All entries

Deciphering Shortcut Learning from an Evolutionary Game Theory Perspective

arXiv:2605.02658v2 Announce Type: new Abstract: Shortcut learning causes deep learning models to rely on non-essential features within the data. However, its formation in deep neural network training

safetyarxiv-cs-ai

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Delay, Plateau, or Collapse: Evaluating the Impact of Systematic Verification Error on RLVR

DGX agent

arXiv:2605.02909v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become a powerful approach for improving the reasoning capabilities of large language models (

applicationsarxiv-cs-lg

6 May 2026

Dell and Intel advance secure AI factories with trust-first infrastructure

The general-purpose data center computing environment is taking on a new role as the AI factory, transforming data into intelligence at industrial scale. The rise of the AI factory has captured the at

applicationssiliconangle

Tools

Demand has outpaced our capacity recently, and we know it's been frustrating. We take your feedback to heart, thank you for sticking with us…

Boris Cherny acknowledges that recent demand for his product or service has exceeded available capacity, resulting in user frustration. He expresses appreciation for customer feedback and loyalty duri

toolsboris-cherny--x

DeRelayL: Sustainable Decentralized Relay Learning

arXiv:2605.02935v1 Announce Type: new Abstract: In the era of big data, large-scale machine learning models have revolutionized various fields, driving significant advancements. However, large-scale m

Descent-Guided Policy Gradient for Scalable Cooperative Multi-Agent Learning

arXiv:2602.20078v3 Announce Type: replace-cross Abstract: Scaling cooperative multi-agent reinforcement learning (MARL) is fundamentally limited by cross-agent noise. When agents share a common reward

safetyarxiv-cs-lg

deSEO: Physics-Aware Dataset Creation for High-Resolution Satellite Image Shadow Removal

arXiv:2605.03610v1 Announce Type: new Abstract: Shadows cast by terrain and tall structures remain a major obstacle for high-resolution satellite image analysis, degrading classification, detection, a

researcharxiv-cs-cv

Agents

Design-OS: A Specification-Driven Framework for Engineering System Design with a Control-Systems Design Case

arXiv:2603.20151v2 Announce Type: replace-cross Abstract: Engineering system design -- whether mechatronic, control, or embedded -- often proceeds in an ad hoc manner, with requirements left implicit

agentsarxiv-cs-ai

Detecting Stealth Sycophancy in Mental-Health Dialogue with Dynamic Emotional Signature Graphs

model-releasesarxiv-cs-cl

arXiv:2605.03472v1 Announce Type: new Abstract: As conversational AI therapists are increasingly used in psychological support settings, reliable offline evaluation of therapeutic response quality rem

Agents

Devin Review is now live in Windsurf, alongside Quick Review: a fast local bug detector powered by SWE-check. Fast and comprehensive code re…

Devin Review is now live in Windsurf, alongside Quick Review: a fast local bug detector powered by SWE-check. Fast and comprehensive code review and fixes are now available right in your IDE. Devin Re

agentscognition-ai--x

Tools

Devin Review runs hundreds of thousands of times per day. Starting today, run it directly from Windsurf: a smart reviewer in your editor, wi…

Devin Review runs hundreds of thousands of times per day. Starting today, run it directly from Windsurf: a smart reviewer in your editor, with bugs and context in one place. Also launching Quick Revie

toolswindsurf--x

Industry

Devs are spending only 16% of their time coding. Atlassian is engineering AI to reclaim the rest

The rise of AI coding agents has commoditized code generation, exposing a deeper challenge for software teams: the non-coding friction that consumes the vast majority of a developer’s day. Fixing that

industrysiliconangle

DGPO: Distribution Guided Policy Optimization for Fine Grained Credit Assignment

arXiv:2605.03327v1 Announce Type: new Abstract: Reinforcement learning is crucial for aligning large language models to perform complex reasoning tasks. However, current algorithms such as Group Relat

safetyarxiv-cs-lg

DiagramNet: An End-to-End Recognition Framework and Dataset for Non-Standard System-Level Diagrams

model-releasesarxiv-cs-ai

arXiv:2605.01338v1 Announce Type: new Abstract: System-level diagrams encode the architectural blueprint of chip design, specifying module functions, dataflows, and interface protocols. However, non-s

Diffusion Masked Pretraining for Dynamic Point Cloud

arXiv:2605.03639v1 Announce Type: new Abstract: Dynamic point cloud pretraining is still dominated by masked reconstruction objectives. However, these objectives inherit two key limitations. Existing

researcharxiv-cs-cv

DINO Soars: DINOv3 for Open-Vocabulary Semantic Segmentation of Remote Sensing Imagery

model-releasesarxiv-cs-cv

arXiv:2605.03175v1 Announce Type: new Abstract: The remote sensing (RS) domain suffers from a lack of densely labeled datasets, which are costly to obtain. Thus, models that can segment RS imagery wel

Tutorials

Direct Simultaneous Translation Activation for Large Audio-Language Models

arXiv:2509.15692v2 Announce Type: replace-cross Abstract: Simultaneous speech-to-text translation (Simul-S2TT) aims to translate speech into target text in real time, outputting translations while rec

tutorialsarxiv-cs-cl

Discover Fast Power Allocation Solution for Multi-Target Tracking via AlphaEvolve Evolution

arXiv:2605.01794v1 Announce Type: cross Abstract: Efficient radar resource allocation is a fundamental yet computationally challenging problem, as optimal solutions typically require iterative optimiz

Discovering Reinforcement Learning Interfaces with Large Language Models

arXiv:2605.03408v1 Announce Type: new Abstract: Reinforcement learning systems rely on environment interfaces that specify observations and reward functions, yet constructing these interfaces for new

safetyarxiv-cs-lg

Disease Is a Spectral Perturbation

arXiv:2605.02949v1 Announce Type: new Abstract: We propose a novel method of understanding disease transformation from a healthy baseline with biomarker-level explainability. By modeling the biomarker

Disentangling Intent from Role: Adversarial Self-Play for Persona-Invariant Safety Alignment

arXiv:2605.01899v1 Announce Type: new Abstract: The growing capabilities of large language models (LLMs) have driven their widespread deployment across diverse domains, even in potentially high-risk s

safetyarxiv-cs-ai

Disentangling Shared and Task-Specific Representations from Multi-Modal Clinical Data

arXiv:2605.03570v1 Announce Type: new Abstract: Real-world clinical data is inherently multimodal, providing complementary evidence that mirrors the practical necessity of jointly assessing multiple r

Distilling Long-CoT Reasoning through Collaborative Step-wise Multi-Teacher Decoding

arXiv:2605.02290v1 Announce Type: new Abstract: Distilling large reasoning models is essential for making Long-CoT reasoning practical, as full-scale inference remains computationally prohibitive. Exi

Distributed Deep Variational Approach for Privacy-preserving Data Release

arXiv:2605.03069v1 Announce Type: cross Abstract: Federated learning (FL) lets distributed nodes train a shared model without exchanging their raw data, but in privacy-sensitive deployments medical se

Distributed Learning with Adversarial Gradient Perturbations

arXiv:2605.03313v1 Announce Type: new Abstract: Privacy concerns in distributed learning often lead clients to return intentionally altered gradient information. We consider the problem of learning co

Distribution-Free Pretraining of Classification Losses via Evolutionary Dynamics

arXiv:2605.03722v1 Announce Type: new Abstract: We propose Evolutionary Dynamic Loss (EDL), a framework that learns a transferable classification loss in the probability space using unlimited syntheti

DMGD: Train-Free Dataset Distillation with Semantic-Distribution Matching in Diffusion Models

arXiv:2605.03877v1 Announce Type: new Abstract: Dataset distillation enables efficient training by distilling the information of large-scale datasets into significantly smaller synthetic datasets. Dif

safetyarxiv-cs-cv

Do LLMs have core beliefs?

arXiv:2605.03255v1 Announce Type: new Abstract: The rise of Large Language Models (LLMs) has sparked debate about whether these systems exhibit human-level cognition. In this debate, little attention

Do Not Waste Your Rollouts: Recycling Search Experience for Efficient Test-Time Scaling

arXiv:2601.21684v2 Announce Type: replace Abstract: Test-Time Scaling enhances the reasoning capabilities of Large Language Models by allocating additional inference compute to broaden the exploration

researcharxiv-cs-cl

DocSync: Agentic Documentation Maintenance via Critic-Guided Reflexion

model-releasesarxiv-cs-ai

arXiv:2605.02163v1 Announce Type: cross Abstract: Software documentation frequently drifts from executable logic as codebases evolve, creating technical debt that degrades maintainability and causes d

Donor-Aware scRNA-seq Benchmarks for IBD Classification

arXiv:2605.03281v1 Announce Type: cross Abstract: Donor-level disease classification from single-cell RNA sequencing (scRNA-seq) requires strict donor-aware cross-validation: naive pipelines that spli

Industry

DoorDash reports Q1 revenue up 33% YoY to 4.04B, vs. 4.14B est., and forecasts Q2 marketplace gross order value above estimates; DASH jumps 11%+ after hours (Koyena Das/Reuters)

Koyena Das / Reuters: DoorDash reports Q1 revenue up 33% YoY to 4.04B, vs. 4.14B est., and forecasts Q2 marketplace gross order value above estimates; DASH jumps 11%+ after hours — DoorDash (DASH.O) o

industrytechmeme

Double Rectified Linear Unit-based Modular Semantics for Quantitative Bipolar Argumentation Framework

arXiv:2605.02551v1 Announce Type: new Abstract: Quantitative Bipolar Argumentation Frameworks (QBAFs) provide an alternative approach to computing argument acceptability in Bipolar Argumentation Frame

@Dr_Gingerballs At least actual ponzi schemes don't light their cash on fire... they just cant meet redemptions at the level of their inflat…

@Dr_Gingerballs At least actual ponzi schemes don't light their cash on fire... they just cant meet redemptions at the level of their inflated fake earnings. After all of the hyperscalers burn every l

safetygary-marcus--x

Driving Style Recognition Like an Expert Using Semantic Privileged Information from Large Language Models

arXiv:2508.13881v2 Announce Type: replace Abstract: Existing driving style recognition systems largely depend on low-level sensor-derived features for training, neglecting the rich semantic reasoning

applicationsarxiv-cs-ro

Agents

Dual-Foundation Models for Unsupervised Domain Adaptation

arXiv:2605.03365v1 Announce Type: new Abstract: Semantic segmentation provides pixel-level scene understanding essential for autonomous driving and fine-grained perception tasks. However, training seg

agentsarxiv-cs-cv

Dynamic Distillation and Gradient Consistency for Robust Long-Tailed Incremental Learning

arXiv:2605.03364v1 Announce Type: new Abstract: The task of Long-tailed Class Incremental Learning (LT-CIL) addresses the sequential learning of new classes from datasets with imbalanced class distrib

researcharxiv-cs-cv

Dynamic Vine Copulas: Detecting and Quantifying Time-Varying Higher-Order Interactions

arXiv:2605.03061v1 Announce Type: cross Abstract: Time varying dependence is often modeled through dynamic correlations or Gaussian graphical models, yet many multivariate systems change through tail

DynaTab: Dynamic Feature Ordering as Neural Rewiring for High-Dimensional Tabular Data

arXiv:2605.03430v1 Announce Type: new Abstract: High-dimensional tabular data lacks a natural feature order, limiting the applicability of permutation-sensitive deep learning models. We propose DynaTa

applicationsarxiv-cs-lg

E-MIA: Exam-Style Black-Box Membership Inference Attacks against RAG Systems

arXiv:2605.00955v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) equips large language models (LLMs) with external evidence by retrieving documents at inference time, but it also

Ecologically-Constrained Task Arithmetic for Multi-Taxa Bioacoustic Classifiers Without Shared Data

arXiv:2605.03914v1 Announce Type: cross Abstract: Training data for bioacoustics is scattered across taxa, regions, and institutions. Centralizing it all is often infeasible. We show that independentl

Effect-Transparent Governance for AI Workflow Architectures: Semantic Preservation, Expressive Minimality, and Decidability Boundaries

arXiv:2605.01030v2 Announce Type: new Abstract: We present a machine-checked formalization of structurally governed AI workflow architectures and prove that effect-level governance can be imposed with

Effective Performance Measurement: Challenges and Opportunities in KPI Extraction from Earnings Calls

model-releasesarxiv-cs-cl

arXiv:2605.03147v1 Announce Type: new Abstract: Earnings calls are a key source of financial information about public companies. However, extracting information from these calls is difficult. Unlike t

Efficient Deconvolution in Populational Inverse Problems

arXiv:2505.19841v2 Announce Type: replace-cross Abstract: This work is focussed on the inversion task of inferring the distribution over parameters of interest leading to multiple sets of observations

Efficient Temporal Datalog Materialisation for Composite Event Recognition

arXiv:2605.02488v1 Announce Type: new Abstract: Several applications demand the timely detection of critical situations, such as threats to safety and transparency, over high-velocity streams of symbo

safetyarxiv-cs-ai

EFGPP: Exploratory framework for genotype-phenotype prediction

arXiv:2605.02954v1 Announce Type: cross Abstract: Predicting complex human traits from genetic data is challenging because different genetic, clinical, and molecular data sources often contain differe

ELAS: Efficient Pre-Training of Low-Rank Large Language Models via 2:4 Activation Sparsity

arXiv:2605.03667v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable capabilities, but their immense computational demands during training remain a critical bottleneck

Industry

ElevenLabs adds high-profile investors after annualized revenue tops $500M

ElevenLabs Inc., a developer of artificial intelligence models optimized for audio processing, today announced that several dozen investors have joined its latest funding round. The startup first clos

industrysiliconangle