arXiv:2605.05365v1 Announce Type: new
Abstract: We present ZAYA1-8B, a reasoning-focused mixture-of-experts (MoE) model with 700M active and 8B total parameters, built on Zyphra's MoE++ architecture. ZAYA1-8B's core pretraining, midtraining, and supervised fine-tuning (SFT) were performed on a full...
Horizon-Constrained Rashomon Sets for Chaotic Forecasting
arXiv:2605.05218v1 Announce Type: new
Abstract: Predictive multiplicity and chaotic dynamics represent two fundamental challenges in machine learning that have evolved independently despite their conceptual connections. We bridge this gap by introducing horizon-constrained Rashomon sets, a theoreti...
arXiv:2605.05209v1 Announce Type: new
Abstract: Neural networks that land in flat regions of the loss landscape tend to generalise better than those in sharp regions. Sharpness-Aware Minimisation exploits this to improve generalisation. But function-preserving reparameterisation can inflate the Hes...
A Self-Attentive Meta-Optimizer with Group-Adaptive Learning Rates and Weight Decay
arXiv:2605.04055v1 Announce Type: new
Abstract: Adaptive optimizers like AdamW apply uniform hyperparameters across all parameter groups, ignoring heterogeneous optimization dynamics across layers and modules. We address this limitation by proposing MetaAdamW - a new optimizer that integrates a sel...
ANDRE: An Attention-based Neuro-symbolic Differentiable Rule Extractor
arXiv:2605.04193v1 Announce Type: new
Abstract: Inductive Logic Programming (ILP) aims to learn interpretable first-order rules from data, but existing symbolic and neuro-symbolic approaches struggle to scale to noisy and probabilistic settings. Classical ILP relies on discrete combinatorial rule s...
arXiv:2605.04100v1 Announce Type: new
Abstract: Off-policy temporal-difference (TD) learning with function approximation faces a structural tradeoff among stability, projection geometry, and variance control. Emphatic TD (ETD) improves the off-policy projection geometry through follow-on emphasis, ...
arXiv:2605.02907v1 Announce Type: new
Abstract: Softmax attention maps every query--key interaction into a probability distribution, but the underlying structure remains largely unexplored. We define the \emph{energy field}, the row-centered attention logit, and show that it exhibits invariant prop...
Making the Invisible Visible: Understanding the Mismatch Between Organizational Goals and Worker Experiences in AI Adoption
arXiv:2605.03078v1 Announce Type: new
Abstract: While AI is often introduced into organizations to drive innovation and efficiency, many adoption efforts fail as workers resist and struggle to integrate these systems. These failures point to a deeper issue: workers, the very people expected to coll...
Computing Thiele Rules on Interval Elections and their Generalizations
arXiv:2605.03067v1 Announce Type: new
Abstract: Approval-based committee voting has received significant attention in the social choice community. Among the studied rules, Thiele rules, and especially Proportional Approval Voting (PAV), stand out for desirable properties such as proportional repres...
Sparse Regression under Correlation and Weak Signals: A Reproducible Benchmark of Classical and Bayesian Methods
arXiv:2605.00835v1 Announce Type: new
Abstract: Choosing between classical and Bayesian sparse regression methods involves a real trade-off: penalized estimators like Lasso run in milliseconds but give no uncertainty estimates,while Horseshoe and Spike-and-Slab priors produce full posteriors but ne...
Accelerating battery research with an AI interface between FINALES and Kadi4Mat
arXiv:2605.00909v1 Announce Type: new
Abstract: The time-consuming formation process critically impacts the longevity of sodium-ion coin cells and End Of Life (EOL) performance. This study aims to optimize formation protocols for duration efficiency, targeting high-performance outcomes while minimi...
ClinicBot: A Guideline-Grounded Clinical Chatbot with Prioritized Evidence RAG and Verifiable Citations
arXiv:2605.00846v1 Announce Type: new
Abstract: Clinical diagnosis requires answers that are accurate, verifiable, and explicitly grounded in official guidelines. While large language models excel at natural language processing, their tendency to hallucinate undermines their utility in high-stakes ...
FedACT: Concurrent Federated Intelligence across Heterogeneous Data Sources
arXiv:2605.00011v1 Announce Type: new
Abstract: Federated Learning (FL) enables collaborative intelligence across decentralized data source devices in a privacy-preserving way. While substantial research attention has been drawn to optimizing the learning process for an individual task, real-world ...
Learning physically grounded traffic accident reconstruction from public accident reports
arXiv:2605.00050v1 Announce Type: new
Abstract: Traffic accidents are routinely documented in textual reports, yet physically grounded accident reconstruction remains difficult because detailed scene measurements and expert reconstructions are scarce, costly and hard to scale. Here we formulate acc...
Automatic Causal Fairness Analysis with LLM-Generated Reporting
arXiv:2604.27011v1 Announce Type: new
Abstract: AutoML, intended as the process of automating the application of machine learning to real-world problems, is a key step for AI popularisation. Most AutoML frameworks are not accounting for the potential lack of fairness in the training data and in the...
Monitoring Neural Training with Topology: A Footprint-Predictable Collapse Index
arXiv:2604.26984v1 Announce Type: new
Abstract: Representational collapse, where embeddings become anisotropic and lose multi-scale structure, can erode downstream performance long before performance metrics react. We propose an online, topology-aware monitor for evolving neural representations tha...
When Continual Learning Moves to Memory: A Study of Experience Reuse in LLM Agents
arXiv:2604.27003v1 Announce Type: new
Abstract: Memory-augmented LLM agents offer an appealing shortcut to continual learning: rather than updating model parameters, they accumulate experience in external memory, seemingly sidestepping the stability-plasticity dilemma of parametric learning. We sho...
Compositional Meta-Learning for Mitigating Task Heterogeneity in Physics-Informed Neural Networks
arXiv:2604.26999v1 Announce Type: new
Abstract: Physics-informed neural networks (PINNs) approximate solutions of partial differential equations (PDEs) by embedding physical laws into the loss function. In parameterized PDE families, variations in coefficients or boundary/initial conditions define ...
Mini-Batch Class Composition Bias in Link Prediction
arXiv:2604.25978v1 Announce Type: new
Abstract: Prior work on node classification has shown that Graph Neural Networks (GNNs) can learn representations that transfer across graphs, when underlying graph properties are shared. For a fixed graph, one would then expect GNNs trained for link prediction...
Automated detection of pediatric congenital heart disease from phonocardiograms using deep and handcrafted feature fusion
arXiv:2604.24767v1 Announce Type: new
Abstract: Congenital heart disease (CHD) is the most common type of birth defect, impacting about 1% of live births worldwide. Echocardiography, the gold-standard diagnostic method, is costly and inaccessible in low-resource settings. Diagnosis is delayed due t...
Comparative Study of Bending Analysis using Physics-Informed Neural Networks and Numerical Dynamic Deflection in Perforated nanobeam
arXiv:2604.24768v1 Announce Type: new
Abstract: In this chapter, we investigate the bending behavior of a perforated nanobeam subjected to sinusoidal loading using an efficient and computationally robust Physics-Informed Functional Link Constrained Framework with Domain Mapping (DFL-TFC) method. Ou...
arXiv:2604.21991v1 Announce Type: new
Abstract: Multi-task optimization is a powerful approach for solving a large number of tasks in parallel. However, existing algorithms face distinct limitations: Population-based methods scale poorly and remain underexplored for large task sets. Approaches that...
Performance Anomaly Detection in Athletics: A Benchmarking System with Visual Analytics
arXiv:2604.21953v1 Announce Type: new
Abstract: Anti-doping programs rely on biological testing to detect performance-enhancing drugs, but such testing costs over $800 per sample and is limited by short detection windows for many prohibited substances. These constraints leave large portions of athl...