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Training Deep Visual Networks Beyond Loss and Accuracy Through a Dynamical Systems Approach

arXiv:2604.09716v1 Announce Type: cross Abstract: Deep visual recognition models are usually trained and evaluated using metrics such as loss and accuracy. While these measures show whether a model is

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Training-Free Model Ensemble for Single-Image Super-Resolution via Strong-Branch Compensation

DGX agent

arXiv:2604.11564v1 Announce Type: new Abstract: Single-image super-resolution has progressed from deep convolutional baselines to stronger Transformer and state-space architectures, yet the correspond

researcharxiv-cs-cv

14 Apr 2026

Training-Free Multi-User Generative Semantic Communications via Null-Space Diffusion Sampling

arXiv:2405.09866v2 Announce Type: replace-cross Abstract: In recent years, novel communication strategies have emerged to face the challenges that the increased number of connected devices and the hig

Transactional Attention: Semantic Sponsorship for KV-Cache Retention

arXiv:2604.11288v1 Announce Type: new Abstract: At K=16 tokens (0.4% of a 4K context), every existing KV-cache compression method achieves 0% on credential retrieval. The failure mode is dormant token

TreeGrad-Ranker: Feature Ranking via O(L)-Time Gradients for Decision Trees

arXiv:2602.11623v2 Announce Type: replace Abstract: We revisit the use of probabilistic values, which include the well-known Shapley and Banzhaf values, to rank features for explaining the local predi

Trustworthy Feature Importance Avoids Unrestricted Permutations

arXiv:2604.11253v1 Announce Type: cross Abstract: Feature importance methods using unrestricted permutations are flawed due to extrapolation errors; such errors appear in all non-trivial variable impo

Turing or Cantor: That is the Question

arXiv:2604.10418v1 Announce Type: new Abstract: Alan Turing is considered as a founder of current computer science together with Kurt Godel, Alonzo Church and John von Neumann. In this paper multiple

UDAPose: Unsupervised Domain Adaptation for Low-Light Human Pose Estimation

arXiv:2604.10485v1 Announce Type: cross Abstract: Low-visibility scenarios, such as low-light conditions, pose significant challenges to human pose estimation due to the scarcity of annotated low-ligh

Uncertainty-Aware Web-Conditioned Scientific Fact-Checking

arXiv:2604.11036v1 Announce Type: cross Abstract: Scientific fact-checking is vital for assessing claims in specialized domains such as biomedicine and materials science, yet existing systems often ha

Uncertainty-Based Ensemble Learning in CMR Semantic Segmentation

arXiv:2502.09269v3 Announce Type: replace Abstract: Existing methods derive clinical functional metrics from ventricular semantic segmentation in cardiac cine sequences. While performing well on overa

Uncertainty-Guided Attention and Entropy-Weighted Loss for Precise Plant Seedling Segmentation

arXiv:2604.10823v1 Announce Type: new Abstract: Plant seedling segmentation supports automated phenotyping in precision agriculture. Standard segmentation models face difficulties due to intricate bac

Unfolding 3D Gaussian Splatting via Iterative Gaussian Synopsis

arXiv:2604.11685v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has become a state-of-the-art framework for real-time, high-fidelity novel view synthesis. However, its substantial storage

UniPROT: Uniform Prototype Selection via Partial Optimal Transport with Submodular Guarantees

arXiv:2604.10952v1 Announce Type: new Abstract: Selecting prototypical examples from a source distribution to represent a target data distribution is a fundamental problem in machine learning. Existin

Universal statistical signatures of evolution in artificial intelligence architectures

arXiv:2604.10571v1 Announce Type: cross Abstract: We test whether artificial intelligence architectural evolution obeys the same statistical laws as biological evolution. Compiling 935 ablation experi

Variational Visual Question Answering for Uncertainty-Aware Selective Prediction

arXiv:2505.09591v3 Announce Type: replace-cross Abstract: Despite remarkable progress in recent years, Vision Language Models (VLMs) remain prone to overconfidence and hallucinations on tasks such as

Vector Field Synthesis with Sparse Streamlines Using Diffusion Model

arXiv:2604.09838v1 Announce Type: new Abstract: We present a novel diffusion-based framework for synthesizing 2D vector fields from sparse, coherent inputs (i.e., streamlines) while maintaining physic

VeriSim: A Configurable Framework for Evaluating Medical AI Under Realistic Patient Noise

arXiv:2604.10441v1 Announce Type: new Abstract: Medical large language models (LLMs) achieve impressive performance on standardized benchmarks, yet these evaluations fail to capture the complexity of

VERTIGO: Visual Preference Optimization for Cinematic Camera Trajectory Generation

arXiv:2604.02467v2 Announce Type: replace-cross Abstract: Cinematic camera control relies on a tight feedback loop between director and cinematographer, where camera motion and framing are continuousl

Vestibular reservoir computing

arXiv:2604.09943v1 Announce Type: new Abstract: Reservoir computing (RC) is a computational framework known for its training efficiency, making it ideal for physical hardware implementations. However,

Vibe-driven model-based engineering

arXiv:2604.10645v1 Announce Type: cross Abstract: There is a pressing need for better development methods and tools to keep up with the growing demand and increasing complexity of new software systems

Video-based Heart Rate Estimation with Angle-guided ROI Optimization and Graph Signal Denoising

arXiv:2604.11395v1 Announce Type: new Abstract: Remote photoplethysmography (rPPG) enables non-contact heart rate measurement from facial videos, but its performance is significantly degraded by facia

VisText-Mosquito: A Unified Multimodal Dataset for Visual Detection, Segmentation, and Textual Explanation on Mosquito Breeding Sites

arXiv:2506.14629v3 Announce Type: replace-cross Abstract: Mosquito-borne diseases pose a major global health risk, requiring early detection and proactive control of breeding sites to prevent outbreak

Visual Late Chunking: An Empirical Study of Contextual Chunking for Efficient Visual Document Retrieval

arXiv:2604.10167v1 Announce Type: cross Abstract: Multi-vector models dominate Visual Document Retrieval (VDR) due to their fine-grained matching capabilities, but their high storage and computational

Volumetric Ergodic Control

arXiv:2511.11533v3 Announce Type: replace-cross Abstract: Ergodic control synthesizes optimal coverage behaviors over spatial distributions for nonlinear systems. However, existing formulations model

We are aware of issues impacting model availability via the Nous Portal and are working to restore service ASAP

Nous Research posted a status update on X acknowledging service disruptions affecting model availability through their Nous Portal. The announcement indicated the team was actively working to restore

researchnous-research--x

We benchmarked TranslateGemma against 5 other LLMs on subtitle translation across 6 languages. At first glance the numbers told a clean story, but then human QA added a chapter. [D]

researchr-machinelearning

This r/MachineLearning discussion post details a hands-on benchmark study in which TranslateGemma — Google's open translation model suite built on Gemma 3, available in 4B, 12B, and 27B sizes and cove

We will read histories of today’s US in a few decades and wonder how something like this could happen. Slowly allowing Polio and Measles to …

Yann LeCun shared or engaged with a post on X expressing concern about the erosion of public health infrastructure in the United States, specifically referencing the potential resurgence of eradicated

researchyann-lecun--x

What is the AC guidance for ICML? (Or: ICML qq thread) [D]

researchr-machinelearning

This r/MachineLearning Reddit thread serves as a community Q&A ('qq thread') focused on the Area Chair (AC) process for ICML, where researchers ask and answer questions about AC guidance, responsibili

When More Thinking Hurts: Overthinking in LLM Test-Time Compute Scaling

arXiv:2604.10739v1 Announce Type: new Abstract: Scaling test-time compute through extended chains of thought has become a dominant paradigm for improving large language model reasoning. However, exist

When Verification Fails: How Compositionally Infeasible Claims Escape Rejection

arXiv:2604.10990v1 Announce Type: cross Abstract: Scientific claim verification, the task of determining whether claims are entailed by scientific evidence, is fundamental to establishing discoveries

Why Do Large Language Models Generate Harmful Content?

arXiv:2604.11663v1 Announce Type: new Abstract: Large Language Models (LLMs) have been shown to generate harmful content. However, the underlying causes of such behavior remain under explored. We prop

Why Do Multilingual Reasoning Gaps Emerge in Reasoning Language Models?

arXiv:2510.27269v3 Announce Type: replace-cross Abstract: Reasoning language models (RLMs) achieve strong performance on complex reasoning tasks, yet they still exhibit a multilingual reasoning gap, p

Wolkowicz-Styan Upper Bound on the Hessian Eigenspectrum for Cross-Entropy Loss in Nonlinear Smooth Neural Networks

arXiv:2604.10202v1 Announce Type: cross Abstract: Neural networks (NNs) are central to modern machine learning and achieve state-of-the-art results in many applications. However, the relationship betw

WOODELF-HD: Efficient Background SHAP for High-Depth Decision Trees

arXiv:2604.10569v1 Announce Type: new Abstract: Decision-tree ensembles are a cornerstone of predictive modeling, and SHAP is a standard framework for interpreting their predictions. Among its variant

X-SYS: A Reference Architecture for Interactive Explanation Systems

arXiv:2602.12748v3 Announce Type: replace Abstract: The explainable AI (XAI) research community has proposed numerous technical methods, yet deploying explainability as systems remains challenging: In

XD-MAP: Cross-Modal Domain Adaptation via Semantic Parametric Maps for Scalable Training Data Generation

arXiv:2601.14477v2 Announce Type: replace-cross Abstract: Until open-world foundation models match the performance of specialized approaches, deep learning systems remain dependent on task- and sensor

You can decompose models into a graph database [N]

researchr-machinelearning

This Reddit post from r/MachineLearning discusses the concept of decomposing machine learning models into a graph database representation, treating a model's components — such as layers, weights, and

Zero-Shot Quantization via Weight-Space Arithmetic

arXiv:2604.03420v2 Announce Type: replace-cross Abstract: We show that robustness to post-training quantization (PTQ) is a transferable direction in weight space. We call this direction the quantizati

Zero-Shot Synthetic-to-Real Handwritten Text Recognition via Task Analogies

arXiv:2604.09713v1 Announce Type: new Abstract: Handwritten Text Recognition (HTR) models trained on synthetic handwriting often struggle to generalize to real text, and existing adaptation methods st

Zero-shot World Models Are Developmentally Efficient Learners

arXiv:2604.10333v1 Announce Type: new Abstract: Young children demonstrate early abilities to understand their physical world, estimating depth, motion, object coherence, interactions, and many other

2D or 3D: Who Governs Salience in VLA Models? -- Tri-Stage Token Pruning Framework with Modality Salience Awareness

arXiv:2604.09244v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have emerged as the mainstream of embodied intelligence. Recent VLA models have expanded their input modalities fr

3D-VCD: Hallucination Mitigation in 3D-LLM Embodied Agents through Visual Contrastive Decoding

arXiv:2604.08645v1 Announce Type: cross Abstract: Large multimodal models are increasingly used as the reasoning core of embodied agents operating in 3D environments, yet they remain prone to hallucin

A Closer Look at the Application of Causal Inference in Graph Representation Learning

arXiv:2604.08890v1 Announce Type: cross Abstract: Modeling causal relationships in graph representation learning remains a fundamental challenge. Existing approaches often draw on theories and methods

A fast and Generic Energy-Shifting Transformer for Hybrid Monte Carlo Radiotherapy Calculation

arXiv:2604.09157v1 Announce Type: cross Abstract: We introduce a novel learning framework for accelerated Monte Carlo (MC) dose calculation termed Energy-Shifting. This approach leverages deep learnin

A novel hybrid approach for positive-valued DAG learning

arXiv:2604.08935v1 Announce Type: cross Abstract: Causal discovery from observational data remains a fundamental challenge in machine learning and statistics, particularly when variables represent inh

A Predictive View on Streaming Hidden Markov Models

arXiv:2604.09208v1 Announce Type: cross Abstract: We develop a predictive-first optimisation framework for streaming hidden Markov models. Unlike classical approaches that prioritise full posterior re

A Semi-Automated Framework for 3D Reconstruction of Medieval Manuscript Miniatures

arXiv:2604.08610v1 Announce Type: new Abstract: This paper presents a semi-automated framework for transforming two-dimensional miniatures from medieval manuscripts into three-dimensional digital mode

Accelerating Transformer-Based Monocular SLAM via Geometric Utility Scoring

arXiv:2604.08718v1 Announce Type: cross Abstract: Geometric Foundation Models (GFMs) have recently advanced monocular SLAM by providing robust, calibration-free 3D priors. However, deploying these mod