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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

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Human DGX agent

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Tutorials

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

DGX agent

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

12 May 2026

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

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

Tutorials

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

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

Applications

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

Dynamic Meta-Metrics: Source-Sentence Conditioned Weighting for MT Evaluation

arXiv:2605.09098v1 Announce Type: new Abstract: We propose Dynamic Meta-Metrics (DMM), a framework for machine translation evaluation that learns source-sentence conditioned combinations of existing m

researcharxiv-cs-cl

Dynamic Skill Lifecycle Management for Agentic Reinforcement Learning

arXiv:2605.10923v1 Announce Type: cross Abstract: Large language model agents increasingly rely on external skills to solve complex tasks, where skills act as modular units that extend their capabilit

safetyarxiv-cs-cl

Dynamics-Aligned Shared Hypernetworks for Contextual RL under Discontinuous Shifts

arXiv:2602.06550v2 Announce Type: replace-cross Abstract: Zero-shot generalization in contextual reinforcement learning remains a core challenge, particularly when the context is latent and must be in

DynaMiCS: Fine-tuning LLMs with Performance Constraints using Dynamic Mixtures

arXiv:2605.10770v1 Announce Type: new Abstract: Multi-domain fine-tuning of large language models requires improving performance on target domains while preserving performance on constrained domains,

safetyarxiv-cs-lg

DynGhost: Temporally-Modelled Transformer for Dynamic Ghost Imaging with Quantum Detectors

arXiv:2605.10185v1 Announce Type: cross Abstract: Ghost imaging reconstructs spatial information from a single-pixel bucket detector by correlating structured illumination patterns with scalar intensi

researcharxiv-cs-ai

Dystruct: Dynamically Structured Diffusion Language Model Decoding via Bayesian Inference

arXiv:2605.09820v1 Announce Type: new Abstract: Diffusion language models (DLMs) have recently emerged as a promising alternative to autoregressive models, primarily due to their ability to enable par

local-aiarxiv-cs-lg

DySurface: Consistent 4D Surface Reconstruction via Bridging Explicit Gaussians and Implicit Functions

arXiv:2605.10360v1 Announce Type: new Abstract: While novel view synthesis (NVS) for dynamic scenes has seen significant progress, reconstructing temporally consistent geometric surfaces remains a cha

researcharxiv-cs-cv

E-TCAV: Formalizing Penultimate Proxies for Efficient Concept Based Interpretability

arXiv:2605.10261v1 Announce Type: new Abstract: TCAV (Testing with Concept Activation Vectors) is an interpretability method that assesses the alignment between the internal representations of a train

Tutorials

EAM: Enhancing Anything with Diffusion Transformers for Blind Super-Resolution

arXiv:2505.05209v4 Announce Type: replace Abstract: Utilizing pre-trained Text-to-Image (T2I) diffusion models to guide Blind Super-Resolution (BSR) has become a predominant approach in the field. Whi

tutorialsarxiv-cs-cv

EAR: Enhancing Uni-Modal Representations for Weakly Supervised Audio-Visual Video Parsing

arXiv:2605.08723v1 Announce Type: new Abstract: Weakly supervised Audio-Visual Video Parsing (AVVP) aims to recognize and temporally localize audio, visual, and audio-visual events in videos using onl

local-aiarxiv-cs-cv

Echo-LoRA: Parameter-Efficient Fine-Tuning via Cross-Layer Representation Injection

arXiv:2605.08177v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) has become a practical route for adapting large language models to downstream tasks, with LoRA-style methods be

EchoAlign: Bridging Generative and Discriminative Learning under Noisy Labels

arXiv:2405.12969v3 Announce Type: replace Abstract: Noisy labels severely hinder the accuracy and generalization of machine learning models, especially when ambiguous instance features make reliable a

Applications

EchoFake: A Replay-Aware Dataset for Practical Speech Deepfake Detection

arXiv:2510.19414v2 Announce Type: replace-cross Abstract: The growing prevalence of speech deepfakes has raised serious concerns, particularly in real-world scenarios such as telephone fraud and ident

applicationsarxiv-cs-ai

EchoPrune: Interpreting Redundancy as Temporal Echoes for Efficient VideoLLMs

arXiv:2605.10050v1 Announce Type: new Abstract: Long-form video understanding remains challenging for Video Large Language Models (VideoLLMs), as the dense frame sampling introduces massive visual tok

researcharxiv-cs-cv

EcoGym: Evaluating LLMs for Long-Horizon Plan-and-Execute in Interactive Economies

arXiv:2602.09514v3 Announce Type: replace-cross Abstract: Long-horizon planning is widely recognized as a core capability of autonomous LLM-based agents; however, current evaluation frameworks suffer

EconWebArena: Benchmarking Autonomous Agents on Economic Tasks in Realistic Web Environments

model-releasesarxiv-cs-cl

arXiv:2506.08136v3 Announce Type: replace Abstract: We introduce EconWebArena, a benchmark for evaluating autonomous agents on complex, multimodal economic tasks in realistic web environments. The ben

Edge-specific signal propagation on mature chromophore-region 3D mechanism graphs for fluorescent protein quantum-yield prediction

arXiv:2605.06644v2 Announce Type: replace Abstract: Fluorescent protein quantum yield (QY) is governed by the mature chromophore and its three-dimensional microenvironment rather than sequence identit

EdgeFlowerTune: Evaluating Federated LLM Fine-Tuning Under Realistic Edge System Constraints

model-releasesarxiv-cs-cl

arXiv:2605.08636v1 Announce Type: new Abstract: Federated fine-tuning offers a promising paradigm for adapting large language models (LLMs) on edge devices by leveraging the rich, diverse, and continu

Edit-Based Refinement for Parallel Masked Diffusion Language Models

arXiv:2605.09603v1 Announce Type: new Abstract: Masked diffusion language models enable parallel token generation and offer improved decoding efficiency over autoregressive models. However, their perf

researcharxiv-cs-cl

EditSleuth: A Dataset of Grounded Reasoning Chains for Image-Edit Forensics

arXiv:2605.08695v1 Announce Type: new Abstract: Forensic analysis of AI-edited images requires more than binary real-versus-fake prediction: a useful system should localize the edit, identify its sema

local-aiarxiv-cs-cv

EduStory: A Unified Framework for Pedagogically-Consistent Multi-Shot STEM Instructional Video Generation

arXiv:2605.09378v1 Announce Type: cross Abstract: Long-horizon video generation has advanced in visual quality, yet existing methods still struggle to maintain knowledge consistency and coherent pedag

Effective Explanations Support Planning Under Uncertainty

arXiv:2605.08406v1 Announce Type: cross Abstract: Explaining how to get from A to B can be challenging. It requires mentally simulating what the listener will do based on what they are told. To captur

Hardware

Efficient Dataset Distillation for Pre-Trained Self-Supervised Models via Statistical Flow Matching

arXiv:2602.05391v2 Announce Type: replace Abstract: Dataset distillation seeks to synthesize a highly compact dataset that achieves performance comparable to the original dataset on downstream tasks.

hardwarearxiv-cs-cv

Efficient Ensemble Selection from Binary and Pairwise Feedback

arXiv:2605.09588v1 Announce Type: cross Abstract: Organizations increasingly deploy multiple AI systems across task domains, but selecting a small, high-performing ensemble can require costly model ca

Tutorials

Efficient Estimation of Kernel Surrogate Models for Task Attribution

arXiv:2602.03783v2 Announce Type: replace-cross Abstract: Modern AI agents such as large language models are trained on diverse tasks -- translation, code generation, mathematical reasoning, and text

tutorialsarxiv-cs-ai

Efficient Evaluation of LLM Performance with Statistical Guarantees

arXiv:2601.20251v3 Announce Type: replace-cross Abstract: Exhaustively evaluating many large language models (LLMs) on a large suite of benchmarks is expensive. We cast benchmarking as finite-populati

Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation

arXiv:2605.10251v1 Announce Type: new Abstract: We present GraphDepth, a monocular depth estimation architecture that synergistically integrates Graph Neural Networks (GNNs) within a convolutional enc

local-aiarxiv-cs-cv

Efficient LLM Collaboration via Planning

arXiv:2506.11578v4 Announce Type: replace Abstract: Recently, large language models (LLMs) have demonstrated strong performance, ranging from simple to complex tasks. However, while large models achie

local-aiarxiv-cs-ai

Agents

Efficient Multi-Robot Motion Planning with Precomputed Translation-Invariant Edge Bundles

arXiv:2605.09801v1 Announce Type: new Abstract: Solving multi-robot motion planning (MRMP) requires generating collision-free kinodynamically feasible trajectories for multiple interacting robots. We

agentsarxiv-cs-ro

Efficient Neural Architectures for Real-Time ECG Interpretation on Limited Hardware

arXiv:2605.09848v1 Announce Type: new Abstract: Electrocardiogram (ECG) interpretation is essential for diagnosing a wide range of cardiac abnormalities. While deep learning has shown strong potential

Applications

Efficient Prompt Learning for Traffic Forecasting

arXiv:2605.08273v1 Announce Type: cross Abstract: Accurate traffic prediction is essential for optimizing transportation systems, enhancing resource allocation, and improving overall urban administrat

applicationsarxiv-cs-ai

Efficient Statistics With Unknown Truncation, Polynomial Time Algorithms, Beyond Gaussians

arXiv:2410.01656v2 Announce Type: replace-cross Abstract: We study the estimation of distributional parameters when samples are shown only if they fall in some unknown set S subseteq R^d. Kontonis, Tz

researcharxiv-cs-lg