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arXiv:2609.38811v1 Announce Type: new Abstract: Metal additive manufacturing parts are inspected by X-ray computed tomography, where labelled data is scarce, the pores and inclusions that matter span

applicationsarxiv-cs-cv

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Local Ai

DDR4/PCIe4 vs DDR5/PCIe5 for LLMs- I benchmarked them for pre-training. What are your thoughts?

DGX agent

Hello everyone. I've recently ran some experiments comparing DDR4/PCIe4 and DDR5/PCIe5 for AI workstations on a pre-training run, and would like to hear yours thoughts. First of all, and as a summary

local-air-localllama

1 Oct 2026

Tutorials

DE-VAE: Revealing Uncertainty in Parametric and Inverse Projections with Variational Autoencoders using Differential Entropy

arXiv:2508.12145v5 Announce Type: replace Abstract: Recently, autoencoders (AEs) have gained interest for creating parametric and invertible projections of multidimensional data. Parametric projection

tutorialsarxiv-cs-lg

Debias It Yourself: Teaching LLMs Cognitive Bias Mitigation Interventions

arXiv:2609.40124v1 Announce Type: new Abstract: Bias has long been studied in social psychology and cognitive science, where decades of research have produced a body of validated interventions that re

safetyarxiv-cs-cl

Decentralized Projection-free Online Upper-Linearizable Optimization with Applications to DR-Submodular Optimization

arXiv:2501.18183v3 Announce Type: replace-cross Abstract: We introduce a novel framework for decentralized projection-free optimization, extending projection-free methods to a broader class of upper-l

Decision-Oriented Recommendation Reranking: An Empirical Study of Jev

model-releasesarxiv-cs-cl

arXiv:2609.40241v1 Announce Type: cross Abstract: Large language models (LLMs) have shown promise for recommendation reranking, but their use introduces an important tradeoff between recommendation qu

Local Ai

Decode-Latency Feedback Prefill: A Model-Free Controller and Its Generalization Limits

arXiv:2609.38386v1 Announce Type: new Abstract: Concurrent autoregressive inference creates a fundamental interference problem: prefilling a newly arrived long prompt can delay tokens for requests tha

local-aiarxiv-cs-ai

DecoMoE: Decoupling Visual Propagation and Expert Computation for Efficient Multimodal MoE Inference

arXiv:2609.38823v1 Announce Type: new Abstract: Multimodal mixture-of-experts (MoE) models combine sparse expert activation with visual-language capabilities, yet their inference remains costly becaus

DeCoPrune: Efficient KV-Cache Pruning for Autoregressive Video Diffusion via Denoising Consistency

arXiv:2609.39096v1 Announce Type: new Abstract: Autoregressive video diffusion supports streaming generation and interactive control, but its KV cache grows with the generated history. Existing compre

Decoupled and Distilled: Task-Adaptive LoRA-Teachers with Ensemble Knowledge Transfer for Few-Shot Class-Incremental Learning

arXiv:2609.39390v1 Announce Type: new Abstract: Few-Shot Class-Incremental Learning (FSCIL) addresses the challenge of learning new classes from very limited samples while retaining knowledge of previ

Tutorials

Decoupling Multi-Contrast Super-Resolution: Self-Supervised Implicit Re-Representation for Unpaired Cross-Modal Synthesis

arXiv:2505.05855v4 Announce Type: replace Abstract: Multi-contrast super-resolution (MCSR) is crucial for enhancing MRI but current deep learning methods are limited. They typically require large, pai

tutorialsarxiv-cs-cv

Tutorials

Decoupling Spherical Reasoning from Dense Prediction for 360 Depth Estimation

arXiv:2609.38856v1 Announce Type: new Abstract: The equirectangular projection (ERP) is widely used for panoramic depth estimation, but its spatially varying distortion makes geometry-consistent featu

tutorialsarxiv-cs-cv

DEdit: Iterative Draft Editing for Speculative Decoding

arXiv:2609.38510v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive LLMs by having a lightweight drafter propose tokens that the target model verifies in parallel. Diffusio

researcharxiv-cs-cl

DeepSeek harness 0.2 - Optional Bundle Architecture, Windows Sandbox improvements, Async Question Mode, Desktop release, Web Search without key

model-releasesr-localllama

Optional Bundle architecture: Schedule (session-local delayed / timed / interval reminders) was removed from the default set and made an explicit Optional Bundle. This cleanly separates “installed” fr

Defining and Categorising Human-AI Interactions in Clinical Trials: A Multidimensional Human-AI Classification Approach

arXiv:2609.38559v1 Announce Type: new Abstract: This paper examines human-AI interactions (HAIIs) in clinical trials and presents a multidimensional categorisation framework that classifies interactio

Delta-K: Boosting Multi-Instance Generation via Cross-Attention Augmentation

arXiv:2603.10210v2 Announce Type: replace-cross Abstract: While Diffusion Models excel in text-to-image synthesis, they frequently suffer from catastrophic concept omission when generating complex mul

safetyarxiv-cs-ai

Democratizing Managed Lustre with lower cost and frictionless development

model-releasesgoogle-cloud-ai

This is the first of a two-part series exploring how Google Cloud is bringing the foundational values of a high-performance parallel filesystem–TB/s throughput, sub-ms latency at high client scale, an

Demographic Pluralism: Inference-Time Modeling of Pluralistic Human Preference Distributions

arXiv:2609.38555v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in culturally sensitive settings, where alignment requires representing diverse preferences within po

safetyarxiv-cs-ai

Hardware

Dense Temporal Motion Retargeting for Legged Robots

arXiv:2609.38617v1 Announce Type: new Abstract: Legged robots can learn expressive whole-body skills from the motions of humans and animals. Due to the morphology gap between the source and the robot,

hardwarearxiv-cs-ro

DensePed-Lite: Quality-Aware Adaptive Detection for Dense Pedestrians under Occlusion

arXiv:2609.39467v1 Announce Type: new Abstract: Pedestrian detection plays a crucial role in computer vision with applications in autonomous driving, surveillance, and public safety. However, real-wor

safetyarxiv-cs-cv

Applications

Depth Anything in 360^irc: Towards Scale Invariance in the Wild

arXiv:2512.22819v2 Announce Type: replace Abstract: Panoramic depth estimation captures the complete 360^irc scene geometry, being essential for robotics and AR/VR applications. While perspective dept

applicationsarxiv-cs-cv

Derandomizing Dense Binary Hypervector Codebooks for Quantized Scalars

arXiv:2609.38471v1 Announce Type: cross Abstract: Hyperdimensional computing and vector symbolic architectures often represent quantized scalar levels by dense binary codebooks whose level-to-level si

safetyarxiv-cs-ai

Detail in Context: A Dual-Scale Machine Learning Framework for Mycosis Fungoides Detection

arXiv:2609.38560v1 Announce Type: new Abstract: Mycosis fungoides (MF) is a rare form of cutaneous T-cell lymphoma that is often misdiagnosed in early stages due to its visual similarity to benign inf

Determining Vertical Displacement of Agricultural Areas Using UAV-Photogrammetry and a Heteroscedastic Deep Learning Model

arXiv:2609.39756v1 Announce Type: new Abstract: This article introduces an algorithm that uses a U-Net architecture to determine vertical ground surface displacements from unmanned aerial vehicle (UAV

Diagnosing On-Policy Self-Distillation for Reasoning Language Models

arXiv:2609.39118v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) has attracted growing interest as a promising approach to improve the reasoning ability of language models. Without e

safetyarxiv-cs-cl

DiDA: Video Object Segmentation with Distillation Learning of Deformable Attention

arXiv:2401.13937v3 Announce Type: replace Abstract: Video object segmentation is a fundamental research problem in computer vision. Recent techniques have often applied attention mechanism to object r

DiFF: Doppler-informed Flow Matching for Human Motion Flow

arXiv:2609.39098v1 Announce Type: cross Abstract: Perceiving human motion via privacy-preserving 4D millimeter-wave (mmWave) radar is critical for next-generation human-robot interaction (HRI), where

Differentiable Expectation-Maximisation and Applications to Gaussian Mixture Model Optimal Transport

arXiv:2509.02109v3 Announce Type: replace Abstract: The Expectation-Maximisation (EM) algorithm is a central tool in statistics and machine learning, widely used for latent-variable models such as Gau

Differentiable Structure Learning for Cyclic Linear Gaussian Models with Latent Confounders

arXiv:2609.38618v1 Announce Type: cross Abstract: We study causal structure learning from observational data in linear Gaussian structural causal models in the presence of directed cycles and an unkno

Diffusable Latents from Structure-Agnostic Distillation

arXiv:2609.39657v1 Announce Type: new Abstract: Distilling pretrained foundation models into an autoencoder bottleneck improves latent diffusability, enabling diffusion models to converge faster and r

Diffusion-2BC: Hybrid Diffusion and Regression Training for Offline Behavior Cloning in Autonomous Driving

arXiv:2609.38472v1 Announce Type: cross Abstract: Behavior cloning provides an offline route to autonomous-driving policy learning, but mean-squared-error regression is poorly matched to demonstration

DiffWAM: A Fast and Efficient Navigation World Action Model

model-releasesarxiv-cs-ai

arXiv:2609.39763v1 Announce Type: cross Abstract: Pretrained video foundation models encode rich semantic and spatiotemporal priors for embodied navigation, yet converting these priors into UAV motion

Agents

DigitalOcean MicroVMs: the compute foundation for building your AI agent infrastructure (private preview)

An AI agent uses compute differently than a traditional application. Over a single session, it writes and runs code, calls a model, and waits on a tool or a person before picking the work back up. As

agentsdigitalocean

Dimension-Free Rank Lifting from Random Hyperplane Arrangements

arXiv:2609.39855v1 Announce Type: new Abstract: We study the width required for a randomly initialized hidden layer of a neural network to achieve rank lifting. Namely, given a dataset X in R^{m imes

Disagreement-Regularized Imitation Learning for Image-Based Continuous Control with Gaussian and Beta Policies

arXiv:2609.38407v1 Announce Type: new Abstract: Purpose: Behavior cloning can accumulate errors when a learned controller visits states outside the demonstrated distribution. This study evaluates whet

safetyarxiv-cs-lg

Discrete Forcing: Infusing Discrete Guidance into Continuous Denoising for Few-Step Action Experts

model-releasesarxiv-cs-ro

arXiv:2609.39526v1 Announce Type: new Abstract: Efficient action generation in vision-language-action (VLA) models requires capturing both coarse action structure and fine-grained details. Discrete ac

Discrete Score Matching Enables Causal Discovery from Count Data

arXiv:2609.39326v1 Announce Type: cross Abstract: Count data pose a challenge for score-matching-based causal discovery: derivatives are unavailable, and simply replacing them with finite differences

Disentangling Computation in Multi-Task Neural Networks with the Green's Operator

arXiv:2609.40292v1 Announce Type: new Abstract: How is computation organized and reused across tasks and time in a trained recurrent network? Most analyses emphasize the geometry of neural activity, d

Disentangling Self-Distillation: Measuring and Modeling Acquisition and Retention

arXiv:2609.39494v1 Announce Type: new Abstract: Self-distillation with privileged context adapts a language model from demonstrations by letting the model, once conditioned on a reference response, te

Distill the Visual Evidence, Not Just the Answer: Cross-World On-Policy Distillation for Vision-Language Models

arXiv:2609.38777v1 Announce Type: new Abstract: A central goal of vision-language model (VLM) distillation is to transfer both the teacher's language capabilities and its visual understanding. However

Local Ai

Distilling Diffusion Score Discrepancy for Efficient Training Data Attribution

arXiv:2609.38776v1 Announce Type: cross Abstract: Training data attribution for diffusion models aims to identify the training samples that influence a generated instance, but existing methods either

local-aiarxiv-cs-ai

Distribution Matching Distillation for Continuous Diffusion Language Models

arXiv:2609.40235v1 Announce Type: cross Abstract: Continuous diffusion language models generate all tokens in parallel, yet high-quality generation can still require hundreds of network evaluations (N

researcharxiv-cs-cl

Distributionally robust linear regression through the lens of adversarial training

arXiv:2609.39449v1 Announce Type: cross Abstract: Distributionally robust optimization (DRO) studies parameter estimation under uncertainty in the underlying probability distribution and has emerged a

Diversity Combining for Multi-Path LLM Reasoning

arXiv:2609.38829v1 Announce Type: new Abstract: Multi-path reasoning methods such as self-consistency (SC) sample K reasoning paths and choose the most frequent answer. However, their gains quickly pl

Agents

Divide and Collapse: MAPF-Collapse via Exact Decomposition into Independent Sub-Instances

arXiv:2609.39559v1 Announce Type: new Abstract: In this work we study the problem of MAPFC, a post-optimization step for Multi-Agent Path Finding (MAPF) plans where we are given a feasible plan produc

agentsarxiv-cs-ai

DLRMv4: The Next-Generation Recommendation MLPerf Training Benchmark

Evolving MLPerf Training from feature-interaction baselines to production-scale sequential recommendation with HSTU and the 560 GB Yambda-5B embedding benchmark. The post DLRMv4: The Next-Generation R

model-releasesmlcommons

Do Better Goal Representations Improve Goal-Conditioned Reinforcement Learning?

model-releasesarxiv-cs-ai

arXiv:2609.39901v1 Announce Type: cross Abstract: Goal-conditioned reinforcement learning (GCRL) relies heavily on how target goals are represented to the policy. While recent methods encode goals via

Agents

Do Self-Evolving Skills Generalize to Held-Out Tasks?

arXiv:2609.39148v1 Announce Type: new Abstract: AI agents can externalize what they learn from past tasks into reusable skills, such as procedures, checklists, code, or other executable artifacts, tha

agentsarxiv-cs-ai