Beyond Global Divergences: A Local-Mass Perspective on Bayesian Inference
arXiv:2606.27090v1 Announce Type: cross Abstract: Global objectives, such as KL divergence and ELBO, are widely used in Bayesian inference for…
From Hallucination to Grounding: Diagnosing Visual Spatial Intelligence via CRISP
arXiv:2606.26535v1 Announce Type: cross Abstract: Current VLM evaluations often conflate language priors with genuine spatial reasoning. To address this, we…
MiniOpt: Reasoning to Model and Solve General Optimization Problems with Limited Resources
arXiv:2606.25832v2 Announce Type: replace-cross Abstract: Achieving strong optimization generalization across diverse optimization problems while requiring limited training resources remains a…
Algorithmic Foundations of Deep Learning: Complexity-Theoretic Rates and a Characterization of Universal Approximation
arXiv:2606.26705v1 Announce Type: cross Abstract: Feedforward neural network (NN) expressivity is typically studied by emulating optimal basis-expansion schemes. While powerful,…
Latent-Mark: An Audio Watermark Robust to Neural Codec Compression
arXiv:2603.05310v3 Announce Type: replace-cross Abstract: While existing audio watermarking techniques have achieved strong robustness against traditional digital signal processing (DSP)…
Noise-Aware Boundary-Enhanced Generative Learning for Ultrasound Speckle Reduction
arXiv:2606.25009v2 Announce Type: replace-cross Abstract: Ultrasound is a non-invasive, real-time, and cost-effective imaging technique widely used in clinical diagnosis. However,…
Chai: Agentic Discovery of Cryptographic Misuse Vulnerabilities
arXiv:2606.26933v1 Announce Type: cross Abstract: AI-assisted vulnerability discovery has proven effective for bug classes like memory safety, where instrumentation confirms…
AutoSpec: Safety Rule Evolution for LLM Agents via Inductive Logic Programming
arXiv:2606.24245v2 Announce Type: cross Abstract: Large language model (LLM) agents increasingly automate complex tasks by integrating language models with external…
CALIBER: Calibrating Confidence Before and After Reasoning in Language Models
arXiv:2606.24281v1 Announce Type: cross Abstract: Reasoning language models are increasingly asked not only to answer difficult questions, but also to…