arXiv:2605.02699v1 Announce Type: cross Abstract: Learning data-efficient object dynamics models for robotic manipulation remains challenging, especially for deformable objects. A popular approach is to model objects as sets of 3D particles and learn their motion using graph neural networks. In practice, this is not enough to maintain physical feasibility over long horizons and may require […]
Enhancing RL Generalizability in Robotics through SHAP Analysis of Algorithms and Hyperparameters
arXiv:2605.02867v1 Announce Type: cross Abstract: Despite significant advances in Reinforcement Learning (RL), model performance remains highly sensitive to algorithm and hyperparameter configurations, while generalization gaps across environments complicate real-world deployment. Although prior work has studied RL generalization, the relative contribution of specific configurations to the generalization gap has not been quantitatively decomposed and systematically leveraged […]
Unsupervised full-field Bayesian inference of orthotropic hyperelasticity from a single biaxial test: a myocardial case study
arXiv:2510.09498v3 Announce Type: replace Abstract: Cardiac muscle tissue exhibits highly non-linear hyperelastic and orthotropic material behavior during passive deformation. Traditional constitutive identification protocols therefore combine multiple loading modes and typically require multiple specimens and substantial handling. In soft living tissues, such protocols are challenged by inter- and intra-sample variability and by manipulation-induced alterations of mechanical […]
Working Paper: Towards a Category-theoretic Comparative Framework for Artificial General Intelligence
arXiv:2603.28906v3 Announce Type: replace Abstract: AGI has become the Holly Grail of AI with the promise of level intelligence and the major Tech companies around the world are investing unprecedented amounts of resources in its pursuit. Yet, there does not exist a single formal definition and only some empirical AGI benchmarking frameworks currently exist. The […]
Co-Generative De Novo Functional Protein Design
arXiv:2605.00948v1 Announce Type: new Abstract: De novo functional protein design aims to generate protein sequences that realize specified biochemical functions without relying on evolutionary templates, enabling broad applications in biotechnology and medicine. Existing approaches adopt either direct function-to-sequence mapping or decoupled structure-sequence generation strategies but often fail to achieve functionality and foldability simultaneously. To address […]
Agentic Forecasting using Sequential Bayesian Updating of Linguistic Beliefs
arXiv:2604.18576v3 Announce Type: replace Abstract: We present the Bayesian Linguistic Forecaster (BLF), an agentic system for binary forecasting that achieves state-of-the-art performance on the ForecastBench benchmark. The system is built on three ideas. (1) Linguistic belief state: a semi-structured representation combining numerical probability estimates with natural-language evidence summaries, updated by the LLM at each step […]
Low-Latency Video Anonymization for Crowd Anomaly Detection: Privacy Versus Performance
arXiv:2410.18717v2 Announce Type: replace-cross Abstract: Recent advancements in artificial intelligence hold ample potential for monitoring applications using surveillance cameras. However, concerns about privacy and model bias have made it challenging to utilize them in public. Although de-identification approaches have been proposed in the literature, aiming to achieve a certain level of anonymization (AN), most of […]
BioVeil MATRIX: Uncovering and categorizing vulnerabilities of agentic biological AI scientists
arXiv:2605.00927v1 Announce Type: new Abstract: Agentic AI scientists equipped with domain-specific tools are rapidly entering scientific workflows across disciplines, with especially strong uptake in the life sciences where they can be used for literature synthesis, sequence analysis, and experimental planning support. While these systems accelerate biological research, they also introduce risks for dual-use applications that […]
Bayesian Credible Sets for Phylogenetic Tree Topologies with Applications to Coverage Analysis and Cross-Model Comparison
arXiv:2505.14532v2 Announce Type: replace-cross Abstract: Credible intervals and credible sets, such as highest posterior density (HPD) intervals, form an integral statistical tool in Bayesian phylogenetics, both for phylogenetic analyses and for development. Readily available for continuous parameters such as base frequencies and clock rates, the vast and complex space of tree topologies poses significant challenges […]
The Oracle’s Fingerprint: Correlated AI Forecasting Errors and the Limits of Bias Transmission
arXiv:2605.00844v1 Announce Type: cross Abstract: When large language models (LLMs) are consulted as forecasting tools, the independence of individual errors — the foundation of collective intelligence — may collapse. We test three conditions necessary for this “epistemic monoculture” to emerge. In Study 1, we show that GPT-4o, Claude, and Gemini exhibit highly correlated forecasting errors […]
Design-OS: A Specification-Driven Framework for Engineering System Design with a Control-Systems Design Case
arXiv:2603.20151v2 Announce Type: replace-cross Abstract: Engineering system design — whether mechatronic, control, or embedded — often proceeds in an ad hoc manner, with requirements left implicit and traceability from intent to parameters largely absent. Existing specification-driven and systematic design methods mostly target software, and AI-assisted tools tend to enter the workflow at solution generation rather […]
Graph Query Generation with Constraint-guided Large Language Agents
arXiv:2605.00845v1 Announce Type: cross Abstract: Knowledge Graph Question Answering (KGQA) has advanced through structured query generation, yet most efforts target RDF/SPARQL, leaving Cypher and property graphs underexplored, despite increasing demand for unified KGQA in industry settings. We propose UniQGen, a novel constraint-based framework that employs LLM agents to dynamically extract and refine representative graph query […]