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Debiasing Message Passing to Mitigate Popularity Bias in GNN-based Collaborative Filtering

arXiv:2605.11145v1 Announce Type: cross Abstract: Collaborative filtering (CF) models based on graph neural networks (GNNs) achieve strong performance in recommender systems by propagating user-item s

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Decomposing Evolutionary Mixture-of-LoRA Architectures: The Routing Lever, the Lifecycle Penalty, and a Substrate-Conditional Boundary

DGX agent

arXiv:2605.11153v1 Announce Type: new Abstract: We decompose an evolutionary mixture-of-LoRA system on a from-scratch ~150M-parameter widened-D substrate (D=1536, V=32000; D/V approx 0.048; the 'widen

model-releasesarxiv-cs-cl

13 May 2026

Decomposing the Generalization Gap in PROTAC Activity Prediction: Variance Attribution and the Inter-Laboratory Ceiling

arXiv:2605.11764v1 Announce Type: new Abstract: Machine-learning predictors of biochemical activity often exhibit large random-split-to-leave-one-target-out generalisation gaps that have been document

DeconDTN-Toolkit: A Library for Evaluation and Enhancement of Robustness to Provenance Shift

arXiv:2605.11237v1 Announce Type: new Abstract: Despite the burgeoning body of work on distribution shifts, provenance shift-where the relationship between data source and label changes at deployment-

Deep Learning for Protein Complex Prediction and Design

arXiv:2605.11189v1 Announce Type: new Abstract: Accurately modeling and designing protein complex structures is a central problem in computational structural biology, with broad implications for under

Deep Minds and Shallow Probes

arXiv:2605.11448v1 Announce Type: new Abstract: Neural representations are not unique objects. Even when two systems realize the same downstream computation, their hidden coordinates may differ by rep

applicationsarxiv-cs-lg

Deep Probabilistic Unfolding for Quantized Compressive Sensing

arXiv:2605.11475v1 Announce Type: new Abstract: We propose a deep probabilistic unfolding model to address the classical quantized compressive sensing problem that leverages an unfolding framework to

Deep Reasoning in General Purpose Agents via Structured Meta-Cognition

arXiv:2605.11388v1 Announce Type: new Abstract: Humans intuitively solve complex problems by flexibly shifting among reasoning modes: they plan, execute, revise intermediate goals, resolve ambiguity t

agentsarxiv-cs-cl

Delay-Empowered Causal Hierarchical Reinforcement Learning

arXiv:2605.12261v1 Announce Type: new Abstract: Many real-world tasks involve delayed effects, where the outcomes of actions emerge after varying time lags. Existing delay-aware reinforcement learning

applicationsarxiv-cs-lg

Delightful Gradients Accelerate Corner Escape

model-releasesarxiv-cs-lg

arXiv:2605.11908v1 Announce Type: new Abstract: Softmax policy gradient converges at O(1/t), but its transient behavior near sub-optimal corners of the simplex can be exponentially slow. The bottlenec

Local Ai

DemaFormer: Damped Exponential Moving Average Transformer with Energy-Based Modeling for Temporal Language Grounding

arXiv:2312.02549v2 Announce Type: replace-cross Abstract: Temporal Language Grounding seeks to localize video moments that semantically correspond to a natural language query. Recent advances employ t

local-aiarxiv-cs-cl

Demystifying When Pruning Works via Representation Hierarchies

arXiv:2603.24652v3 Announce Type: replace Abstract: Network pruning, which removes less important parameters or architectures, is often expected to improve efficiency while preserving performance. How

Tutorials

DenseTRF: Texture-Aware Unsupervised Representation Adaptation for Surgical Scene Dense Prediction

arXiv:2605.11265v1 Announce Type: new Abstract: Dense prediction tasks in surgical computer vision, such as segmentation and surgical zone prediction, can provide valuable guidance for laparoscopic an

tutorialsarxiv-cs-cv

Deploying Self-Supervised Learning for Real Seismic Data Denoising

model-releasesarxiv-cs-cv

arXiv:2605.11109v1 Announce Type: cross Abstract: Self-supervised learning (SSL) has emerged as a promising approach to seismic data denoising as it does not require clean reference data. In this work

Design Your Ad: Personalized Advertising Image and Text Generation with Unified Autoregressive Models

arXiv:2605.12138v1 Announce Type: cross Abstract: Generating realistic and user-preferred advertisements is a key challenge in e-commerce. Existing approaches utilize multiple independent models drive

Detecting Data Contamination in LLMs via In-Context Learning

model-releasesarxiv-cs-cl

arXiv:2510.27055v2 Announce Type: replace Abstract: We present Contamination Detection via Context (CoDeC), a practical and accurate method to detect and quantify training data contamination in large

Detecting In-Person Conversations in Noisy Real-World Environments with Smartwatch Audio and Motion Sensing

arXiv:2507.12002v2 Announce Type: replace Abstract: Social interactions play a crucial role in shaping human behavior, relationships, and societies. It encompasses various forms of communication, such

applicationsarxiv-cs-lg

Detecting overfitting in Neural Networks during long-horizon grokking using Random Matrix Theory

arXiv:2605.12394v1 Announce Type: new Abstract: Training Neural Networks (NNs) without overfitting is difficult; detecting that overfitting is difficult as well. We present a novel Random Matrix Theor

Devin can now build and run Android apps. We added Android Virtual Device (AVD) support for Devin’s machine, which means Devin can now auton…

Cognition AI has expanded Devin's capabilities to include Android app development and testing by integrating Android Virtual Device (AVD) support into Devin's machine environment. This enhancement ena

agentscognition-ai--x

DexTwist: Dexterous Hand Retargeting for Twist Motion via Mixed Reality-based Teleoperation

arXiv:2605.12182v1 Announce Type: new Abstract: Dexterous teleoperation via Mixed Reality (MR)-based interfaces offers a scalable paradigm for transferring human manipulation skills to dexterous robot

applicationsarxiv-cs-ro

Diabetic Retinopathy Classification using Downscaling Algorithms and Deep Learning

arXiv:2605.11430v1 Announce Type: new Abstract: Diabetic Retinopathy (DR) is an art and science of recording and classifying the retinal images of a diabetic patient. DR classification deals with clas

DiFaReli++: Diffusion Face Relighting with Consistent Cast Shadows

model-releasesarxiv-cs-cv

arXiv:2304.09479v5 Announce Type: replace Abstract: We introduce a novel approach to single-view face relighting in the wild, addressing challenges such as global illumination and cast shadows. A comm

Differentially Private Synthetic Text Generation for Retrieval-Augmented Generation (RAG)

arXiv:2510.06719v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by grounding them in external knowledge. However, its application i

DiffScore: Text Evaluation Beyond Autoregressive Likelihood

model-releasesarxiv-cs-cl

arXiv:2605.11601v1 Announce Type: new Abstract: Autoregressive language models are widely used for text evaluation, however, their left-to-right factorization introduces positional bias, i.e., early t

DiffSegLung: Diffusion Radiomic Distillation for Unsupervised Lung Pathology Segmentation

arXiv:2605.11758v1 Announce Type: cross Abstract: Unsupervised segmentation of pulmonary pathologies in CT remains an open challenge due to the absence of annotated multi pathology cohorts and the fai

Diffusion-State Policy Optimization for Masked Diffusion Language Models

arXiv:2602.06462v3 Announce Type: replace Abstract: Masked diffusion language models generate text through iterative masked-token filling, but terminal-only rewards on final completions provide coarse

safetyarxiv-cs-cl

DigiCert tackles AI’s trust crisis with identity, automation and cryptographic governance

Enterprise AI is moving faster than the digital trust systems built to govern it. As autonomous agents, synthetic content and machine identities spread across critical systems, organizations face a ne

agentssiliconangle

DIPSER: A Dataset for In-Person Student Engagement Recognition in the Wild

arXiv:2502.20209v3 Announce Type: replace Abstract: In this paper, a novel dataset is introduced, designed to assess student attention within in-person classroom settings. This dataset encompasses RGB

Dirichlet process mixtures of block g priors for model selection and prediction in linear models

arXiv:2411.00471v3 Announce Type: replace-cross Abstract: This paper introduces Dirichlet process mixtures of block g priors for model selection and prediction in linear models. These priors are exten

DisagMoE: Computation-Communication overlapped MoE Training via Disaggregated AF-Pipe Parallelism

model-releasesarxiv-cs-lg

arXiv:2605.11005v1 Announce Type: new Abstract: Mixture-of-experts (MoE) architectures enable trillion-parameter LLMs with sparsely activated experts. Expert parallelism (EP) is a widely adopted MoE t

Discrete Flow Matching for Offline-to-Online Reinforcement Learning

arXiv:2605.12379v1 Announce Type: new Abstract: Many reinforcement learning (RL) tasks have discrete action spaces, but most generative policy methods based on diffusion and flow matching are designed

Disentangled Sparse Representations for Concept-Separated Diffusion Unlearning

arXiv:2605.12122v1 Announce Type: cross Abstract: Unlearning specific concepts in text-to-image diffusion models has become increasingly important for preventing undesirable content generation. Among

Dissecting Discrete Soft Actor-Critic: Limitations and Principled Alternatives

arXiv:2509.09838v2 Announce Type: replace Abstract: While Soft Actor-Critic (SAC) is highly effective in continuous control, its discrete counterpart (DSAC) performs poorly on challenging discrete-act

Distributed Pose Graph Optimization via Continuous Riemannian Dynamics

model-releasesarxiv-cs-ro

arXiv:2605.11210v1 Announce Type: new Abstract: We present a framework for distributed Pose Graph Optimization (PGO) by formulating the problem as a second-order continuous-time dynamical system evolv

Local Ai

Distributed Quantum Gaussian Processes for Multi-Agent Systems

arXiv:2602.15006v2 Announce Type: replace-cross Abstract: Gaussian Processes (GPs) are a powerful tool for probabilistic modeling, but their performance is often constrained in complex, large-scale re

local-aiarxiv-cs-lg

DiVeQ: Differentiable Vector Quantization Using the Reparameterization Trick

arXiv:2509.26469v3 Announce Type: replace Abstract: Vector quantization is common in deep models, yet its hard assignments block gradients and hinder end-to-end training. We propose DiVeQ, which treat

Tutorials

Do AI models really learn physics, or just learn what physics looks like? Flatiron's @_helenqu, with @PolymathicAI & CDS researchers includi…

Do AI models really learn physics, or just learn what physics looks like? Flatiron's @_helenqu, with @PolymathicAI & CDS researchers including @ylecun, finds that models predicting in latent space rec

tutorialsyann-lecun--x

Do Enterprise Systems Need Learned World Models? The Importance of Context to Infer Dynamics

model-releasesarxiv-cs-cl

arXiv:2605.12178v1 Announce Type: cross Abstract: World models enable agents to anticipate the effects of their actions by internalizing environment dynamics. In enterprise systems, however, these dyn

Do Language Models Encode Knowledge of Linguistic Constraint Violations?

arXiv:2605.12055v1 Announce Type: new Abstract: Large Language Models (LLMs) achieve strong linguistic performance, yet their internal mechanisms for producing these predictions remain unclear. We inv

Do multi-agent systems make LLM reasoning better? Most AI devs assume that it should. But this new paper shows that this is often not the ca…

Do multi-agent systems make LLM reasoning better? Most AI devs assume that it should. But this new paper shows that this is often not the case. It ran 22,500 deterministic trajectories across GAIA, SW

safetydair-ai--x

Does Head Pose Correction Improve Biometric Facial Recognition?

arXiv:2512.03199v2 Announce Type: replace Abstract: Biometric facial recognition models often demonstrate significant decreases in accuracy when processing real-world images, often characterized by po

applicationsarxiv-cs-cv

Industry

Don’t be part of a medium. Create a new one. Don’t join a festival. Invent your own. Don’t just become the camera of the next generation. Be…

industrycristobal-valenzuela--x

Don’t be part of a medium. Create a new one. Don’t join a festival. Invent your own. Don’t just become the camera of the next generation. Become its grammar. Don’t wait for a category. Become the reas

Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation

arXiv:2510.04265v4 Announce Type: replace-cross Abstract: Pass@k is widely used to report the reasoning performance of LLMs, but it often produces unstable and potentially misleading rankings, especia

DORA: Dynamic Online Reinforcement Agent for Token Merging in Vision Transformers

arXiv:2605.11683v1 Announce Type: new Abstract: Vision Transformers (ViTs) incur significant computational overhead due to the quadratic complexity of self-attention relative to the token sequence len

agentsarxiv-cs-cv

DP-{lambda}CGD: Efficient Noise Correlation for Differentially Private Model Training

arXiv:2601.22334v2 Announce Type: replace Abstract: Differentially private stochastic gradient descent (DP-SGD) is the gold standard for training machine learning models with formal differential priva

DreamAvoid: Critical-Phase Test-Time Dreaming to Avoid Failures in VLA Policies

arXiv:2605.11750v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are often brittle in fine-grained manipulation, where minor action errors during the critical phases can rapidly e

agentsarxiv-cs-cl

DreamPolicy: A Unified World-model Policy for Scalable Humanoid Locomotion

arXiv:2505.18780v3 Announce Type: replace-cross Abstract: Achieving versatile humanoid locomotion with a single policy presents a critical scalability challenge. Prevailing methods often rely on disti

DriftXpress: Faster Drifting Models via Projected RKHS Fields

arXiv:2605.12183v1 Announce Type: new Abstract: Drifting Models have emerged as a new paradigm for one-step generative modeling, achieving strong image quality without iterative inference. The premise