Announce Type: new Abstract: Polygenic risk scores (PRSs) aggregate genetic effect estimates to predict disease susceptibility, yet clinical deployment often exposes raw genotype data to third-party compute infrastructure. Prior homomorphic-encryption approaches, still require trust in a designated evaluator. We present bioETH-PRS, a protocol that replaces that evaluator role with immutable smart contracts on a […]
SDPM: Survival Diffusion Probabilistic Model for Continuous-Time Survival Analysis
arXiv:2605.22776v1 Announce Type: cross Abstract: Survival analysis aims to estimate a time-to-event distribution from data with censored observations. Many existing methods either impose structural assumptions on the hazard function or discretize the time axis, which may limit flexibility and introduce approximation errors. We propose the Survival Diffusion Probabilistic Model (SDPM), a generative approach to continuous-time […]
Orchard: An Open-Source Agentic Modeling Framework
arXiv:2605.15040v2 Announce Type: replace Abstract: Agentic modeling aims to transform LLMs into autonomous agents capable of solving complex tasks through planning, reasoning, tool use, and multi-turn interaction with environments. Despite major investment, open research remains constrained by infrastructure and training gaps. Many high-performing systems rely on proprietary codebases, models, or services, while most open-source frameworks […]
CR4T: Rewrite-Based Guardrails for Adolescent LLM Safety
arXiv:2605.21609v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly embedded in adolescent digital environments, mediating information seeking, advice, and emotionally sensitive interactions. Yet existing safety mechanisms remain largely grounded in adult-centric norms and operationalize safety through refusal-oriented suppression. While such approaches may reduce immediate policy violations, they can also create conversational dead-ends, limit […]
SWE-Mutation: Can LLMs Generate Reliable Test Suites in Software Engineering?
arXiv:2605.22175v1 Announce Type: cross Abstract: Evaluating software engineering capabilities has become a core component of modern large language models (LLMs); however, the key bottleneck hindering further scaling lies not in the scarcity of high-quality solutions, but in the lack of high-quality test suites. Test suites are indispensable both for synthesizing program repair trajectories and for […]
4D-GSW: Kinematic-Aware Spatio-Temporal Consistent Watermarking for 4D Gaussian Splatting
arXiv:2605.22342v1 Announce Type: cross Abstract: While 4D Gaussian Splatting (4DGS) has revolutionized high-fidelity dynamic reconstruction, safeguarding the intellectual property of these assets remains an open challenge. Conventional steganographic techniques often neglect the underlying kinematic manifolds, triggering non-physical artifacts such as severe temporal flickering and “FVD collapse”. To address this, we propose textbf4D-GSW, a kinematic-aware watermarking […]
SDGBiasBench: Benchmarking and Mitigating Vision–Language Models’ Biases in Sustainable Development Goals
arXiv:2605.21919v1 Announce Type: cross Abstract: Assessing progress toward the Sustainable Development Goals (SDGs) requires multi-step reasoning over visual cues, contextual knowledge, and development indicators, where incomplete evidence use and imperfect evidence integration can introduce hidden prediction biases. Real-world SDG monitoring further spans both qualitative judgments and quantitative estimation. However, existing benchmarks typically evaluate these aspects […]
AgroVG: A Large-Scale Multi-Source Benchmark for Agricultural Visual Grounding
arXiv:2605.22034v1 Announce Type: cross Abstract: Visual grounding, the task of localizing objects described by natural-language expressions, is a foundational capability for agricultural AI systems, enabling applications such as selective weeding, disease monitoring, and targeted harvesting. Reliable evaluation of agricultural visual grounding remains challenging because agricultural targets are often small, repetitive, occluded, or irregularly shaped, and […]
Orchard: An Open-Source Agentic Modeling Framework
arXiv:2605.15040v2 Announce Type: replace Abstract: Agentic modeling aims to transform LLMs into autonomous agents capable of solving complex tasks through planning, reasoning, tool use, and multi-turn interaction with environments. Despite major investment, open research remains constrained by infrastructure and training gaps. Many high-performing systems rely on proprietary codebases, models, or services, while most open-source frameworks […]
Rethinking Forward Processes for Score-Based Nonlinear Data Assimilation in High Dimensions
arXiv:2604.02889v2 Announce Type: replace-cross Abstract: Data assimilation is the process of estimating the state of a dynamical system over time by combining model predictions with measurements. This task becomes challenging when the system is nonlinear and high-dimensional. To address this, score-based Bayesian filters have recently emerged. However, these methods still show unsatisfactory performance in certain […]
bioETH-PRS: Confidential Polygenic Risk Scoring without a Trusted Evaluator via Fully Homomorphic Encryption on a Programmable Blockchain
arXiv:2605.21634v1 Announce Type: new Abstract: Polygenic risk scores (PRSs) aggregate genetic effect estimates to predict disease susceptibility, yet clinical deployment often exposes raw genotype data to third-party compute infrastructure. Prior homomorphic-encryption approaches, still require trust in a designated evaluator. We present bioETH-PRS, a protocol that replaces that evaluator role with immutable smart contracts on a […]
SPECTRA: Spectral Domain-Aware Graph Generation for Imbalanced Molecular Property Regression
arXiv:2511.04838v2 Announce Type: replace-cross Abstract: Molecular property regression struggles with cases in chemically relevant target ranges that are underrepresented in datasets. Standard average error minimization approaches underperform in these highly relevant cases, and oversampling approaches lead to meaningless molecular representations. In this paper, we propose SPECTRA, a spectral, domain-aware graph generation method designed to improve […]