All entries

cuNRTO: GPU-Accelerated Nonlinear Robust Trajectory Optimization

arXiv:2603.02642v2 Announce Type: replace Abstract: Robust trajectory optimization enables autonomous systems to operate safely under uncertainty by computing control policies that satisfy the constra

hardwarearxiv-cs-ro

Content type

All Blog X Post Paper YouTube Reddit GitHub

Curious finding while creating evals and benchmarks for long-horizon (100+ turn) agents While it’s generally thought that a direct swap to o…

DGX agent

Curious finding while creating evals and benchmarks for long-horizon (100+ turn) agents While it’s generally thought that a direct swap to open source models can bring immediate cost savings, that’s n

agentsharrison-chase--x

19 May 2026

Curriculum Group Policy Optimization: Adaptive Sampling for Unleashing the Potential of Text-to-Image Generation

arXiv:2605.17807v1 Announce Type: cross Abstract: Text-to-Image (T2I) generation has achieved remarkable progress in recent years. Meanwhile, reinforcement learning methods, particularly those based o

Cursor is now available in Jira. Assign Cursor to work items, or mention @Cursor in a comment to kick off a cloud agent. Cursor uses the ti…

Cursor is now available in Jira. Assign Cursor to work items, or mention @Cursor in a comment to kick off a cloud agent. Cursor uses the title, description, comments, and your team's repository setti

agentscursor--x

Industry

customers are increasingly asking us for certainty on capacity. as models get better, we expect that the world will be capacity-constrained …

customers are increasingly asking us for certainty on capacity. as models get better, we expect that the world will be capacity-constrained for some time. we are offering discounted tokens for 1-3 yea

industrysam-altman--x

CVE-Factory: Scaling Expert-Level Agentic Tasks for Code Security Vulnerability

arXiv:2602.03012v2 Announce Type: replace-cross Abstract: Evaluating and improving the security capabilities of code agents requires high-quality, executable vulnerability tasks. However, existing wor

CyberCorrect: A Cybernetic Framework for Closed-Loop Self-Correction in Large Language Models

arXiv:2605.17305v1 Announce Type: new Abstract: Large language model (LLM) self-correction -- the ability to detect and fix errors in generated outputs -- remains largely ad hoc, relying on generic pr

D^2Evo: Dual Difficulty-Aware Self-Evolution for Data-Efficient Reinforcement Learning

arXiv:2605.17037v1 Announce Type: cross Abstract: Reinforcement learning (RL) has demonstrated potential for enhancing reasoning in large language models (LLMs). However, effective RL training, which

DACA-GRPO: Denoising-Aware Credit Assignment for Reinforcement Learning in Diffusion Language Models

arXiv:2605.16342v1 Announce Type: cross Abstract: Diffusion large language models are a compelling alternative to autoregressive models, yet existing RL methods for diffusion treat all denoising steps

Applications

DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Data

arXiv:2605.17866v1 Announce Type: new Abstract: Small-scale data is a critical problem in time-series forecasting tasks. Data augmentation is an effective strategy for this task, but it has a limitati

applicationsarxiv-cs-lg

Dance Across Shifts: Forward-Facilitation Continual Test-Time Adaptation through Dynamic Style Bridging

arXiv:2605.18608v1 Announce Type: new Abstract: Continual Test-Time Adaptation (CTTA) aims to empower perception systems to handle dynamic distribution shifts encountered after deployment. Existing me

safetyarxiv-cs-cv

Applications

DanceHMR: Hand-Aware Whole-Body Human Mesh Recovery from Monocular Videos

arXiv:2605.18102v1 Announce Type: new Abstract: Monocular video human mesh recovery is essential for digital humans, avatar animation, and embodied simulation, where both temporal stability and expres

applicationsarxiv-cs-cv

DARC: Disagreement-Aware Alignment via Risk-Constrained Decoding

arXiv:2603.08145v2 Announce Type: replace-cross Abstract: Preference-based alignment methods (e.g., RLHF, DPO) typically optimize a single scalar objective, implicitly averaging over heterogeneous hum

DARE-EEG: A Foundation Model for Mining Dual-Aligned Representation of EEG

arXiv:2605.18298v1 Announce Type: new Abstract: Foundation models pre-trained through masked reconstruction on large-scale EEG data have emerged as a promising paradigm for learning generalizable neur

Darwinium pushes mobile fraud detection beyond the login moment

model-releasessiliconangle

Artificial intelligence fraud prevention platform company Darwinium UK Ltd. today released updates to its Android and iOS mobile software development kits, adding continuous in-session detection of re

DASH: A Meta-Attack Framework for Synthesizing Effective and Stealthy Adversarial Examples

arXiv:2508.13309v3 Announce Type: replace Abstract: Numerous techniques have been proposed for generating adversarial examples in white-box settings under strict Lp-norm constraints. However, such nor

DashAttention: Differentiable and Adaptive Sparse Hierarchical Attention

arXiv:2605.18753v1 Announce Type: cross Abstract: Current hierarchical attention methods, such as NSA and InfLLMv2, select the top-k relevant key-value (KV) blocks based on coarse attention scores and

hardwarearxiv-cs-ai

Data-driven and distributed governance of building facilities management using decentralized autonomous organization, digital twin, and large language models

arXiv:2605.16298v1 Announce Type: cross Abstract: While traditional AI and data-driven facilities management approaches have improved building operational efficiency, they remain constrained by centra

agentsarxiv-cs-ai

Data-Driven Dynamic Modeling of a Tendon-Actuated Continuum Robot

arXiv:2605.18720v1 Announce Type: new Abstract: Developing dynamic models for tendon-driven continuum robots is challenging due to their nonlinear, high-dimensional, and friction-dominated dynamics. T

researcharxiv-cs-ro

Data Presentation Over Architecture: Resampling Strategies for Credit Risk Prediction with Tabular Foundation Models

arXiv:2605.18635v1 Announce Type: cross Abstract: Credit default prediction is a tabular learning problem with severe class imbalance, heterogeneous features, and tight latency budgets. Tabular Founda

Databricks context engineer associate: the industry’s first certification for reliable AI agent systems

Databricks has launched the Context Engineer Associate certification, described as the industry's first credential specifically designed for professionals building reliable AI agent systems. The certi

agentsdatabricks

Tools

datasette-llm 0.1a8

datasette-llm is a plugin that integrates LLM functionality into Datasette for language model operations. Version 0.1a8 represents an alpha release of this plugin that extends Datasette's capabilities

toolssimon-willison

Tools

datasette-llm-accountant 0.1a4

datasette-llm-accountant is a plugin that handles allowances and estimated pricing features for Datasette LLM usage. It works with datasette-llm and datasette-llm-limits to let users configure spendin

toolssimon-willison

DBES: A Systematic Benchmark and Metric Suite for Evaluating Expert Specialization in Large-Scale MoEs

arXiv:2605.18498v1 Announce Type: cross Abstract: Expert specialization in Mixture-of-Experts (MoE) models remains poorly understood, with traditional evaluations conflating architectural load-balanci

DCFold: Efficient Protein Structure Generation with Single Forward Pass

arXiv:2605.17899v1 Announce Type: cross Abstract: AlphaFold3 introduces a diffusion-based architecture that elevates protein structure prediction to all-atom resolution with improved accuracy. This st

Decart raises $300M for its AI optimization software, world models

Artificial intelligence developer Decart.ai Inc. today announced that it has raised 300 million in funding at a nearly 4 billion valuation. Radical Ventures led the round with participation from Nvidi

hardwaresiliconangle

Decision-Aware Proximal Bridge Learning for Optimal Treatment Selection

arXiv:2605.16989v1 Announce Type: new Abstract: Individualized treatment selection with continuous actions requires accurate causal response estimation in decision-relevant regions, rather than unifor

safetyarxiv-cs-lg

DECODE: Domain-aware Continual Domain Expansion for Motion Prediction

arXiv:2411.17917v2 Announce Type: replace Abstract: Motion prediction is critical for autonomous vehicles to effectively navigate complex environments and accurately anticipate the behaviors of other

agentsarxiv-cs-cv

DecoRec: Decomposed 3D Scene Reconstruction from Single-View Images via Object-Level Diffusion

arXiv:2605.16807v1 Announce Type: new Abstract: In this paper, we introduce extit{DecoRec}, a novel system designed to elevate single-view 2D images to a decomposed 3D scene mesh. Current methods for

Applications

Decouple then Converge: Handling Unknown Unlabeled Distributions in Long-Tailed Semi-Supervised Learning

arXiv:2406.13187v2 Announce Type: replace Abstract: While long-tailed semi-supervised learning (LTSSL) has attracted growing attention in many real-world classification tasks, existing LTSSL algorithm

applicationsarxiv-cs-lg

Decoupled Conformal Optimisation: Efficient Prediction Sets via Independent Tuning and Calibration

model-releasesarxiv-cs-lg

arXiv:2605.18354v1 Announce Type: new Abstract: Bayesian conformal optimisation methods often use the same held-out data both to search for efficient prediction sets and to certify coverage or risk. T

DecoupleSearch: Decouple Planning and Search via Hierarchical Reward Modeling

arXiv:2510.21712v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) systems have emerged as a pivotal methodology for enhancing Large Language Models (LLMs) through the dyna

Decoupling KL and Trajectories: A Unified Perspective for SFT, DAgger, Offline RL, and OPD in LLM Distillation

arXiv:2605.16826v1 Announce Type: cross Abstract: Knowledge distillation is central to LLM post-training, yet its design space remains poorly understood, especially alongside reinforcement learning (R

Decoupling Motion and Geometry in 4D Gaussian Splatting

arXiv:2603.00952v2 Announce Type: replace Abstract: High-fidelity reconstruction of dynamic scenes is an important yet challenging problem. While recent 4D Gaussian Splatting (4DGS) has demonstrated t

Deep Agents now integrates with @nebiusai Token Factory. Now, you can run agent workloads on production-grade AI infrastructure with open-so…

Deep Agents now integrates with @nebiusai Token Factory. Now, you can run agent workloads on production-grade AI infrastructure with open-source models, dedicated endpoints, real-time search, and full

agentsharrison-chase--x

Deep Learning-Based Channel Extrapolation for Dual-Band Massive MIMO Systems

arXiv:2601.06858v2 Announce Type: replace-cross Abstract: Future wireless communication systems will increasingly rely on the integration of millimeter wave (mmWave) and sub-6 GHz bands to meet hetero

researcharxiv-cs-lg

Deep learning-based compression of giga-resolution whole slide images

arXiv:2605.17668v1 Announce Type: new Abstract: Implementation of digital pathology leads to an increased number of whole slide images (WSIs). The large size of WSIs is challenging. Today, WSIs are co

Deep Learning for MRI Slice Interpolation: The Critical Role of Problem Formulation

arXiv:2605.16476v1 Announce Type: cross Abstract: Through-plane resolution in clinical MRI is typically much coarser than in-plane resolution, limiting diagnostic utility. This work investigates deep

Deep Reinforcement Learning Framework for Diversified Portfolio Management Across Global Equity Markets

arXiv:2605.17307v1 Announce Type: cross Abstract: This study develops and evaluates a deep reinforcement learning framework for dynamic portfolio allocation across global equity markets. The Soft Acto

Deep sequence models tend to memorize geometrically; it is unclear why

arXiv:2510.26745v3 Announce Type: replace-cross Abstract: Deep sequence models are said to store atomic facts predominantly in the form of associative memory: a brute-force lookup of co-occurring enti

Tutorials

deepagents v0.6 is about performance the first level at which we can control that is the model layer: how can you squeeze perf out of a mode…

tutorialsharrison-chase--x

deepagents v0.6 is about performance the first level at which we can control that is the model layer: how can you squeeze perf out of a model? tweaking prompts, tool names, and tool descriptions in ac

DeepArrhythmia: Segment-Contextualized ECG Arrhythmia Classification via Selective Evidence Acquisition

arXiv:2605.16441v1 Announce Type: cross Abstract: Beat-level Electrocardiography (ECG) arrhythmia detection aims to assign an arrhythmia class to each beat in a recording, yet many existing systems tr

agentsarxiv-cs-ai

Deepfake Detection in Social Media: A Temporal Artifact Analysis Using 3D Convolutional Neural Networks

arXiv:2605.17573v1 Announce Type: new Abstract: Synthetic facial videos have proliferated across social media faster than platform moderation can respond, raising the cost of disinformation and identi

Tutorials

Degradation Frequency Curve: An Explicit Frequency-Quantified Representation for All-in-One Image Restoration

arXiv:2605.17506v1 Announce Type: new Abstract: A fundamental difficulty in all-in-one blind image restoration is that degradation is usually treated as an implicit factor hidden in degraded-to-clean

tutorialsarxiv-cs-cv

Industry

Dell overhauls data center portfolio with AI-focused storage, servers and cyber resilience tools

Dell Technologies Inc. today unveiled a broad set of storage, compute, cyber resilience and automation upgrades designed to help enterprises modernize data centers for artificial intelligence workload

industrysiliconangle

Dell’s CFO sees the enterprise AI buildout as a generational opportunity still in its opening act

AI factory momentum has reached a critical inflection point as capital joins silicon and energy as key constraints in the race to build enterprise AI infrastructure. That dynamic is playing out in rea

hardwaresiliconangle

DeMa: Dual-Path Delay-Aware Mamba for Efficient Multivariate Time Series Analysis

arXiv:2601.05527v2 Announce Type: replace-cross Abstract: Accurate and efficient multivariate time series (MTS) analysis is increasingly critical for a wide range of intelligent applications. Within t

Industry

Demis Hassabis said this might be the ‘foothills of the singularity.’ What?

Welcome to a 'profound moment for humanity,' according to Google DeepMind CEO Demis Hassabis, who closed out Google I/O's keynote presentation on Tuesday, saying: Google's cutting-edge research and pr

industrythe-verge-ai