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Written by: Jamie Collier, Robin Grunewald Germany has reclaimed its position as a primary focus for cyber extortion in Europe. While data leak site (DLS) posts rose almost 50% globally in 2025, Googl

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Research

The Linear Centroids Hypothesis: How Deep Network Features Represent Data

DGX agent

arXiv:2604.11962v1 Announce Type: new Abstract: Identifying and understanding the features that a deep network (DN) extracts from its inputs to produce its outputs is a focal point of interpretability

researcharxiv-cs-lg

15 Apr 2026

The Non-Optimality of Scientific Knowledge: Path Dependence, Lock-In, and The Local Minimum Trap

arXiv:2604.11828v1 Announce Type: new Abstract: Science is widely regarded as humanity's most reliable method for uncovering truths about the natural world. Yet the trajectory of scientific discovery

The Stackelberg Speaker: Optimizing Persuasive Communication in Social Deduction Games

arXiv:2510.09087v2 Announce Type: replace Abstract: Large language model (LLM) agents have shown remarkable progress in social deduction games (SDGs). However, existing approaches primarily focus on i

safetyarxiv-cs-ai

Think Through Uncertainty: Improving Long-Form Generation Factuality via Reasoning Calibration

arXiv:2604.12046v1 Announce Type: new Abstract: Large language models (LLMs) often hallucinate in long-form generation. Existing approaches mainly improve factuality through post-hoc revision or reinf

researcharxiv-cs-cl

Time-reversed Flow Matching with Worst Transport in High-dimensional Latent Space for Image Anomaly Detection

arXiv:2508.05461v3 Announce Type: replace Abstract: Likelihood-based deep generative models have been widely investigated for Image Anomaly Detection (IAD), particularly Normalizing Flows, yet their s

local-aiarxiv-cs-cv

TimeSAF: Towards LLM-Guided Semantic Asynchronous Fusion for Time Series Forecasting

arXiv:2604.12648v1 Announce Type: cross Abstract: Despite the recent success of large language models (LLMs) in time-series forecasting, most existing methods still adopt a Deep Synchronous Fusion str

Token Encoding for Semantic Recovery

arXiv:2604.12931v1 Announce Type: cross Abstract: Token-based semantic communication is promising for future wireless networks, as it can compact semantic tokens under very limited channel capacity. H

researcharxiv-cs-lg

Token-Level Policy Optimization: Linking Group-Level Rewards to Token-Level Aggregation via Sequence-Level Likelihood

arXiv:2604.12736v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) has significantly advanced the reasoning ability of large language models (LLMs), particularly in their mathem

safetyarxiv-cs-cl

Topology-Aware Reasoning over Incomplete Knowledge Graph with Graph-Based Soft Prompting

arXiv:2604.12503v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown remarkable capabilities across various tasks but remain prone to hallucinations in knowledge-intensive scenari

Towards Long-horizon Agentic Multimodal Search

arXiv:2604.12890v1 Announce Type: cross Abstract: Multimodal deep search agents have shown great potential in solving complex tasks by iteratively collecting textual and visual evidence. However, mana

Towards Platonic Representation for Table Reasoning: A Foundation for Permutation-Invariant Retrieval

arXiv:2604.12133v1 Announce Type: new Abstract: Historical approaches to Table Representation Learning (TRL) have largely adopted the sequential paradigms of Natural Language Processing (NLP). We argu

safetyarxiv-cs-ai

Transforming External Knowledge into Triplets for Enhanced Retrieval in RAG of LLMs

model-releasesarxiv-cs-cl

arXiv:2604.12610v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) mitigates hallucination in large language models (LLMs) by incorporating external knowledge during generation. Howe

Turbo-DDCM: Fast and Flexible Zero-Shot Diffusion-Based Image Compression

arXiv:2511.06424v2 Announce Type: replace-cross Abstract: While zero-shot diffusion-based compression methods have seen significant progress in recent years, they remain notoriously slow and computati

Uncertainty-Aware Image Classification In Biomedical Imaging Using Spectral-normalized Neural Gaussian Processes

arXiv:2602.02370v2 Announce Type: replace Abstract: Accurate histopathologic interpretation is key for clinical decision-making; however, current deep learning models for digital pathology are often o

safetyarxiv-cs-cv

UniMark: Unified Adaptive Multi-bit Watermarking for Autoregressive Image Generators

arXiv:2604.11843v1 Announce Type: new Abstract: Invisible watermarking for autoregressive (AR) image generation has recently gained attention as a means of protecting image ownership and tracing AI-ge

Unlocking the Potential of Grounding DINO in Videos: Parameter-Efficient Adaptation for Limited-Data Spatial-Temporal Localization

model-releasesarxiv-cs-cv

arXiv:2604.12346v1 Announce Type: new Abstract: Spatio-temporal video grounding (STVG) aims to localize queried objects within dynamic video segments. Prevailing fully-trained approaches are notorious

Unveiling the Surprising Efficacy of Navigation Understanding in End-to-End Autonomous Driving

arXiv:2604.12208v1 Announce Type: cross Abstract: Global navigation information and local scene understanding are two crucial components of autonomous driving systems. However, our experimental result

local-aiarxiv-cs-ai

V-Nutri: Dish-Level Nutrition Estimation from Egocentric Cooking Videos

model-releasesarxiv-cs-cv

arXiv:2604.11913v1 Announce Type: new Abstract: Nutrition estimation of meals from visual data is an important problem for dietary monitoring and computational health, but existing approaches largely

Variational Autoencoding Discrete Diffusion with Enhanced Dimensional Correlations Modeling

arXiv:2505.17384v2 Announce Type: replace-cross Abstract: Discrete diffusion models have recently shown great promise for modeling complex discrete data, with masked diffusion models (MDMs) offering a

VidTAG: Temporally Aligned Video to GPS Geolocalization with Denoising Sequence Prediction at a Global Scale

arXiv:2604.12159v1 Announce Type: new Abstract: The task of video geolocalization aims to determine the precise GPS coordinates of a video's origin and map its trajectory; with applications in forensi

VISTA: Validation-Informed Trajectory Adaptation via Self-Distillation

arXiv:2604.12044v1 Announce Type: cross Abstract: Deep learning models may converge to suboptimal solutions despite strong validation accuracy, masking an optimization failure we term Trajectory Devia

Visual Diffusion Models are Geometric Solvers

arXiv:2510.21697v2 Announce Type: replace Abstract: In this paper we show that visual diffusion models can serve as effective geometric solvers: they can directly reason about geometric problems by wo

Visual Preference Optimization with Rubric Rewards

arXiv:2604.13029v1 Announce Type: cross Abstract: The effectiveness of Direct Preference Optimization (DPO) depends on preference data that reflect the quality differences that matter in multimodal ta

VPTracker: Global Vision-Language Tracking via Visual Prompt

arXiv:2512.22799v2 Announce Type: replace Abstract: Vision-Language Tracking aims to continuously localize objects described by a visual template and a language description. Existing methods, however,

local-aiarxiv-cs-cv

VULCAN: Vision-Language-Model Enhanced Multi-Agent Cooperative Navigation for Indoor Fire-Disaster Response

model-releasesarxiv-cs-ro

arXiv:2604.12831v1 Announce Type: new Abstract: Indoor fire disasters pose severe challenges to autonomous search and rescue due to dense smoke, high temperatures, and dynamically evolving indoor envi

Hardware

Vultr Easily Meets EU Digital Readiness Operations Act (DORA) Requirements in Independent Assessment

Vultr, a cloud infrastructure provider, has successfully met the requirements of the EU's Digital Operational Resilience Act (DORA) according to an independent assessment, demonstrating its compliance

hardwarevultr

We believe the future of intelligence is decentralized. Tonight we're opening the new @agi_inc office in SF with a dinner, gathering chipmak…

We believe the future of intelligence is decentralized. Tonight we're opening the new @agi_inc office in SF with a dinner, gathering chipmakers, OEM partners, and investors to discuss what happens whe

local-aidiv-garg--x

Why and When Visual Token Pruning Fails? A Study on Relevant Visual Information Shift in MLLMs Decoding

arXiv:2604.12358v1 Announce Type: new Abstract: Recently, visual token pruning has been studied to handle the vast number of visual tokens in Multimodal Large Language Models. However, we observe that

Why Did Apple Fall: Evaluating Curiosity in Large Language Models

arXiv:2510.20635v2 Announce Type: replace-cross Abstract: Curiosity serves as a pivotal conduit for human beings to discover and learn new knowledge. Recent advancements of large language models (LLMs

WikiSeeker: Rethinking the Role of Vision-Language Models in Knowledge-Based Visual Question Answering

model-releasesarxiv-cs-cl

arXiv:2604.05818v2 Announce Type: replace-cross Abstract: Multi-modal Retrieval-Augmented Generation (RAG) has emerged as a highly effective paradigm for Knowledge-Based Visual Question Answering (KB-

Industry

8 AI and data trends shaping financial services in 2026

Databricks outlines eight key AI and data trends expected to transform financial services in 2026, likely covering advancements in generative AI, real-time data processing, machine learning for risk m

industrydatabricks

A Comparative Theoretical Analysis of Entropy Control Methods in Reinforcement Learning

arXiv:2604.09676v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a key approach for enhancing reasoning in large language models (LLMs), yet scalable training is often hindered

safetyarxiv-cs-ai

A Comparison of Multi-View Stereo Methods for Photogrammetric 3D Reconstruction: From Traditional to Learning-Based Approaches

arXiv:2604.10246v1 Announce Type: new Abstract: Photogrammetric 3D reconstruction has long relied on traditional Structure-from-Motion (SfM) and Multi-View Stereo (MVS) methods, which provide high acc

A Diffusion-Contrastive Graph Neural Network with Virtual Nodes for Wind Nowcasting in Unobserved Regions

arXiv:2604.10328v1 Announce Type: cross Abstract: Accurate weather nowcasting remains one of the central challenges in atmospheric science, with critical implications for climate resilience, energy se

A Minimal Model of Representation Collapse: Frustration, Stop-Gradient, and Dynamics

arXiv:2604.09979v1 Announce Type: cross Abstract: Self-supervised representation learning is central to modern machine learning because it extracts structured latent features from unlabeled data and e

Applications

A Multiparty Homomorphic Encryption Approach to Confidential Federated Kaplan Meier Survival Analysis

arXiv:2412.20495v2 Announce Type: replace-cross Abstract: The proliferation of real-world health data enables multi-institutional survival studies, yet privacy constraints preclude centralizing sensit

applicationsarxiv-cs-ai

A Structured Clustering Approach for Inducing Media Narratives

arXiv:2604.10368v1 Announce Type: new Abstract: Media narratives wield tremendous power in shaping public opinion, yet computational approaches struggle to capture the nuanced storytelling structures

researcharxiv-cs-cl

A Triadic Suffix Tokenization Scheme for Numerical Reasoning

arXiv:2604.11582v1 Announce Type: cross Abstract: Standard subword tokenization methods fragment numbers inconsistently, causing large language models (LLMs) to lose positional and decimal structure -

A Unified Theory of Sparse Dictionary Learning in Mechanistic Interpretability: Piecewise Biconvexity and Spurious Minima

arXiv:2512.05534v4 Announce Type: replace-cross Abstract: As AI models achieve remarkable capabilities across diverse domains, understanding what representations they learn and how they encode concept

A3-FPN: Asymptotic Content-Aware Pyramid Attention Network for Dense Visual Prediction

arXiv:2604.10210v1 Announce Type: new Abstract: Learning multi-scale representations is the common strategy to tackle object scale variation in dense prediction tasks. Although existing feature pyrami

ActDistill: General Action-Guided Self-Derived Distillation for Efficient Vision-Language-Action Models

arXiv:2511.18082v3 Announce Type: replace Abstract: Recent Vision-Language-Action (VLA) models have shown impressive flexibility and generalization, yet their deployment in robotic manipulation remain

tutorialsarxiv-cs-cv

Adapting 2D Multi-Modal Large Language Model for 3D CT Image Analysis

arXiv:2604.10233v1 Announce Type: cross Abstract: 3D medical image analysis is of great importance in disease diagnosis and treatment. Recently, multimodal large language models (MLLMs) have exhibited

Adaptive Multi-Expert Reasoning via Difficulty-Aware Routing and Uncertainty-Guided Aggregation

arXiv:2604.10335v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate strong performance in math reasoning benchmarks, but their performance varies inconsistently across problems wi

researcharxiv-cs-cl

AdaQE-CG: Adaptive Query Expansion for Web-Scale Generative AI Model and Data Card Generation

arXiv:2604.09617v1 Announce Type: new Abstract: Transparent and standardized documentation is essential for building trustworthy generative AI (GAI) systems. However, existing automated methods for ge

AdvDINO: Domain-Adversarial Self-Supervised Representation Learning for Spatial Proteomics

arXiv:2508.04955v2 Announce Type: replace-cross Abstract: Self-supervised learning (SSL) has emerged as a powerful approach for learning visual representations without manual annotations. However, the

Adversarial Robustness of Graph Transformers

arXiv:2407.11764v2 Announce Type: replace Abstract: Existing studies have shown that Message-Passing Graph Neural Networks (MPNNs) are highly susceptible to adversarial attacks. In contrast, despite t

researcharxiv-cs-lg

AdvSynGNN: Structure-Adaptive Graph Neural Nets via Adversarial Synthesis and Self-Corrective Propagation

arXiv:2602.17071v2 Announce Type: replace-cross Abstract: Graph neural networks frequently encounter significant performance degradation when confronted with structural noise or non-homophilous topolo