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CuteTTS: Efficient and High-Quality Speech Synthesis via Autoregressive Modeling of Continuous Latents

arXiv:2608.08638v1 Announce Type: cross Abstract: Zero-shot text-to-speech (TTS) now supports interactive assistants, personalized media, and accessibility tools. All TTS systems require faithful ling

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Safety

CyberAGENTS: Structured Autonomy for Agentic Gamified Learning in Cybersecurity

DGX agent

arXiv:2608.07965v1 Announce Type: new Abstract: Gamification is especially effective in learning domains requiring active problem-solving and iterative skill-building, such as cybersecurity education.

safetyarxiv-cs-ai

11 Aug 2026

DA-NBV: A Direction-Aware Next-Best-View Planner for Efficient 3D Reconstruction of Ships at Sea

arXiv:2608.08025v1 Announce Type: cross Abstract: Accurate 3D reconstruction of ships at sea is important for maritime supervision, damage assessment, and autonomous maritime operations. Although 3D r

Damage Classification for 3D Point Cloud Data via 3D Data Analysis and Vision Foundation Model-based 2D Projections

arXiv:2608.08955v1 Announce Type: new Abstract: Fine-grained damage classification of 3D point cloud data (PCD) remains a persistent challenge, constrained by high computational demands and limited la

Dancing Points: Synthesizing Ballroom Dancing with Three-Point Inputs

arXiv:2601.02096v2 Announce Type: replace-cross Abstract: Ballroom dancing is a structured yet expressive motion category. Its highly diverse movement and complex interactions between leader and follo

DarwinX: Evolving Agent Harnesses Through Natural Selection

model-releasesarxiv-cs-ai

arXiv:2608.07545v1 Announce Type: cross Abstract: An LLM agent's capability depends not only on model weights but on its harness: prompts, tools, skills, and control flow. Self-improvement loops alrea

Data collection from highways: a geometric, class-agnostic approach to embedded vehicle counting

arXiv:2608.07643v1 Announce Type: new Abstract: Traffic data collection is dominated today by deep object detectors followed by tracking-by-detection, a pipeline that presupposes what is often missing

Data-Driven Fire-Zone Segmentation for Improved Short-Term Wildfire Prediction

arXiv:2608.07472v1 Announce Type: new Abstract: Wildfire prediction models typically discretize study areas into uniform grids, ignoring the heterogeneous spatial distribution of ignitions. We challen

researcharxiv-cs-lg

Data Repetition Beats Data Scaling in Long-CoT Supervised Fine-Tuning

arXiv:2602.11149v2 Announce Type: replace Abstract: Supervised fine-tuning (SFT) on chain-of-thought data is an essential post-training step for reasoning language models. Standard machine learning in

researcharxiv-cs-cl

DAVE: A Decoupled Audio-Visual Enhancement Framework for Real-World Speech Separation

arXiv:2608.09288v1 Announce Type: cross Abstract: Audio-visual speech enhancement under real-world conditions remains challenging due to unreliable visual inputs and the lack of large-scale training d

applicationsarxiv-cs-ai

Decentralized Nonconvex Composite Federated Learning with Gradient Tracking and Momentum

model-releasesarxiv-cs-lg

arXiv:2504.12742v2 Announce Type: replace Abstract: Decentralized Federated Learning (DFL) enables collaborative model training without relying on a central server. When local objectives are nonconvex

Local Ai

Decided Upstream, Written Late: Locating and Pricing the Cross-Lingual Refusal Circuit of a Multilingual MoE

arXiv:2608.08032v1 Announce Type: new Abstract: Safety alignment in multilingual models is uneven: a model that reliably refuses a harmful request in English will often comply with the same request in

local-aiarxiv-cs-ai

Decision-Focused Learning in Network Interdiction Games

arXiv:2608.09036v1 Announce Type: cross Abstract: We study decision-focused learning (DFL) in shortest-path network interdiction (SPNI) games, a Stackelberg game where an interdictor (leader) strength

applicationsarxiv-cs-lg

DeCo: Zero-Shot Industrial Anomaly Generation through Decoupling and Recoupling

arXiv:2608.07904v1 Announce Type: new Abstract: Industrial anomaly inspection is severely hindered by the scarcity of real anomalous data.Zero-shot industrial anomaly generation addresses this by gene

Decoding-Level Taboo: A Diagnostic Stress Test for LLM Robustness

model-releasesarxiv-cs-cl

arXiv:2608.09900v1 Announce Type: new Abstract: Large language model evaluations typically focus on performance under nominal conditions, creating an illusion of capability where models comfortably wa

Decoding Phenotypes: A Framework for Fusing Genomic Language Models and Neuroimaging

arXiv:2608.08926v1 Announce Type: new Abstract: Neuroimaging and genetic testing are two important clinical references for nervous system diseases, offering complementary diagnostic information. Howev

Decoy Images Amplify Caption-Mediated Defenses Against Encoded Jailbreaks

arXiv:2608.01043v2 Announce Type: replace-cross Abstract: We report a counter-intuitive interaction between image inputs and existing black-box defenses on Vision--Language Models (VLMs): pairing an e

Deep Learning based Detection of Fishing Vessels and Fishing Monitoring using Nightlight Images

arXiv:2608.09360v1 Announce Type: cross Abstract: The demand for maritime surveillance has given rise to the need for monitoring fishing vessel activities, particularly in addressing the challenge of

Deep Learning Imputation of Missing Radius of Maximum Winds (Rmax) Values in Tropical Cyclone Best-Track Data

arXiv:2608.09683v1 Announce Type: new Abstract: Probabilistic coastal hazard assessments require accurate characterization of tropical cyclone (TC) parameters, yet datasets often contain missing recor

researcharxiv-cs-lg

Deep Multimodal Wearable Sensor Fusion for Detection of Body-Focused Repetitive Behaviors

arXiv:2608.09830v1 Announce Type: new Abstract: Body-focused repetitive behaviors, such as hair pulling and skin picking, are compulsive motor actions commonly associated with obsessive-compulsive and

researcharxiv-cs-lg

Deep probabilistic logic programming for diagnostic reasoning from incomplete information: A case study in stroke detection

arXiv:2608.08561v1 Announce Type: new Abstract: In medical applications, raw data is frequently associated with significant privacy concerns, lending particular importance to the encoding of summary s

applicationsarxiv-cs-ai

Deep Residual Echo State Networks: exploring residual orthogonal connections in untrained Recurrent Neural Networks

arXiv:2508.21172v3 Announce Type: replace-cross Abstract: Echo State Networks (ESNs) are a particular type of untrained Recurrent Neural Networks (RNNs) within the Reservoir Computing (RC) framework,

DeepFreqMark: End-To-End Learnable Frequency-Domain Watermarking with Spherical Attack Simulation for Latent Diffusion Models

arXiv:2608.08999v1 Announce Type: cross Abstract: The proliferation of AI-generated images produced by Latent Diffusion Models (LDMs) has raised critical concerns regarding copyright infringement and

applicationsarxiv-cs-ai

Agents

Defending Retrieval-Augmented Intrusion Detection Against Knowledge Poisoning and Prompt Injection

arXiv:2608.08100v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) enables large language models to classify network flows and generate human-readable incident reports by retrievin

agentsarxiv-cs-ai

Local Ai

Deferred Audio Pruning with Local Audio-Visual Dynamics for Omni-LLMs

arXiv:2608.08794v1 Announce Type: new Abstract: Omni-modal LLMs jointly process audio, video, and text, but long multimodal sequences incur substantial prefill and KV-cache costs. Existing omni-modal

local-aiarxiv-cs-ai

Agents

Defining Decentralization: An Ontological Perspective

arXiv:2608.09748v1 Announce Type: cross Abstract: Decentralization as a concept in computer science has existed for over half a century. Despite its fundamental role across domains such as security, d

agentsarxiv-cs-ai

Tutorials

Degradation-Guided Underwater Image Restoration with Task-Oriented Latent Control

arXiv:2608.08661v1 Announce Type: new Abstract: Degradation information in underwater images plays a dual role: its spatial and spectral cues can guide adaptive restoration, while degradation-entangle

tutorialsarxiv-cs-cv

Degraded Infrared Small Object Detection via Degradation-Adapted Physics-Guided Restoration

arXiv:2608.09311v1 Announce Type: new Abstract: Infrared small object detection has made significant progress in recent years. However, degradations such as fog and nonuniformity can suppress target-b

Demystifying Prediction Powered Inference

arXiv:2601.20819v2 Announce Type: replace-cross Abstract: Machine learning predictions are increasingly used to supplement incomplete or costly-to-measure outcomes in fields such as biomedical researc

safetyarxiv-cs-lg

Dense Point-to-Mask Optimization with Reinforced Point Selection for Crowd Instance Segmentation

arXiv:2604.01742v2 Announce Type: replace Abstract: Crowd instance segmentation is a crucial task with a wide range of applications, including surveillance and transportation. Currently, point labels

safetyarxiv-cs-cv

Deployable Per-Instance Multi-Layer Activation Steering for Large Language Models

arXiv:2608.08829v1 Announce Type: cross Abstract: Activation steering edits the behaviour of a frozen language model by adding a learned vector to its residual stream, and current practice fixes the i

Hardware

Depth-adaptive Inference of Looped Language Models via Continuous Depth Batching

arXiv:2608.09444v1 Announce Type: cross Abstract: A main promise of looped language models (LMs) is depth-adaptive inference. By iterating a block of shared layers a variable number of times, the mode

hardwarearxiv-cs-cl

Depth-Aware Implicit Neural Representation Priors for 3D Gravity Inversion

model-releasesarxiv-cs-ai

arXiv:2608.08959v1 Announce Type: new Abstract: Gravimetry images subsurface density contrasts associated with geological structures, geothermal systems, and intrusive bodies. Recovering a three-dimen

Describe-to-Score: A text-guided framework for image complexity assessment

model-releasesarxiv-cs-cv

arXiv:2509.16609v2 Announce Type: replace Abstract: Accurately assessing image complexity (IC) is essential for many vision tasks, yet existing approaches rely almost exclusively on visual features an

Hardware

Design Space of Self--Consistent Electrostatic Machine Learning Interatomic Potentials

arXiv:2603.14700v2 Announce Type: replace-cross Abstract: Machine learning interatomic potentials (MLIPs) have become widely used tools in atomistic simulations. For much of the history of this field,

hardwarearxiv-cs-lg

Designing for Ethical AI: HCI Feature Considerations to Improve Fairness and User Experience in AutoML use for Human Resources

arXiv:2608.07477v1 Announce Type: cross Abstract: This thesis examines the fairness of Automated Machine Learning (AutoML) tools in human resource hiring systems through the combined lenses of regulat

Detecting Clear Contact Lenses for Iris Recognition: A Two-Stage Mask-Guided Attention Approach

model-releasesarxiv-cs-ai

arXiv:2608.08977v1 Announce Type: cross Abstract: This work focuses on the impact and detection of clear contact lenses in the context of iris recognition. While the detection of cosmetic or patterned

Detection of Self-Introductions in Legislative Testimony

arXiv:2608.07891v1 Announce Type: new Abstract: Self-introductions are common in legislative committee testimonies. Successfully detecting them and extracting the speaker's name is enormously helpful

researcharxiv-cs-cl

Determinization in Structure Theories: A Unified Framework via Closure, Comparability, and Joint Admissibility

arXiv:2608.07476v1 Announce Type: new Abstract: We develop a formal framework for constructing canonical interpretations from plural structure theories. A structure theory is a triple T = ({Sigma}, A,

DevIntent: How Much Does LLM-Generated Code Violate Developer Intent?

model-releasesarxiv-cs-cl

arXiv:2608.07614v1 Announce Type: cross Abstract: Code generated by LLMs can violate a developer's implicit intentions when given an ambiguous prompt, yet standard benchmarks measure only whether code

DH-VLM: Dual-Horizon Cooperative Latent Reasoning for Autonomous Driving

arXiv:2608.09333v1 Announce Type: new Abstract: Large-scale language models for autonomous driving enable enhanced global understanding and long-horizon planning. However, when deployed in isolated ve

safetyarxiv-cs-ro

Diagnosing as Cardiologists Do: ECG Agents with Doctor-Grounded Priors for Clinical Reasoning Across Diseases and Populations

model-releasesarxiv-cs-cv

arXiv:2608.09053v1 Announce Type: cross Abstract: Cardiologists interpret electrocardiograms by localizing waveform components, measuring rhythm and interval patterns, and translating these structured

DialectS2S: End-to-End Speech Dialogue Modeling for Low-Resource Chinese Dialects

arXiv:2608.08067v1 Announce Type: cross Abstract: Current end-to-end speech dialogue models are primarily optimized for mainstream languages and remain limited in low-resource dialect scenarios due to

Tutorials

Did the Grid Erase the Event? EndoClock for Auditing Medical World-Model Pipelines

arXiv:2608.09266v1 Announce Type: new Abstract: Medical world models commonly learn from multimodal recordings synchronized onto a fixed-rate grid. This preprocessing resamples each native stream onto

tutorialsarxiv-cs-cv

Different Feedback, Different Updates: Selective Self-Learning from User Interactions for Large Language Models

arXiv:2608.09109v1 Announce Type: new Abstract: User feedback offers natural supervision for persistent LLM improvement, but a single message may support multiple behavioral changes with different sco

Hardware

Differentiate the Solver, Not the Equation: Reverse-Sweep Adjoints for Block Implicit Simulation

arXiv:2608.08559v1 Announce Type: cross Abstract: Differentiable simulation is a key component in learning, control, and inverse problems, where gradients through nonlinear implicit solvers are requir

hardwarearxiv-cs-lg

DiffSafeMerge: Mitigating Backdoor Inheritance in Diffusion Model Merging

arXiv:2608.09445v1 Announce Type: cross Abstract: Unconditional diffusion checkpoint merging assumes benign sources, yet a compromised public checkpoint can transfer a dormant backdoor while clean gen

Diffuse the object, keep its label: curating detector training data from a few unlabeled photographs via VLM-built 3D vegetation scenes

model-releasesarxiv-cs-cv

arXiv:2608.09691v1 Announce Type: new Abstract: Labeled images of small objects hidden in vegetation are scarce, and detectors trained on them generalize poorly across sites. Rather than reusing label