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Don't Ignore the Tail: Decoupling top-K Probabilities for Efficient Language Model Distillation

arXiv:2602.20816v3 Announce Type: replace Abstract: The core learning signal used in language model distillation is the standard Kullback-Leibler (KL) divergence between the student and teacher distri

researcharxiv-cs-cl

Hardware

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Don't Learn the Shape: Forecasting Periodic Time Series by Rank-1 Decomposition

DGX agent

arXiv:2605.07222v1 Announce Type: new Abstract: How few parameters do we really need to forecast a periodic time series? An hourly electricity series, reshaped as a 24-row matrix with one column per d

hardwarearxiv-cs-lg

11 May 2026

Don't Retrain, Align: Adapting Autoregressive LMs to Diffusion LMs via Representation Alignment

arXiv:2605.06885v1 Announce Type: cross Abstract: Diffusion language models (DLMs) have recently demonstrated capabilities that complement standard autoregressive (AR) models, particularly in non-sequ

Hardware

Dooly: Configuration-Agnostic, Redundancy-Aware Profiling for LLM Inference Simulation

arXiv:2605.07985v1 Announce Type: cross Abstract: Selecting the optimal LLM inference configuration requires evaluation across hardware, serving engines, attention backends, and model architectures, s

hardwarearxiv-cs-ai

DPG-CD: Depth-Prior-Guided Cross-Modal Joint 2D-3D Change Detection

arXiv:2605.07151v1 Announce Type: cross Abstract: Urban spatial evolution is manifested not only through horizontal expansion but also through vertical structural changes. Consequently, jointly captur

Dr-BA: Separable Optimization for Direct Radar Bundle Adjustment & Localization

arXiv:2605.07041v1 Announce Type: cross Abstract: This paper introduces Dr-BA, a first-of-its-kind radar bundle adjustment (BA) framework that operates directly on 2D spinning radar intensity images.

safetyarxiv-cs-cv

Dr. Post-Training: A Data Regularization Perspective on LLM Post-Training

arXiv:2605.07063v1 Announce Type: cross Abstract: Data selection methods address a critical challenge in LLM post-training: effectively leveraging scarce, high-fidelity target data alongside abundant

Drawing Lines in Psychological Space: What K-means Clustering Reveals in Simulated and Real Psychometric Data

arXiv:2605.06989v1 Announce Type: cross Abstract: K-means clustering is widely used in psychological and psychometric research to identify profiles, subgroups, and potential typologies, yet its classi

DReS: Dual Reconstruction Smoothing for Functional Regularization

arXiv:2510.00253v2 Announce Type: replace Abstract: Smoothness is a key inductive bias in machine learning and is closely related to generalization. Existing smoothness-inducing methods typically rely

safetyarxiv-cs-lg

Drifting Field Policy: A One-Step Generative Policy via Wasserstein Gradient Flow

arXiv:2605.07727v1 Announce Type: cross Abstract: We propose Drifting Field Policy (DFP), a non-ODE one-step generative policy built on the drifting model paradigm. We frame the policy update as a rev

DRIP-R: A Benchmark for Decision-Making and Reasoning Under Real-World Policy Ambiguity in the Retail Domain

arXiv:2605.07699v1 Announce Type: cross Abstract: LLM-based agents are increasingly deployed for routine but consequential tasks in real-world domains, where their behavior is governed by inherently a

DT-PBO: an Interpretable Tree-based Surrogate Model for Preferential Bayesian Optimization

arXiv:2512.14263v2 Announce Type: replace-cross Abstract: Preferential Bayesian Optimization (PBO) aims to find a decision-maker's most preferred solution in as few pairwise comparisons as possible. E

Agents

Dual-Agent Co-Training for Health Coaching via Implicit Adversarial Preference Optimization

arXiv:2605.07011v1 Announce Type: new Abstract: Motivational-interviewing-based health coaching is an effective approach for improving mental health and promoting healthy behavior change. However, the

agentsarxiv-cs-lg

Dual-Scale Temporal Fusion Reveals Structured Predictability in Subseasonal-to-Seasonal Temperature Prediction

arXiv:2605.06911v1 Announce Type: new Abstract: Subseasonal-to-seasonal (S2S) temperature forecasts, spanning several weeks to a few months, are critically needed in agriculture practice, energy plann

researcharxiv-cs-lg

Local Ai

DualResolution Residual Architecture with Artifact Suppression for Melanocytic Lesion Segmentation

arXiv:2508.06816v3 Announce Type: replace Abstract: Lesion segmentation, in contrast to natural scene segmentation, requires handling subtle variations in texture and color, frequent imaging artifacts

local-aiarxiv-cs-cv

DVD: Discrete Voxel Diffusion for 3D Generation and Editing

arXiv:2605.07971v1 Announce Type: new Abstract: We introduce Discrete Voxel Diffusion (DVD), a discrete diffusion framework to generate, assess, and edit sparse voxels for SLat (Structured LATent) bas

Agents

Dynamic Mode Decomposition along Depth in Vision Transformers

arXiv:2605.07556v1 Announce Type: new Abstract: Recent work has shown that contiguous vision transformer (ViT) blocks (a) can be replaced by a linear map and (b) organize into recurrent phases of comp

agentsarxiv-cs-cv

Dynamic one-time delivery of critical data by small and sparse UAV swarms: a model problem for MARL scaling studies

arXiv:2512.09682v2 Announce Type: replace-cross Abstract: This work studies the application of Multi-Agent Reinforcement Learning (MARL) to decentralized control of unmanned aerial vehicles to relay a

Dynamic Properties and Motion Reproducibility of a Compact Pneumatically Actuated Humanoid Upper Body for Data-Driven Control

arXiv:2603.14787v2 Announce Type: replace Abstract: Pneumatically-actuated anthropomorphic robots with high degrees of freedom (DOF) offer significant potential for physical human-robot interaction. H

researcharxiv-cs-ro

DynaRetarget: Dynamically-Feasible Retargeting using Sampling-Based Trajectory Optimization

arXiv:2602.06827v2 Announce Type: replace Abstract: In this paper, we introduce DynaRetarget, a complete pipeline for retargeting human motions to humanoid control policies. The core component of Dyna

applicationsarxiv-cs-ro

Echo: KV-Cache-Free Associative Recall with Spectral Koopman Operators

model-releasesarxiv-cs-lg

arXiv:2605.06997v1 Announce Type: new Abstract: Long chain-of-thought reasoning and agentic tool-calling produce traces spanning tens of thousands of tokens, yet Transformer KV caches grow linearly wi

Econometric vs. Causal Structure-Learning for Time-Series Policy Decisions: Evidence from the UK COVID-19 Policies

arXiv:2603.00041v2 Announce Type: replace-cross Abstract: Causal machine learning (ML) recovers graphical structures that inform us about potential cause-and-effect relationships. Most progress has fo

Edge Deep Learning in Computer Vision and Medical Diagnostics: A Comprehensive Survey

arXiv:2605.06714v1 Announce Type: cross Abstract: Edge deep learning, a paradigm change reconciling edge computing and deep learning, facilitates real-time decision making attuned to environmental fac

Edge Detection for Organ Boundaries via Top Down Refinement and SubPixel Upsampling

arXiv:2508.06805v2 Announce Type: replace Abstract: Accurate localization of organ boundaries is critical in medical imaging for segmentation, registration, surgical planning, and radiotherapy. While

Local Ai

EditRefiner: A Human-Aligned Agentic Framework for Image Editing Refinement

arXiv:2605.07457v1 Announce Type: new Abstract: Recent text-guided image editing (TIE) models have made remarkable progress, yet edited images still frequently suffer from fine-grained issues such as

local-aiarxiv-cs-cv

EditTransfer++: Toward Faithful and Efficient Visual-Prompt-Guided Image Editing

arXiv:2605.07455v1 Announce Type: new Abstract: Visual-prompt-guided edit transfer aims to learn image transformations directly from example pairs, offering more precise and controllable editing than

safetyarxiv-cs-cv

Effective and Memory-Efficient Alternatives to ECC for Reliable Large-Scale DNNs

model-releasesarxiv-cs-lg

arXiv:2605.07417v1 Announce Type: cross Abstract: Modern Deep Learning (DL) workloads are increasingly deployed in safety-critical domains, such as automotive systems and hyperscale data centers, wher

Efficient Data Selection for Multimodal Models via Incremental Optimization Utility

arXiv:2605.07488v1 Announce Type: new Abstract: The scaling of Large Multimodal Models (LMMs) is constrained by the quality-quantity trade-off inherent in synthetic data. Previous approaches, such as

Agents

Efficient Verification of Neural Control Barrier Functions with Smooth Nonlinear Activations

arXiv:2605.07757v1 Announce Type: new Abstract: Formal verification of neural control barrier functions (NCBFs) remains challenging, especially for neural networks with nonlinear activations like (anh

agentsarxiv-cs-lg

Tutorials

Effort levels are now available to all users on usage-based Bugbot and can be configured from your Bugbot dashboard. Learn more: http://curs…

Cursor AI has rolled out effort levels functionality to all users on usage-based Bugbot plans, allowing users to configure these settings directly through their Bugbot dashboard. This feature enables

tutorialscursor--x

EGA: Adapting Frozen Encoders for Vector Search with Bounded Out-of-Distribution Degradation

arXiv:2605.05674v2 Announce Type: replace-cross Abstract: Vector search systems built on frozen vision encoders face queries from unseen classes at deployment, yet existing adapter training collapses

EggHand: A Multimodal Foundation Model for Egocentric Hand Pose Forecasting

arXiv:2605.07642v1 Announce Type: new Abstract: Forecasting future 3D hand pose sequences from egocentric video is essential for understanding human intention and enabling embodied applications such a

EgoPro-Bench: Benchmarking Personalized Proactive Interaction in Egocentric Video Streams

arXiv:2605.07299v1 Announce Type: cross Abstract: Existing Multimodal Large Language Models (MLLMs) remain primarily reactive, failing to continuously perceive environments or proactively assist users

Tutorials

EmambaIR: Efficient Visual State Space Model for Event-guided Image Reconstruction

arXiv:2605.08073v1 Announce Type: cross Abstract: Recent event-based image reconstruction methods predominantly rely on Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to process c

tutorialsarxiv-cs-ai

Emergence of Distortions in High-Dimensional Guided Diffusion Models

arXiv:2602.00716v4 Announce Type: replace-cross Abstract: Classifier-free guidance (CFG) is the de facto standard for conditional sampling in diffusion models, yet it often reduces sample diversity. U

applicationsarxiv-cs-lg

Local Ai

Emergent Manifold Separability during Reasoning in Large Language Models

arXiv:2602.20338v2 Announce Type: replace Abstract: Chain-of-Thought (CoT) prompting significantly improves reasoning in Large Language Models, yet the temporal dynamics of the underlying representati

local-aiarxiv-cs-lg

Emergent social transmission of model-based representations without inference

arXiv:2604.05777v2 Announce Type: replace Abstract: How do people acquire rich, flexible knowledge about their environment from others despite limited cognitive capacity? Humans are often thought to r

Emergent Symbolic Structure in Health Foundation Models: Extraction, Alignment, and Cross-Modal Transfer

arXiv:2605.07407v1 Announce Type: new Abstract: Health foundation models (FMs) learn useful representations from wearable sensors, but interpreting what they encode and transferring that knowledge acr

safetyarxiv-cs-lg

Enabling Unsupervised Training of Deep EEG Denoisers With Intelligent Partitioning

arXiv:2605.06724v1 Announce Type: cross Abstract: Denoising wearable electroencephalogram (EEG) is inherently challenging since neural activity is not only subtle but also inseparable from spectrally

End-to-end PDDL Planning with Hardcoded and Dynamic Agents

arXiv:2512.09629v2 Announce Type: replace Abstract: We present an end-to-end framework for planning supported by verifiers. An orchestrator receives a human specification written in natural language a

Enhancing Eye Movement Biometrics for User Authentication via Continuous Gaze Offset Score Fusion

arXiv:2605.06810v1 Announce Type: cross Abstract: Eye movement biometrics (EMB) use subject-specific gaze dynamics for user authentication and identification. Recent deep learning-based EMB systems ac

Enhancing Federated Quadruplet Learning: Stochastic Client Selection and Embedding Stability Analysis

arXiv:2605.07888v1 Announce Type: cross Abstract: Federated Learning (FL) enables decentralised model training across distributed clients without requiring data centralisation. However, the generalisa

Ensemble Distributionally Robust Bayesian Optimisation

arXiv:2605.07565v1 Announce Type: cross Abstract: We study zeroth-order optimisation under context distributional uncertainty, a setting commonly tackled using Bayesian optimisation (BO). A prevailing

Ensemble Learning for Healthcare: A Comparative Analysis of Hybrid Voting and Ensemble Stacking in Obesity Risk Prediction

arXiv:2509.02826v2 Announce Type: replace-cross Abstract: Obesity is a critical global health issue driven by dietary, physiological, and environmental factors, and is strongly associated with chronic

applicationsarxiv-cs-ai

Enterprises are going to actually want a coherent roadmap for the development of tools like Codex and Cowork, so they can plan and train and…

applicationsethan-mollick--x

Enterprises are going to actually want a coherent roadmap for the development of tools like Codex and Cowork, so they can plan and train and scale their use. This conflicts with the Labs’ vision where

Entropy-Regularized Adjoint Matching for Offline Reinforcement Learning

arXiv:2605.06156v2 Announce Type: replace-cross Abstract: Integrating expressive generative policies, such as flow-matching models, into offline reinforcement learning (RL) allows agents to capture co

EnvSimBench: A Benchmark for Evaluating and Improving LLM-Based Environment Simulation

arXiv:2605.07247v1 Announce Type: new Abstract: Scalable AI agents training relies on interactive environments that faithfully simulate the consequences of agent actions. Manually crafted environments

Epistemological standards in SF venture capital are so low that you can write this, and everyone will simply nod along. The actual evidence,…

Epistemological standards in SF venture capital are so low that you can write this, and everyone will simply nod along. The actual evidence, including a recent NBER paper on thousands of firms, finds:

safetygary-marcus--x