Neural Architecture Search for Generative Adversarial Networks: A Comprehensive Review and Critical Analysis
arXiv:2606.26169v1 Announce Type: new
Abstract: Neural Architecture Search (NAS) has emerged as a pivotal technique in optimizing the design of Generative Adversarial Networks (GANs), automating the search for effective architectures while addressing the challenges inherent in manual design. This p...
Life After Benchmark Saturation: A Case Study of CORE-Bench
arXiv:2606.26158v1 Announce Type: new
Abstract: When a benchmark's accuracy saturates, it is often retired and replaced with a more challenging version. We show that this approach privileges accuracy and misses the opportunity to study six other key dimensions of agent performance: construct validi...
A Spectral Phase Diagram for Binary Few-Shot Classification: Intrinsic Dimensionality, Geometric Saturation, and Representational Diagnosis
arXiv:2606.24903v1 Announce Type: new
Abstract: Deciding when to stop collecting labeled examples is a fundamental but undertheorized problem in applied machine learning. The saturation index $S(K) = \operatorname{erank}(\widehat{\Sigma}_W^{(K)}) / K$ measures the ratio of the effective rank of the...
The Hitchhiker's Guide to Agentic AI: From Foundations to Systems
arXiv:2606.24937v1 Announce Type: new
Abstract: The Hitchhiker's Guide to Agentic AI is a comprehensive practitioner's reference for building autonomous AI systems. The book covers the full stack from first principles to production deployment, organized around a central thesis: building great agent...
Weight-Space Geometry of Offline Reasoning Training
arXiv:2606.23740v1 Announce Type: new
Abstract: Offline reinforcement-learning losses (RFT, RIFT, DFT, Offline GRPO, DPO) are widely used to distill reasoning from large teachers into smaller students, and are typically compared on downstream accuracy alone. We ask whether they are mechanistically ...
A Survey on Federated Causal Discovery and Inference
arXiv:2606.23741v1 Announce Type: new
Abstract: Causal reasoning, which encompasses the discovery of causal structures and the inference of causal effects, is fundamental to data-driven decision making. In practice, data for reliable causal analysis are often distributed across institutions and can...
Low-power analogue neural networks with trainable nonlinear connections for continuous control
arXiv:2606.23742v1 Announce Type: new
Abstract: Physical neural networks promise low-power machine learning by computing directly with analogue device physics, but most architectures force nonlinear device responses to act as scalar weights. Inspired by Kolmogorov-Arnold networks, we place trainabl...
Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery
arXiv:2606.23757v1 Announce Type: new
Abstract: Extracting interpretable governing equations from sparse, noisy chemical time-series data remains difficult because discrete reaction topology and continuous kinetic parameters are tightly coupled. We present PC-MCMC-CIGP, a reproducible gray-box work...
On the Identifiability of User Adaptation in Co-Adaptive Neural Interfaces
arXiv:2606.20569v1 Announce Type: new
Abstract: We analyze identifiability in co-adaptive human-machine systems. We show that closed-loop encoder estimates do not uniquely identify user adaptation, but instead reflect properties of the joint system. We discuss implications for interpreting behavior...
arXiv:2606.19361v1 Announce Type: new
Abstract: Identification conditions describe the computability of a target query or parameter of interest as a function of the type and amount of information available. In causal identification, this information is often expressed in the form of a causal graph,...
When to Trust, How to Distill: Multi-Foundation Model Guidance for Lightweight, Robust Scientific Time Series Forecasting
arXiv:2606.19363v1 Announce Type: new
Abstract: The deployment of Time-Series Foundation Models (TSFMs) in physical sciences is hindered by a critical trade-off: while these models encode rich, universal temporal dynamics, they suffer from severe distributional misalignment when applied zero-shot t...
The US AI rulebook is being rewritten. Your compliance team can't wait
America's AI regulatory landscape just had a month that made legal counsel everywhere reach for stronger coffee. Colorado's landmark AI Act, once celebrated as the country's first comprehensive state AI law, was gutted and replaced before it ever took effect.
Artemis: Anatomy-Resolved inTervention for Eliminating Multimodal NeuroImage confounderS
arXiv:2606.18287v1 Announce Type: new
Abstract: Multimodal neuroimaging, integrating functional connectivity from fMRI and structural connectivity from DTI, enables non-invasive analysis of brain networks using graph neural networks. However, demographic factors such as age and sex systematically c...
Diagnosing and Repairing Shape-Prior Shortcuts in Long-Range Single-Shot Fringe Projection Profilometry
arXiv:2606.17093v1 Announce Type: new
Abstract: Learning-based single-shot fringe projection profilometry (FPP) has been studied mostly at close range. The long-range regime (standoff beyond 1 m) remains largely unaddressed: inverse-square intensity falloff lowers fringe signal-to-noise ratio and d...
GRAPE: Guided Parameter-Space Evolution for Compact Adversarial Robustness
arXiv:2606.14865v1 Announce Type: new
Abstract: Adversarial Training (AT) improves neural network robustness, but most methods train a fixed parameter space from the start. This paper asks whether the order in which parameters become optimizable can affect the final robust solution, even when the f...
{\alpha}-Fair Insurance Pricing: A Fairness Continuum
arXiv:2606.14898v1 Announce Type: new
Abstract: Fairness in insurance pricing remains a long-standing and deeply debated puzzle. On one hand, insurers, driven by profitability considerations, set premiums that differentiate across individual risks to achieve actuarial fairness. On the other hand, i...
arXiv:2606.13703v1 Announce Type: new
Abstract: The Muddy Children Puzzle is a puzzle about knowledge and ignorance that has been inspiring for the development of epistemic logic. Who came up with it first? This is unclear. We trace the origin of the Muddy Children Puzzle through logical and litera...