Search: “index”

MLQENABLER: Enabling Secure Machine Learning Queries over Encrypted Database in Cloud Computing

arXiv:2607.08197v1 Announce Type: cross Abstract: In cloud computing, the public cloud service providers (CSPs) can provide cloud storage as the primary service while providing additional machine lear

10 Jul 2026

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Predicting Pseudo-nitzschia harmful algal blooms along the Portuguese Coast using satellite-derived predictors

DGX agent

arXiv:2607.07834v1 Announce Type: new Abstract: Pseudo-nitzschia diatoms pose recurrent risks to coastal ecosystems and shellfish harvesting along the Portuguese Atlantic coast. Here we develop and ev

applicationsarxiv-cs-lg

10 Jul 2026

ToDMA: Large Model-Driven Massive Token Communications for Semantic Multiple Access

arXiv:2505.10946v3 Announce Type: replace-cross Abstract: Token communications (TokenCom) is an emerging generative semantic communication paradigm, where tokens serve as compact representation units

10 Jul 2026

Cross-Trajectory Chimera Interventions Reveal Dissociable Roles of Weight Magnitude and Direction in Grokking

arXiv:2607.06628v1 Announce Type: cross Abstract: Which properties of a partially trained network are causally portable to a different, independently trained network? Single-trajectory interventions s

Fractal KV-Cache Archives: Lossless Symbolic Storage with In-Place Retrieval for Long-Context LLM Inference

arXiv:2607.07144v1 Announce Type: new Abstract: The key-value (KV) cache dominates the memory cost of long-context autoregressive inference, and a growing body of work compresses it through quantizati

Framing Instability in LLM Ethical Stance: Auditing Negation Sensitivity in Moral Dilemmas

arXiv:2601.21433v2 Announce Type: replace Abstract: Language models are increasingly consulted on ethically consequential questions, yet the stance a model expresses may not survive a change in framin

Strategies for Span Labeling with Large Language Models

arXiv:2601.16946v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used for text analysis tasks, such as named entity recognition or error detection. Unlike encoder-base

researcharxiv-cs-cl

T^{3}S: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series

arXiv:2506.12885v4 Announce Type: replace Abstract: Crop type classification from optical satellite time series remains limited in its ability to generalize across growing seasons, particularly when c

Towards a Pseudo-Labeling Workflow for Celltype-Classification from Explanted Brain Slice Recordings

arXiv:2607.06569v1 Announce Type: cross Abstract: This paper proposes an unsupervised workflow to pseudo-label extracellular spikes from human brain slice MEA recordings into two putative cell types:

A VLM-Enhanced Framework for Comprehensive Traffic Sign Condition Assessment Integrating Daytime Visual Performance and Nighttime Retroreflectivity Evaluation

arXiv:2607.06478v1 Announce Type: new Abstract: Traffic signs are crucial components of road safety, serving as visual tools under all lighting conditions. The Manual on Uniform Traffic Control Device

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks

arXiv:2607.05489v1 Announce Type: cross Abstract: The AInstein architecture introduced an unsupervised neural method for solving the Riemannian Einstein equations on arbitrary manifolds. This Physics

Catalyst Papers in Artificial Intelligence Research: A Landscape on ICLR from 2017 to 2025

arXiv:2607.05401v1 Announce Type: cross Abstract: A small number of methodological contributions, including word2vec, the Transformer, large-scale pre-training, and reinforcement learning from human f

Applications

Drift Happens: An Empirical Study of Neural Architecture Robustness to Temporal Distribution Shift

arXiv:2607.05908v1 Announce Type: new Abstract: Real-world data distributions evolve over time, inducing temporal distribution shift that can substantially degrade the reliability of deployed machine

applicationsarxiv-cs-lg

Empirical Minimal-Realisation Compression of Deep Neural Networks via Controllability-Observability Tests

arXiv:2607.05457v1 Announce Type: cross Abstract: Deep neural networks often contain substantial hidden-state redundancy, but most compression methods operate directly on weights, neurons, or quantise

From RGB Generation to Dense Field Readout: Pixel-Space Dense Prediction with Text-to-Image Models

arXiv:2607.06553v1 Announce Type: new Abstract: Large-scale text-to-image models are attractive backbones for dense prediction because RGB generation pretraining learns rich semantic, structural, and

researcharxiv-cs-cv

GraphBU: MILP Instance Generation with Graph-Native Block Units

arXiv:2607.06532v1 Announce Type: new Abstract: Mixed-integer linear programming (MILP) instances used for solver development are hard to obtain when models come from private or application-specific p

Inject or Navigate? Token-Efficient Retrieval for LLM Analysis of Transactional Legal Documents

model-releasesarxiv-cs-cl

arXiv:2607.05764v1 Announce Type: new Abstract: Answering questions over a set of transactional legal documents is most simply done by injecting the whole corpus into the LLM's context window on every

Leech Lattice Vector Quantization for Efficient LLM Compression

arXiv:2603.11021v2 Announce Type: replace Abstract: Scalar quantization of large language models (LLMs) is fundamentally limited by information-theoretic bounds. While vector quantization (VQ) overcom

Platonic Representations for Poverty Mapping: Unified Vision-Language Codes or Agent-Induced Novelty?

arXiv:2508.01109v3 Announce Type: replace Abstract: We investigate whether socioeconomic indicators, like household wealth, leave recoverable informational imprints in both satellite imagery (capturin

AIFS-SUBS: Extending Data-Driven Forecasting to Sub-Seasonal Timescales

arXiv:2607.05100v1 Announce Type: cross Abstract: Data-driven models now rival numerical weather prediction in the medium range, but extending them to sub-seasonal lead times raises challenges absent

Attention Dynamics in Diffusion Models: A Visual Analytics Framework for Human-AI Collaboration

arXiv:2607.02563v1 Announce Type: cross Abstract: Diffusion-based text-to-image models can synthesize complex and highly structured visual content, yet the emergence and evolution of semantic structur

BoRP: Bootstrapped Regression Probing for Scalable and Human-Aligned LLM Evaluation

arXiv:2601.18253v2 Announce Type: replace-cross Abstract: Accurate evaluation of user satisfaction is critical for iterative development of conversational AI. However, for open-ended assistants, tradi

Brand-as-Memory: Vision-Language Models Encode Causal, Mechanistically Localizable Credibility Priors for News Sources

arXiv:2607.03365v1 Announce Type: cross Abstract: Vision-language models (VLMs) increasingly read news and web content as images, where the publisher's identity is visually present. We show that VLMs

Dataset Distillation Efficiently Encodes Low-Dimensional Representations from Gradient-Based Learning of Non-Linear Tasks

arXiv:2603.14830v3 Announce Type: replace Abstract: Dataset distillation, a training-aware data compression technique, has recently attracted increasing attention as an effective tool for mitigating c

Deep Learning-Based Characterization of Detonation-Cell Size Distributions in Soot-Foil Records

arXiv:2607.03764v1 Announce Type: cross Abstract: The geometric size and regularity of detonation cells are key physical parameters for characterizing detonation waves. Traditional manual measurement

Agents

Do GUI Agents Believe Their Eyes? Diagnosing State-Belief Reliance on Pixels versus Structure

arXiv:2607.04334v1 Announce Type: new Abstract: Multimodal GUI agents read an interface through two redundant channels: the rendered pixels of a screenshot and a serialized structure such as a DOM or

agentsarxiv-cs-ai

Entropy-Coded MS-VQ-VAE with Learned Priors for Ultra-Low Bitrate Video Compression

arXiv:2607.02562v1 Announce Type: new Abstract: Learned video codecs based on continuous latent representations struggle to operate reliably below 0.1 bits per pixel~(bpp): without a differentiable ra

researcharxiv-cs-cv

Environmental Drivers of Respiratory Disease: A District Level Analysis

arXiv:2607.04416v1 Announce Type: new Abstract: Sri Lanka has experienced a decade of progressive forest degradation and rising atmospheric pollution, yet district-level respiratory admissions have pa

Local Ai

Exploring Convolutional Neural Processes for Weather Downscaling

arXiv:2607.04190v1 Announce Type: new Abstract: Global reanalysis products such as ERA5-Land provide spatially complete weather fields but at resolutions too coarse for local applications, particularl

local-aiarxiv-cs-lg

From Regulation to Requirements: An Automated Requirement Derivation and Explanation Pipeline

arXiv:2607.04448v1 Announce Type: cross Abstract: Ensuring software compliance with regulations such as the General Data Protection Regulation (GDPR) and the Artificial Intelligence Act (EU AI Act) po

Fully Rotation-Equivariant Spectral-Spatial Learning for Multispectral Object Detection

arXiv:2607.05148v1 Announce Type: new Abstract: Existing multispectral detectors are limited by discrete spectral processing, a scale-dependent shift in the relative reliability of spectral and spatia

Hardware

Generative wave propagator

arXiv:2607.04440v1 Announce Type: cross Abstract: Seismic wavefield simulation is fundamental to seismology, but conventional finite-difference (FD) methods remain limited by numerical dispersion and

hardwarearxiv-cs-ai

LangLoc: 'Tell Me What You See'

arXiv:2607.05077v1 Announce Type: new Abstract: We tackle fine-grained indoor localization from natural language: given a free-form description of one's surroundings, estimate the observer's 2D positi

Applications

Machine Learning for Depression Screening and Intervention: an Original Circadian Rhythm Score-based Methodology

arXiv:2607.04648v1 Announce Type: cross Abstract: Depression screening from large-scale behavioral data is challenged by fragmented circadian indicators, limited interpretability, and the lack of inte

applicationsarxiv-cs-ai

MACS: Measurement-Aware Consistency Sampling for Inverse Problems

arXiv:2510.02208v3 Announce Type: replace-cross Abstract: Diffusion models have emerged as powerful generative priors for solving inverse imaging problems. However, their practical deployment is hinde

researcharxiv-cs-cv

Memory-Efficient FastText: A Comprehensive Approach Using Double-Array Trie Structures and Mark-Compact Memory Management

model-releasesarxiv-cs-cl

arXiv:2506.01254v2 Announce Type: replace Abstract: FastText remains a practical choice for industrial word representation because it can synthesize vectors for out-of-vocabulary words from character

Taste-aware music retrieval from audio embeddings

model-releasesarxiv-cs-lg

arXiv:2607.03296v1 Announce Type: cross Abstract: Crossmodal correspondences between sound and taste are well established in psychology and neuroscience, but largely absent from content-based multimed

The Good, the Bad, and the Brittle: Benchmarking Robustness and Generalisation of Histopathology Foundation Models

arXiv:2607.04401v1 Announce Type: new Abstract: How robust and generalisable are pathology foundation models and have their scaling limites been reached? We benchmarked twelve pathology foundation mod

TrendFact: A Benchmark Towards Hotspot Perception in Automatic Fact-Checking

model-releasesarxiv-cs-cl

arXiv:2410.15135v5 Announce Type: replace Abstract: With the surge of online misinformation, Large Language Models (LLMs) and Reasoning Large Language Models (RLMs) serving as Automatic Fact-Checking

IonSense-QKG: A Quantum-Readiness Metadata Framework for Lithium-Ion Battery Dataset Discovery

model-releasesarxiv-cs-lg

arXiv:2607.01286v1 Announce Type: new Abstract: Public lithium-ion battery datasets are increasingly used for state-of-health estimation, remaining-useful-life prediction, anomaly detection, electroch

Memora: A Harmonic Memory Representation Balancing Abstraction and Specificity

arXiv:2602.03315v2 Announce Type: replace Abstract: Agent memory systems must accommodate continuously growing information while supporting efficient, context-aware retrieval for downstream tasks. Abs

MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning

arXiv:2506.09105v3 Announce Type: replace-cross Abstract: We present MetaTT, a Tensor Train (TT) adapter framework for fine-tuning of pre-trained transformers. MetaTT enables flexible and parameter-ef

Psychological Imagination Networks Show Cross-Population Centrality and Clustering Alignment in Humans That Large Language Models Fail to Replicate

arXiv:2510.04391v5 Announce Type: replace Abstract: Mental imagery vividness is a stable individual trait, yet whether imagined scenarios share relational structure across human and synthetic large la

WBMM: Windowed Batch Matrix Multiplication for Efficient Large Receptive Field Convolution

arXiv:2607.02097v1 Announce Type: cross Abstract: Large kernel depthwise convolutions achieve strong performance but suffer from significant degradation as kernel size grows due to irregular memory ac

Amortized Maximum Inner Product Search with Learned Support Functions

model-releasesarxiv-cs-lg

arXiv:2603.08001v3 Announce Type: replace Abstract: Maximum inner product search (MIPS) is a crucial subroutine in machine learning, requiring the identification of a vector taken within a database (t

How Environment and Urbanization Shape Bird Diversity in Sri Lanka

arXiv:2607.00582v1 Announce Type: cross Abstract: This study presents a comprehensive analysis of bird diversity across Sri Lanka by integrating spatial, temporal, and environmental data. Bird observa

Agents

Knowdit: Agentic Smart Contract Vulnerability Detection with Auditing Knowledge Summarization

arXiv:2603.26270v2 Announce Type: replace-cross Abstract: Smart contracts govern billions of dollars in decentralized finance (DeFi), yet automated vulnerability detection remains challenging because

agentsarxiv-cs-ai

OmniMoE: An Efficient MoE by Orchestrating Atomic Experts at Scale

arXiv:2602.05711v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) architectures are evolving towards finer granularity to improve parameter efficiency. However, existing MoE designs f