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Direct Bethe Free Energy Minimization for Bayesian Neural Ne twork

model-releasesarxiv-cs-lg

arXiv:2605.08446v1 Announce Type: new Abstract: We propose training Bayesian neural networks by directly minimizing the Bethe free energy rather than maximizing a variational lower bound. On tree-stru

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Direct From Darwin: Deriving Advanced Optimizers From Evolutionary First Principles

DGX agent

arXiv:2605.05284v2 Announce Type: replace-cross Abstract: Evolutionary computation has long promised to deliver both high-performance optimization tools as well as rigorous scientific simulations of D

researcharxiv-cs-lg

12 May 2026

Discovery of Nonlinear Dynamics with Automated Basis Function Generation

arXiv:2605.09696v1 Announce Type: new Abstract: Discovering governing equations from observational data remains a fundamental challenge in scientific modeling, particularly when the underlying mathema

Discrete Double-Bracket Flows for Isotropic-Noise Invariant Eigendecomposition

arXiv:2602.13759v2 Announce Type: replace Abstract: We study eigendecomposition on SO(n) under streaming observations C_k = C_{sig} + sigma_k^2 I + E_k, where the isotropic background sigma_k^2 I may

Discrete Flow Matching: Convergence Guarantees Under Minimal Assumptions

arXiv:2605.08882v1 Announce Type: new Abstract: Flow Matching has recently emerged as a popular class of generative models for simulating a target distribution mu_1 from samples drawn from a source di

Discrete Langevin-Inspired Posterior Sampling

arXiv:2605.09302v1 Announce Type: cross Abstract: We study posterior sampling for inverse problems in discrete state spaces using discrete diffusion models as generative priors. While continuous diffu

researcharxiv-cs-cv

Discriminative Span as a Predictor of Synthetic Data Utility via Classifier Reconstruction

arXiv:2605.09697v1 Announce Type: new Abstract: In many real-world computer vision applications, including medical imaging and industrial inspection, binary classification tasks are characterized by a

applicationsarxiv-cs-cv

Disentangled Representation Learning via Flow Matching

arXiv:2602.05214v2 Announce Type: replace Abstract: Disentangled representation learning aims to capture the underlying explanatory factors of observed data, enabling a principled understanding of the

safetyarxiv-cs-lg

Dissecting Jet-Tagger Through Mechanistic Interpretability

arXiv:2605.09881v1 Announce Type: cross Abstract: Mechanistic interpretability seeks to reverse engineer a trained neural network by identifying the minimal subset of internal components. We perform a

Agents

Distill, Diffuse, and Semanticize (DDS): Annotation-Free 3D Scene Understanding Based on Multi-Granularity Distillation and Graph-Diffusion-Based Segmentation

arXiv:2605.08293v1 Announce Type: new Abstract: 3D semantic scene understanding has broad applications in digital twins, autonomous driving, smart agriculture, and embodied perception. However, dense

agentsarxiv-cs-cv

Distilling 3D Spatial Reasoning into a Lightweight Vision-Language Model with CoT

arXiv:2605.09719v1 Announce Type: cross Abstract: Large-scale 3D vision-language models (VLMs) like LLaVA-3D offer strong spatial reasoning but are difficult to deploy due to high computational costs.

applicationsarxiv-cs-ai

Distributional Reinforcement Learning via the Cramer Distance

arXiv:2605.08104v1 Announce Type: new Abstract: This paper explores the application of the Soft Actor-Critic (SAC) algorithm within a Distributional Reinforcement Learning setting and introduces an im

Distributional Spectral Diagnostics for Localizing Grokking Transitions

model-releasesarxiv-cs-lg

arXiv:2605.08237v1 Announce Type: new Abstract: In grokking, a model first fits the training data while test accuracy remains low, and only later begins to generalize. We ask whether this transition c

Do Agents Need to Plan Step-by-Step? Rethinking Planning Horizon in Data-Centric Tool Calling

arXiv:2605.08477v1 Announce Type: new Abstract: Explicit planning is a critical capability for LLM-based agents solving complex data-centric tasks, which require precise tool calling over external dat

tutorialsarxiv-cs-cl

Do Benchmarks Underestimate LLM Performance? Evaluating Hallucination Detection With LLM-First Human-Adjudicated Assessment

arXiv:2605.08462v1 Announce Type: cross Abstract: Hallucination remains a persistent challenge in Large Language Models (LLMs), particularly in context-grounded settings such as RAG and agentic AI sys

Do Foundation Model Embeddings Improve Cross-Country Crop Yield Generalisation? A Leave-One-Country-Out Evaluation in Sub-Saharan Africa

model-releasesarxiv-cs-cv

arXiv:2605.08113v1 Announce Type: cross Abstract: Accurate predictions of smallholder maize yields across national boundaries are critical for food security planning in sub-Saharan Africa, yet most pu

Do Linear Probes Generalize Better in Persona Coordinates?

arXiv:2605.09391v1 Announce Type: new Abstract: It is becoming increasingly necessary to have monitors check for harmful behaviors during language model interactions, but text-only monitoring has not

safetyarxiv-cs-ai

Do LLMs Experience an Internal Polylogue? Investigating Reasoning through the Lens of Personas

arXiv:2605.09159v1 Announce Type: new Abstract: Recent work shows that large language models (LLMs) encode behavioural traits ('personas') as linear directions in activation space, often called 'perso

tutorialsarxiv-cs-ai

Do multimodal models imagine electric sheep?

arXiv:2605.09693v1 Announce Type: cross Abstract: Yes. We find that large multimodal models develop mental imagery when solving spatial puzzles, and they do imagine sheep when solving sheep puzzles. W

researcharxiv-cs-ai

Do not copy and paste! Rewriting strategies for code retrieval

arXiv:2605.08299v1 Announce Type: cross Abstract: Embedding-based code retrieval often suffers when encoders overfit to surface syntax. Prior work mitigates this by using LLMs to rephrase queries and

Do Self-Evolving Agents Forget? Capability Degradation and Preservation in Lifelong LLM Agent Adaptation

arXiv:2605.09315v1 Announce Type: new Abstract: Recent advances in LLM agents enable systems that autonomously refine workflows, accumulate reusable skills, self-train their underlying models, and mai

Do you actually own your document parsing infrastructure? 👀 At @llama_index, we wanted to make that easier, so we built 𝗹𝗶𝘁𝗲𝗽𝗮𝗿𝘀𝗲-…

model-releasesjerry-liu--x

Do you actually own your document parsing infrastructure? 👀 At @llama_index, we wanted to make that easier, so we built 𝗹𝗶𝘁𝗲𝗽𝗮𝗿𝘀𝗲-𝘀𝗲𝗿𝘃𝗲𝗿, a lightweight HTTP backend built on top of LiteParse that can

DocScope: Benchmarking Verifiable Reasoning for Trustworthy Long-Document Understanding

model-releasesarxiv-cs-cl

arXiv:2605.08888v1 Announce Type: new Abstract: Evaluating whether Multimodal Large Language Models can produce trustworthy, verifiable reasoning over long, visually rich documents requires evaluation

Dolphin-CN-Dialect: Where Chinese Dialects Matter

arXiv:2605.08961v1 Announce Type: new Abstract: We present Dolphin-CN-Dialect, a streaming-capable ASR model with a focus on Chinese and dialect-rich scenarios. Compared to the previous version, Dolph

applicationsarxiv-cs-cl

Domain-Adaptive Arrhythmia Classification Using a Hybrid Transformer on Wearable Heart Signals

arXiv:2605.08199v1 Announce Type: cross Abstract: Cardiovascular disease remains the leading cause of death globally, underscoring the need for effective, accessible monitoring solutions, particularly

Done, But Not Sure: Disentangling World Completion from Self-Termination in Embodied Agents

arXiv:2605.08747v1 Announce Type: new Abstract: Standard embodied evaluations do not independently score whether an agent correctly commits to task completion at episode closure, a capacity we call te

Don't Click That: Teaching Web Agents to Resist Deceptive Interfaces

arXiv:2605.09497v1 Announce Type: new Abstract: Vision-language model (VLM) based web agents demonstrate impressive autonomous GUI interaction but remain vulnerable to deceptive interface elements. Ex

Don't Fix the Basis -- Learn It: Spectral Representation with Adaptive Basis Learning for PDEs

arXiv:2605.10451v1 Announce Type: new Abstract: Spectral neural operators achieve strong performance for PDE learning, but rely on fixed global bases that limit their ability to represent spatially he

tutorialsarxiv-cs-lg

Don't Retrieve, Generate: Prompting LLMs for Synthetic Training Data in Dense Retrieval

model-releasesarxiv-cs-cl

arXiv:2504.21015v4 Announce Type: replace-cross Abstract: Training effective dense retrieval models typically relies on hard negative (HN) examples mined from large document corpora using methods such

Doubly Robust Proxy Causal Learning with Neural Mean Embeddings

arXiv:2605.09514v1 Announce Type: new Abstract: Unobserved confounding prevents standard covariate adjustment from identifying causal response functions in observational studies. Proxy causal learning

Tools

Download Windsurf to try it out https://windsurf.com/download

Windsurf is an AI-powered code editor developed by Codeium that combines traditional IDE functionality with generative AI capabilities for software development. The post promotes downloading Windsurf

toolswindsurf--x

Local Ai

DP-LAC: Lightweight Adaptive Clipping for Differentially Private Federated Fine-tuning of Language Models

arXiv:2605.10272v1 Announce Type: cross Abstract: Federated learning (FL) enables the collaborative training of large-scale language models (LLMs) across edge devices while keeping user data on-device

local-aiarxiv-cs-ai

DRIFT: Drift-Resilient Invariant-Feature Transformer for DGA Detection

arXiv:2605.10436v1 Announce Type: cross Abstract: Domain Generation Algorithms (DGAs) evolve continuously to evade botnet detection, posing a persistent challenge for dependable network defense. While

tutorialsarxiv-cs-lg

Drift is a Sampling Error: SNR-Aware Power Distributions for Long-Horizon Robotic Planning

model-releasesarxiv-cs-ro

arXiv:2605.09537v1 Announce Type: new Abstract: Despite rapid progress in Vision-Language-Action (VLA) models for robotic control, instruction drift remains a persistent failure mode in long-horizon t

Agents

DRIVE-C: A Controlled Corruption Dataset for Autonomous Driving

arXiv:2605.09774v1 Announce Type: new Abstract: DRIVE-C is a controlled corruption dataset designed to evaluate visual perception robustness in autonomous driving systems. It is built from real-world

agentsarxiv-cs-cv

Agents

DriveFuture: Future-Aware Latent World Models for Autonomous Driving

arXiv:2605.09701v1 Announce Type: new Abstract: Existing latent world models for autonomous driving have opened a promising path toward future-aware driving intelligence. However, they typically treat

agentsarxiv-cs-cv

DRNet: All-in-One Image Restoration via Prior-Guided Dynamic Reparameterization

model-releasesarxiv-cs-cv

arXiv:2605.08627v1 Announce Type: new Abstract: All-in-one image restoration aims to handle diverse degradations within a single model. However, existing methods often suffer from three key limitation

Drum Synthesis from Expressive Drum Grids via Neural Audio Codecs

arXiv:2605.10281v1 Announce Type: cross Abstract: Generating realistic drum audio directly from symbolic representations is a challenging task at the intersection of music perception and machine learn

safetyarxiv-cs-ai

Dsat: A Native SAT Solver for Discrete Logic

arXiv:2605.09347v1 Announce Type: new Abstract: Discrete variables are common in many applications, such as probabilistic reasoning, planning and explainable AI. When symbolic reasoning techniques are

researcharxiv-cs-ai

DSGBench: A Diverse Strategic Game Benchmark for Evaluating LLM-based Agents in Complex Decision-Making Environments

arXiv:2503.06047v2 Announce Type: replace Abstract: Large language model (LLM)-based agents are increasingly applied to complex strategic environments that demand long-horizon reasoning, multi-agent i

Dual-Path Hyperprior Informed Deep Unfolding Network for Image Compressive Sensing

arXiv:2605.09566v1 Announce Type: new Abstract: Recent Deep Unfolding Networks (DUNs) have significantly advanced Compressive Sensing (CS) by integrating iterative optimization with deep networks. How

researcharxiv-cs-cv

Dual Tuning for Reasoning Efficacy-Driven Data Curation in Multimodal LLM Training

arXiv:2603.04415v2 Announce Type: replace Abstract: Reasoning post-training improves Large Language Models (LLMs) on complex tasks such as mathematics and coding, but its benefits across diverse multi

tutorialsarxiv-cs-cl

Local Ai

DUALFloodGNN: Physics-informed Graph Neural Network for Operational Flood Modeling

arXiv:2512.23964v2 Announce Type: replace-cross Abstract: Flood models inform strategic disaster management by simulating the spatiotemporal hydrodynamics of flooding. While physics-based numerical fl

local-aiarxiv-cs-ai

DUET: Optimize Token-Budget Allocation for Reinforcement Learning with Verifiable Rewards

arXiv:2605.08441v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) generates hundreds of thousands of tokens per training step, with rollout generation dominating

DuetFair: Coupling Inter- and Intra-Subgroup Robustness for Fair Medical Image Segmentation

arXiv:2605.10521v1 Announce Type: cross Abstract: Medical image segmentation models can perform unevenly across subgroups. Most existing fairness methods focus on improving average subgroup performanc

safetyarxiv-cs-ai

Dungeons & Desktops: Building a procedurally generated roguelike with GitHub Copilot CLI

Learn how one Hubber used GitHub Copilot CLI to build an extension that turns any codebase into a unique, roguelike dungeon. The post Dungeons & Desktops: Building a procedurally generated roguelike w

tutorialsgithub-ai-blog

Dynamic Cross-Modal Prompt Generation for Multimodal Continual Instruction Tuning

arXiv:2605.10765v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) achieve strong performance through instruction tuning, yet real-world deployment often requires continual cap

applicationsarxiv-cs-ai

Dynamic Linear Coregionalization for Realistic Synthetic Multivariate Time Series

arXiv:2604.05064v2 Announce Type: replace-cross Abstract: Synthetic data is essential for training foundation models for time series (FMTS), but most generators assume static correlations, and are typ

researcharxiv-cs-ai