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arXiv:2608.23922v1 Announce Type: new Abstract: Data mixing is a central design problem in large language model pretraining: given a fixed token budget, practitioners must decide how much data to allo

applicationsarxiv-cs-ai

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Model Releases

Data Predictability Shapes Weibull Weight-Scale Growth in Transformer Training

DGX agent

arXiv:2608.23573v1 Announce Type: new Abstract: A trained transformer's weight magnitudes can be summarized by a two-parameter Weibull distribution whose shape k approx 1.2 is stable across layers and

model-releasesarxiv-cs-lg

26 Aug 2026

DDMS: Discriminative Distillation of Multi-view Foundational Features into Single-view Models

arXiv:2608.23850v1 Announce Type: new Abstract: Foundational visual features such as DINO have played a critical role across modern computer vision, and have recently become key components in multi-vi

local-aiarxiv-cs-cv

Decoupling candidate dual AGN from chance superpositions in the GOTHIC survey via a deep-learning framework

arXiv:2608.24164v1 Announce Type: cross Abstract: Dual active galactic nuclei (DAGN) mark a critical phase in the evolution of merging galaxies and the pairing of supermassive black holes, yet they re

Hardware

Deep Feature Pyramid Convolutional Networks with In-Place Activated Batch Normalization for Automated Skin Lesion Boundary Segmentation

arXiv:1812.00877v2 Announce Type: replace Abstract: Segmentation of skin lesion boundaries in dermoscopic imaging is an important prerequisite step for computer-aided diagnosis of malignant melanoma,

hardwarearxiv-cs-cv

Deep Learning Super Resolution for Satellite Cloud Mask Downscaling

arXiv:2608.24715v1 Announce Type: cross Abstract: A vast amount of optical satellite data is being transmitted to Earth-based servers every day, and more than half of this data is affected by haze or

Delayed Optimizer-State Transport Shapes Short-Horizon Training Decisions

arXiv:2608.24593v1 Announce Type: new Abstract: Adaptive optimizers retain gradient history in moment variables, allowing a local change in loss weighting to alter later updates. We examine whether th

local-aiarxiv-cs-lg

Design and Evaluation of a Compliant Quasi Direct Drive End-effector for Safe Robotic Ultrasound Imaging

arXiv:2410.03086v2 Announce Type: replace Abstract: Robot-assisted ultrasound scanning promises to advance autonomous and accessible medical imaging. However, ensuring patient safety and compliant hum

safetyarxiv-cs-ro

Design-to-Plan: A Large Language Model-Based Multi-Agent Framework for Manufacturing Process Planning from 3D CAD Models and 2D Engineering Drawings

arXiv:2608.24039v1 Announce Type: cross Abstract: Manufacturing process planning transforms heterogeneous design information into coherent manufacturing decisions. However, existing approaches focus o

Differential Learning for Robust Prediction of Thermal Stability with Application to Energetic Materials

arXiv:2608.23874v1 Announce Type: cross Abstract: Predicting thermal stability during handling and storage is essential for the design of safe and reliable energetic materials. However, experimental m

safetyarxiv-cs-lg

Dimensionless Controls of Plasticity Under Alternating Tasks: From Evolutionary Biology to Continual Learning

arXiv:2608.23889v1 Announce Type: cross Abstract: Plasticity under changing environments is central to both evolutionary biology and continual learning. Motivated by recent work on genotype--phenotype

researcharxiv-cs-lg

Agents

Discovering Adaptive Transmission Programs for Collective Innovation

arXiv:2608.24545v1 Announce Type: new Abstract: Human collective intelligence depends on transmission processes: who shares what with whom, how, and when. While these processes emerge from individual

agentsarxiv-cs-ai

Discovering Cross-Language Reasoning Invariance in LLMs with Geometry-Invariant Sparse Autoencoders

arXiv:2608.23809v1 Announce Type: cross Abstract: Multilingual language models can solve the same mathematical problem in different languages, but it remains unclear whether they rely on shared featur

Disentangled Skill Representations for Predictive Human Modeling

arXiv:2608.23776v1 Announce Type: cross Abstract: Understanding human skill is important for AI systems that collaborate with, coach, or assist people. Unlike typical latent variable estimation proble

Diverse by Reasoning: Harnessing the Wisdom of LLM Crowds for Future Prediction

arXiv:2608.24001v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for future prediction, motivating the use of multiple models as a wisdom-of-the-crowd mechanism. Howe

Do Joint Language-Audio Embeddings Encode Perceptual Timbre Semantics?

arXiv:2510.14249v2 Announce Type: replace-cross Abstract: Understanding and modeling the relationship between language and sound are essential for applications such as music information retrieval, tex

safetyarxiv-cs-ai

Tutorials

Do LLMs Understand Limit Order Book Dynamics?

arXiv:2608.23706v1 Announce Type: new Abstract: A large language model (LLM) trained on synthetic limit order book (LOB) data achieves near perfect scores in generating valid sequences of LOB events.

tutorialsarxiv-cs-ai

Do physics-informed neural networks (PINNs) need to be deep? Shallow PINNs using the Levenberg-Marquardt algorithm

arXiv:2602.08515v3 Announce Type: replace-cross Abstract: This work investigates shallow physics-informed neural networks (PINNs) for solving forward and inverse problems governed by nonlinear partial

researcharxiv-cs-lg

Do Recipes Have Personas? Characterizing and Generating Creator Style in Attributed Procedural Graphs

arXiv:2608.24369v1 Announce Type: new Abstract: While large language models (LLMs) possess vast zero-shot procedural knowledge, their tendency to produce homogenized logic often obscures the unique, i

local-aiarxiv-cs-ai

Do Robotic World Models Really Follow Actions? Diagnosing and Aligning Action-Conditioned Generation for Policy Learning

arXiv:2608.24885v1 Announce Type: cross Abstract: Action-conditioned world models are increasingly used as learned simulators for policy evaluation and improvement, yet their effectiveness rests on an

safetyarxiv-cs-cv

Agents

Don't Just Listen, Try Planning: Graph-based Retrieval-Generation Agent for Long-form Audio Meeting Understanding

arXiv:2608.24048v1 Announce Type: cross Abstract: While long-form audio meeting understanding (LAMU) is garnering growing attention, task-specific question answering (QA) datasets remain scarce. Exist

agentsarxiv-cs-ai

DoublesEval: Diagnosing Multi-Agent Tactical Reasoning in Vision-Language Models via Professional Doubles Badminton

model-releasesarxiv-cs-cv

arXiv:2608.24439v1 Announce Type: new Abstract: Visual Language Models (VLMs) excel at describing visible scene content but struggle to reason about dynamic multi-agent interactions, where action sema

DreamLedger: Execution-Settled Credit Files for World-Model Imagination in Robot Decision Loops

arXiv:2608.23863v1 Announce Type: new Abstract: Robots are beginning to act on world-model predictions, yet reliability is still expressed through instantaneous, model-internal signals. DreamLedger in

researcharxiv-cs-ro

DriftAD: Visually-Guided Text Drift for Few-Shot Industrial Anomaly Detection

arXiv:2608.23723v1 Announce Type: new Abstract: Few-shot anomaly detection (FSAD) has recently benefited from vision-language models such as CLIP, which enable anomaly de?tection by aligning visual fe

DRRG: A Discrete Diffusion Framework for Radiology Report Generation

arXiv:2608.24105v1 Announce Type: new Abstract: Purpose: Automatic radiology report generation (RRG) has been widely explored to improve reporting accuracy and reduce radiologists' workload. Most exis

Dynamical System-Based Imitation Learning and Neuroadaptive Control for Trajectory Recovery in Autonomous Ships

arXiv:2608.23924v1 Announce Type: cross Abstract: Repetitive maritime operations can be effectively learned using the Imitation Learning (IL) paradigm, which transfers human expertise directly to Unma

safetyarxiv-cs-ro

E2HiL: Entropy-Guided Sample Selection for Efficient Real-World Human-in-the-Loop Reinforcement Learning

arXiv:2601.19969v2 Announce Type: replace-cross Abstract: Human-in-the-loop guidance has emerged as an effective approach for accelerating online reinforcement learning (RL) in real-world manipulation

safetyarxiv-cs-lg

Eating for a Sustainable Planet: Personalized Sustainable Diet Recommendation via Constraint-Aware Decision-Making Modeling

arXiv:2608.24274v1 Announce Type: new Abstract: A sustainable diet represents a multi-dimensional synergy among four essential pillars: nutrition adequacy, economic affordability, cultural acceptabili

safetyarxiv-cs-ai

Effective Learning Rate Governs Loss Dynamics in Language Model Pretraining

model-releasesarxiv-cs-lg

arXiv:2608.24814v1 Announce Type: new Abstract: We uncover ELR collapse in language model pretraining: learning rate (LR) and parameter norm govern loss dynamics primarily through their ratio, the eff

Agents

EgoErrorVQA: Assess Egocentric Comprehension Capabilities through Procedural Errors for Ego-Agentic AI

arXiv:2608.24134v1 Announce Type: new Abstract: The majority of our everyday activities are procedural and consist of sequences of interdependent steps. However, existing benchmarks for Visual Agents

agentsarxiv-cs-cv

Hardware

Elastic KV Cache for LLM Serving:A Working Reclamation Mechanism, and Why Chunked Prefill Already Closes the Gap

arXiv:2608.23658v1 Announce Type: cross Abstract: An LLM serving engine sizes its key-value (KV) cache once, at startup, permanently setting aside a reserve for the worst-case prefill activation. Duri

hardwarearxiv-cs-ai

Eluna: An Agentic LLM System for Automating Warehouse Operations with Reasoning and Task Execution

arXiv:2607.08960v2 Announce Type: replace-cross Abstract: Warehouse operations are governed by Standard Operating Procedures (SOPs) that encode complex, multi-system decision logic, which must be exec

EMFE: A lightweight, explainable machine learning framework for malaria cell classification

arXiv:2608.24793v1 Announce Type: new Abstract: Automated malaria diagnosis from stained blood-smear microscopy is dominated by deep convolutional neural networks that are accurate but computationally

EmoTra-TTS: Smooth Intra-Utterance Emotion Transitions for Speech Synthesis

arXiv:2608.23791v1 Announce Type: cross Abstract: Psychological research on emotion dynamics has established that human affect is a continuous, evolving process: emotions rise, decay, and transition w

EMRB: A Multi-Level Benchmark for Evaluating LLM Reasoning over Raw Electromagnetic Signals

arXiv:2608.24086v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as code agents for scientific and engineering analysis, but their ability to analyze raw physical-lay

Energy-Aware Performance Evaluation of Nonlinear Mechatronic Systems Under Matched-Tracking Conditions

arXiv:2608.23578v1 Announce Type: cross Abstract: Trajectory-tracking metrics such as root-mean-square error (RMSE), overshoot, and settling time are widely used to evaluate control performance in mec

researcharxiv-cs-ro

Applications

Enhancing Bayesian Optimization and Active Learning Through Kernel Diversity

arXiv:2608.24721v1 Announce Type: cross Abstract: Hyperparameter selection remains a key challenge in Bayesian optimization (BO) and Bayesian active learning (AL), as model misspecification can lead t

applicationsarxiv-cs-ai

Ensemble of Convolutional Neural Networks for StrokePrediction: Towards Improved Diagnostic Accuracy

arXiv:2608.24771v1 Announce Type: cross Abstract: Brain stroke, known for its high mortality and incidence rates, poses significant health risks and requires rapid intervention for survival. Early dia

Equivariant Cellular Sheaves for Molecular Electronic Structure: Bridging Sheaf Cohomology and E(3)-Equivariant Hamiltonian Learning

arXiv:2608.23571v1 Announce Type: new Abstract: Equivariant message-passing networks are the standard model for molecular property and interatomic-potential prediction, and recent work predicts the el

local-aiarxiv-cs-lg

Equivariant Covariance Tensors: Guaranteed SPD Uncertainty for Tensor-Valued Geometric Learning

arXiv:2608.24386v1 Announce Type: cross Abstract: Tensor-valued prediction is fundamental to geometric deep learning, yet uncertainty quantification (UQ) for such outputs remains an open challenge. Wh

ESQ-Bench: A Multi-Tier Enterprise Oracle Benchmark for Evaluating NL2SQL Dialect Generalization and Silent Semantic Divergence

arXiv:2608.23569v1 Announce Type: new Abstract: State-of-the-art Natural Language to SQL (NL2SQL) models report execution accuracy exceeding 89 percent on established benchmarks such as Spider and BIR

EstLLM: Enhancing Estonian Capabilities in Multilingual LLMs via Continued Pretraining and Post-Training

arXiv:2603.02041v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are predominantly trained on English-centric data, resulting in uneven performance for smaller languages. We stud

Ethical LLM-Assisted Research: A Framework for Responsible Delegation, Verification, and Epistemic Value

arXiv:2608.23644v1 Announce Type: new Abstract: Large language models (LLMs) are becoming routine instruments of scientific research, assisting with literature synthesis, hypothesis development, codin

Evaluating Deep Multivariate Imputation Models on Wearable Device Data

arXiv:2608.24436v1 Announce Type: cross Abstract: Wearable device data enables continuous health monitoring, but suffers from structured missingness: features sharing a physical sensor drop out togeth

Evaluating Language Models on Cross-Language Code Functional Equivalence

arXiv:2608.23961v1 Announce Type: cross Abstract: Background: Large Language Models (LLMs) have demonstrated strong performance across a variety of code-understanding tasks, leading many to believe th

Evaluating Multiple LLM Generations with Validated Task Coverage

arXiv:2608.24228v1 Announce Type: new Abstract: Many LLM applications are most useful when they provide several candidate outputs for comparison, validation, or combination. Predominant evaluation set

Event-Based Motion Estimation via Oriented Distance Fields

model-releasesarxiv-cs-cv

arXiv:2608.24223v1 Announce Type: new Abstract: Event-based motion estimation is central to tasks that demand high temporal resolution and robustness to fast motion. Existing methods typically rely on

Every Layer Counts: An Exponential L_2 Depth Hierarchy for ReLU Networks

arXiv:2608.23877v1 Announce Type: new Abstract: We prove a depth hierarchy for ReLU neural networks in which every additional ReLU layer can save exponentially many neurons. For every ellgeq 3, a glob

researcharxiv-cs-lg