All entries

Council Mode: Mitigating Hallucination and Bias in LLMs via Multi-Agent Consensus

arXiv:2604.02923v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs), particularly those employing Mixture-of-Experts (MoE) architectures, have achieved remarkable capabilities acros

Model Releases

CounterRefine: Answer-Conditioned Counterevidence Retrieval for Inference-Time Knowledge Repair in Factual Question Answering

DGX agent

arXiv:2603.16091v2 Announce Type: replace-cross Abstract: In factual question answering, many errors are not failures of access but failures of commitment: the system retrieves relevant evidence, yet

model-releasesarxiv-cs-ai

22 Apr 2026

Counting Worlds Branching Time Semantics for post-hoc Bias Mitigation in generative AI

arXiv:2604.19431v1 Announce Type: cross Abstract: Generative AI systems are known to amplify biases present in their training data. While several inference-time mitigation strategies have been propose

Applications

Courier Health, which develops an AI-powered biopharma platform for managing the end-to-end patient experience, raised a $50M Series B led by Oak HC/FT (Heather Landi/Fierce Healthcare)

Heather Landi / Fierce Healthcare: Courier Health, which develops an AI-powered biopharma platform for managing the end-to-end patient experience, raised a $50M Series B led by Oak HC/FT — Oak HC/FT N

applicationstechmeme

Coyote vs. Acme is finally getting released—with a killer trailer

model-releasesars-technica

The trailer for 'Coyote vs. Acme' has been released, offering a first look at the long-delayed live-action/animation hybrid ahead of its theatrical debut Aug. 28. The film follows Wile E. Coyote as he

CreatiParser: Generative Image Parsing of Raster Graphic Designs into Editable Layers

arXiv:2604.19632v1 Announce Type: new Abstract: Graphic design images consist of multiple editable layers, such as text, background, and decorative elements, while most generative models produce raste

Cross-cloud infrastructure innovation for the agentic enterprise

model-releasesgoogle-cloud-ai

The era of agentic AI is accelerating from human- to machine-speed operations, while also creating profound stress on legacy technology infrastructure. This new reality pushes foundational systems to

Cross-lingual Matryoshka Representation Learning across Speech and Text

arXiv:2602.19991v2 Announce Type: replace Abstract: Speakers of under-represented languages face both a language barrier, as most online knowledge is in a few dominant languages, and a modality barrie

researcharxiv-cs-cl

Cross-Model Consistency of AI-Generated Exercise Prescriptions: A Repeated Generation Study Across Three Large Language Models

arXiv:2604.19598v1 Announce Type: cross Abstract: This study compared repeated generation consistency of exercise prescription outputs across three large language models (LLMs), specifically GPT-4.1,

CrossPan: A Comprehensive Benchmark for Cross-Sequence Pancreas MRI Segmentation and Generalization

arXiv:2604.18797v1 Announce Type: new Abstract: Automatic pancreas segmentation is fundamental to abdominal MRI analysis, yet deep learning models trained on one MRI sequence often fail catastrophical

Industry

CrowdStrike brings real-time Cloud Detection and Response to Google Cloud

CrowdStrike Holdings Inc. today expanded its Cloud Detection and Response service to Google Cloud Platform, adding runtime protection for Google workloads as attackers lean on artificial intelligence

industrysiliconangle

Industry

Crypto scam lures ships into Strait of Hormuz, falsely promising safe passage

Scammers are impersonating Iranian authorities and sending fraudulent messages to shipowners demanding Bitcoin or USDT payments for safe passage through the Strait of Hormuz. The Athens-based firm MAR

industryars-technica

CulturALL: Benchmarking Multilingual and Multicultural Competence of LLMs on Grounded Tasks

arXiv:2604.19262v1 Announce Type: cross Abstract: Large language models (LLMs) are now deployed worldwide, inspiring a surge of benchmarks that measure their multilingual and multicultural abilities.

Local Ai

Curiosity-Critic: Cumulative Prediction Error Improvement as a Tractable Intrinsic Reward for World Model Training

arXiv:2604.18701v1 Announce Type: cross Abstract: Local prediction-error-based curiosity rewards focus on the current transition without considering the world model's cumulative prediction error acros

local-aiarxiv-cs-ai

Curvature-Aware PCA with Geodesic Tangent Space Aggregation for Semi-Supervised Learning

arXiv:2604.18816v1 Announce Type: cross Abstract: Principal Component Analysis (PCA) is a fundamental tool for representation learning, but its global linear formulation fails to capture the structure

Cyber Defense Benchmark: Agentic Threat Hunting Evaluation for LLMs in SecOps

arXiv:2604.19533v1 Announce Type: cross Abstract: We introduce the Cyber Defense Benchmark, a benchmark for measuring how well large language model (LLM) agents perform the core SOC analyst task of th

DADA: Dual Averaging with Distance Adaptation

arXiv:2501.10258v2 Announce Type: replace-cross Abstract: We present a novel universal gradient method for solving convex optimization problems. Our algorithm, Dual Averaging with Distance Adaptation

researcharxiv-cs-lg

DanceCrafter: Fine-Grained Text-Driven Controllable Dance Generation via Choreographic Syntax

arXiv:2604.18648v1 Announce Type: cross Abstract: Text-driven controllable dance generation remains under-explored, primarily due to the severe scarcity of high-quality datasets and the inherent diffi

researcharxiv-cs-ai

DASH-KV: Accelerating Long-Context LLM Inference via Asymmetric KV Cache Hashing

arXiv:2604.19351v1 Announce Type: new Abstract: The quadratic computational complexity of the standard attention mechanism constitutes a fundamental bottleneck for large language models in long-contex

researcharxiv-cs-cl

Data Leakage Detection and De-duplication in Large Scale Geospatial Image Datasets

arXiv:2304.02296v2 Announce Type: replace Abstract: In our study, we conducted a comprehensive analysis of three widely used datasets in the domain of building footprint extraction using deep neural n

researcharxiv-cs-cv

Day 1 at Google Cloud Next ‘26 recap

model-releasesgoogle-cloud-ai

Last year at Google Cloud Next ‘25, we asked you to imagine a new future for AI. At Next ‘26, the question before you is how do you move AI into production across your entire enterprise? According to

DDF2Pol: A Dual-Domain Feature Fusion Network for PolSAR Image Classification

arXiv:2604.18853v1 Announce Type: new Abstract: This paper presents DDF2Pol, a lightweight dual-domain convolutional neural network for PolSAR image classification. The proposed architecture integrate

Agents

Debating the Unspoken: Role-Anchored Multi-Agent Reasoning for Half-Truth Detection

arXiv:2604.19005v1 Announce Type: new Abstract: Half-truths, claims that are factually correct yet misleading due to omitted context, remain a blind spot for fact verification systems focused on expli

agentsarxiv-cs-cl

Debiased neural operators for estimating functionals

arXiv:2604.19296v1 Announce Type: new Abstract: Neural operators are widely used to approximate solution maps of complex physical systems. In many applications, however, the goal is not to recover the

safetyarxiv-cs-lg

Debug2Fix: Can Interactive Debugging Help Coding Agents Fix More Bugs?

arXiv:2602.18571v2 Announce Type: replace-cross Abstract: While significant progress has been made in automating various aspects of software development through coding agents, there is still significa

Decompose, Structure, and Repair: A Neuro-Symbolic Framework for Autoformalization via Operator Trees

arXiv:2604.19000v1 Announce Type: cross Abstract: Statement autoformalization acts as a critical bridge between human mathematics and formal mathematics by translating natural language problems into f

Decomposed Trust: Privacy, Adversarial Robustness, Ethics, and Fairness in Low-Rank LLMs

arXiv:2511.22099v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have driven major advances across domains, yet their massive size hinders deployment in resource-constrained sett

Decoupled DiLoCo: A new frontier for resilient, distributed AI training

model-releasesgoogle-deepmind

Decoupled DiLoCo is a distributed architecture that enables training of large language models across distant data centers using lower bandwidth and improved hardware resilience by dividing training in

Deep Image Prior for photoacoustic tomography can mitigate limited-view artifacts

arXiv:2604.19176v1 Announce Type: cross Abstract: We study the deep image prior (DIP) framework applied to photoacoustic tomography (PAT) as an unsupervised reconstruction approach to mitigate limited

researcharxiv-cs-lg

Deep sprite-based image models: An analysis

arXiv:2604.19480v1 Announce Type: new Abstract: While foundation models drive steady progress in image segmentation and diffusion algorithms compose always more realistic images, the seemingly simple

Deep Supervised Contrastive Learning of Pitch Contours for Robust Pitch Accent Classification in Seoul Korean

model-releasesarxiv-cs-cl

arXiv:2604.19477v1 Announce Type: cross Abstract: The intonational structure of Seoul Korean has been defined with discrete tonal categories within the Autosegmental-Metrical model of intonational pho

DeltaSeg: Tiered Attention and Deep Delta Learning for Multi-Class Structural Defect Segmentation

arXiv:2604.18745v1 Announce Type: new Abstract: Automated segmentation of structural defects from visual inspection imagery remains challenging due to the diversity of damage types, extreme class imba

researcharxiv-cs-cv

Denoising, Fast and Slow: Difficulty-Aware Adaptive Sampling for Image Generation

arXiv:2604.19141v1 Announce Type: new Abstract: Diffusion- and flow-based models usually allocate compute uniformly across space, updating all patches with the same timestep and number of function eva

Design Rules for Extreme-Edge Scientific Computing on AI Engines

arXiv:2604.19106v1 Announce Type: cross Abstract: Extreme-edge scientific applications use machine learning models to analyze sensor data and make real-time decisions. Their stringent latency and thro

researcharxiv-cs-ai

Detecting Data Contamination in Large Language Models

arXiv:2604.19561v1 Announce Type: new Abstract: Large Language Models (LLMs) utilize large amounts of data for their training, some of which may come from copyrighted sources. Membership Inference Att

researcharxiv-cs-ai

Detecting Hallucinations in SpeechLLMs at Inference Time Using Attention Maps

arXiv:2604.19565v1 Announce Type: cross Abstract: Hallucinations in Speech Large Language Models (SpeechLLMs) pose significant risks, yet existing detection methods typically rely on gold-standard out

Detection of T-shirt Presentation Attacks in Face Recognition Systems

arXiv:2604.19365v1 Announce Type: new Abstract: Face recognition systems are often used for biometric authentication. Nevertheless, it is known that without any protective measures, face recognition s

Local Ai

Detoxification for LLM: From Dataset Itself

arXiv:2604.19124v1 Announce Type: new Abstract: Existing detoxification methods for large language models mainly focus on post-training stage or inference time, while few tackle the source of toxicity

local-aiarxiv-cs-cl

Developing a Robotic Surgery Training System for Wide Accessibility and Research

arXiv:2505.20562v2 Announce Type: replace Abstract: Robotic surgery represents a major breakthrough in medical interventions, which has revolutionized surgical procedures. However, the high cost and l

safetyarxiv-cs-ro

Diagnosable ColBERT: Debugging Late-Interaction Retrieval Models Using a Learned Latent Space as Reference

arXiv:2604.19566v1 Announce Type: cross Abstract: Reliable biomedical and clinical retrieval requires more than strong ranking performance: it requires a practical way to find systematic model failure

safetyarxiv-cs-cl

Diamond Maps: Efficient Reward Alignment via Stochastic Flow Maps

arXiv:2602.05993v2 Announce Type: replace-cross Abstract: Flow and diffusion models produce high-quality samples, but adapting them to user preferences or constraints post-training remains costly and

Diff-SBSR: Learning Multimodal Feature-Enhanced Diffusion Models for Zero-Shot Sketch-Based 3D Shape Retrieval

arXiv:2604.19135v1 Announce Type: new Abstract: This paper presents the first exploration of text-to-image diffusion models for zero-shot sketch-based 3D shape retrieval (ZS-SBSR). Existing sketch-bas

Differentiable Satellite Constellation Configuration via Relaxed Coverage and Revisit Objectives

arXiv:2604.19062v1 Announce Type: new Abstract: Satellite constellation design requires optimizing orbital parameters across multiple satellites to maximize mission specific metrics. For many types of

researcharxiv-cs-ro

DINO Eats CLIP: Adapting Beyond Knowns for Open-set 3D Object Retrieval

arXiv:2604.19432v1 Announce Type: new Abstract: Vision foundation models have shown great promise for open-set 3D object retrieval (3DOR) through efficient adaptation to multi-view images. Leveraging

Discovering a Shared Logical Subspace: Steering LLM Logical Reasoning via Alignment of Natural-Language and Symbolic Views

arXiv:2604.19716v1 Announce Type: new Abstract: Large Language Models (LLMs) still struggle with multi-step logical reasoning. Existing approaches either purely refine the reasoning chain in natural l

safetyarxiv-cs-cl

Discrete Tilt Matching

arXiv:2604.18739v1 Announce Type: new Abstract: Masked diffusion large language models (dLLMs) are a promising alternative to autoregressive generation. While reinforcement learning (RL) methods have

researcharxiv-cs-lg

Tutorials

Disentangling Damage from Operational Variability: A Label-Free Self-Supervised Representation Learning Framework for Output-Only Structural Damage Identification

arXiv:2604.19658v1 Announce Type: new Abstract: Damage identification is a core task in structural health monitoring. In practice, however, its reliability is often compromised by confounding non-dama

tutorialsarxiv-cs-lg

Disparities In Negation Understanding Across Languages In Vision-Language Models

model-releasesarxiv-cs-cl

arXiv:2604.18942v1 Announce Type: new Abstract: Vision-language models (VLMs) exhibit affirmation bias: a systematic tendency to select positive captions ('X is present') even when the correct descrip