research

A Measurement-Driven Digital Twin Architecture for Plant-Level Biomass Estimation and Growth Forecasting in Hydroponic Systems

arXiv:2606.02796v1 Announce Type: new Abstract: Alternatives to soil-based horticulture, such as hydroponics, have been developed to respond to food distribution concerns for dense urban centers. A ne

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A Quantitative Approximation Framework for Flow Distillation in Diffusion Models

DGX agent

arXiv:2606.03820v1 Announce Type: cross Abstract: We develop a quantitative approximation framework for diffusion distillation, viewing few-step sampling as error propagation under compositions of lea

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3 Jun 2026

A Scoping Review of the Ethical Perspectives on Anthropomorphising Large Language Model-Based Conversational Agents

arXiv:2601.09869v2 Announce Type: replace Abstract: Anthropomorphisation -- the phenomenon whereby non-human entities are ascribed human-like qualities -- has become increasingly salient with the rise

A Sparse Bayesian Learning Algorithm for Estimation of Interaction Kernels in Motsch-Tadmor Model

arXiv:2505.07068v2 Announce Type: replace-cross Abstract: In this paper, we investigate the data-driven identification of asymmetric interaction kernels in the Motsch-Tadmor model based on observed tr

AAD-1: Asymmetric Adversarial Distillation for One-Step Autoregressive Video Generation

arXiv:2606.03972v1 Announce Type: new Abstract: We present AAD-1, an Asymmetric Adversarial Distillation framework for One-step autoregressive image-to-video generation. State-of-the-art methods adopt

Absolute love seeing everyone using Hermes Desktop preview! A ton of updates have already gone in and there's more to come to give yall the …

Nous Research announced the release of Hermes Desktop preview, expressing enthusiasm about user adoption and indicating that multiple updates have already been implemented with additional improvements

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Act Like a Pathologist: Tissue-Aware Whole Slide Image Reasoning

arXiv:2603.00667v3 Announce Type: replace Abstract: Computational pathology has advanced rapidly in recent years, driven by domain-specific image encoders and growing interest in using vision-language

Adapting Noise to Data: Generative Flows from 1D Processes

arXiv:2510.12636v5 Announce Type: replace-cross Abstract: The default Gaussian latent in flow-based generative models poses challenges when learning certain distributions such as heavy-tailed ones. We

AdaWeather: Adaptively Mixing Probabilistic Weather Forecasts with Logarithmic Regret

arXiv:2606.02663v1 Announce Type: cross Abstract: Recent advances in machine learning have produced probabilistic weather forecasting models comparable to state-of-the-art numerical weather predictors

AI Model Extraction Attacks: Bypassing Single-Client Assumptions in Defenses

arXiv:2606.03381v1 Announce Type: cross Abstract: Ensuring the protection of Artificial Intelligence (AI) models deployed in military Command and Control (C2) systems and critical infrastructure is es

AI Rater Discrimination Depends on Scoring Protocol in Complex Clinical Decision-Making

arXiv:2606.03198v1 Announce Type: cross Abstract: Clinical AI evaluation increasingly delegates scoring to large language models (LLMs) acting as AI raters, yet their scoring behavior across evaluatio

AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining

arXiv:2508.13174v2 Announce Type: replace Abstract: Formula alpha mining, which generates predictive signals from financial data, is critical for quantitative investment. Although various algorithmic

An Attention-Based Denoising Model for Diffusion Weighted Imaging

arXiv:2606.03903v1 Announce Type: new Abstract: Diffusion-weighted imaging (DWI) is used for whole-body cancer screening, but it typically requires a long acquisition time. When the scan time is reduc

An Exploration of Collision-based Enemy Morphology Generation

arXiv:2606.02832v1 Announce Type: new Abstract: Despite a great deal of prior research into Procedural Content Generation (PCG), relatively little prior work has explored generating enemies for video

An Improved Method for Personalizing Diffusion Models

arXiv:2407.05312v2 Announce Type: replace Abstract: Diffusion models have demonstrated impressive image generation capabilities. Personalized approaches, such as textual inversion and Dreambooth, enha

Analyzing Stream Collapse in Hyper-Connections: From Diagnosis to Mitigation

arXiv:2606.03483v1 Announce Type: cross Abstract: Hyper-Connections (HC) replace the single Transformer residual stream with multiple streams, introducing a permutation symmetry over stream indices. W

Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension

arXiv:2405.03386v2 Announce Type: replace Abstract: Training with noisy class labels impairs neural networks' generalization performance. In this context, mixup is a popular regularization technique t

Anomalies in Multivariate Time Series Benchmarks Are Mostly Univariate

arXiv:2606.02670v1 Announce Type: cross Abstract: Many recent multivariate time series anomaly detection (MT-SAD) models incorporate cross-channel modeling, under the implicit assumption that the stru

Applying Two-Grid Preconditioner for Subsurface Flow Simulation using Attention-enhanced Hybrid Network to Accelerate Multiscale Discretization in High-contrast Media

arXiv:2606.02582v1 Announce Type: cross Abstract: In this paper, we study the efficient numerical solution of Darcy equations in strongly heterogeneous media with high-contrast permeability and propos

Approximating Probabilistic Inference in Statistical EL with Knowledge Graph Embeddings

arXiv:2407.11821v2 Announce Type: replace Abstract: Statistical information is ubiquitous but drawing valid conclusions from it is prohibitively hard. We explain how knowledge graph embeddings can be

Are Common Substructures Transferable? Riemannian Graph Foundation Model with Neural Vector Bundles

arXiv:2606.03270v1 Announce Type: cross Abstract: Foundation models have sparked a revolution via a pretraining-adaptation paradigm, with recent efforts extending this success to graphs. Unlike other

Are Deep Learning Based Hybrid PDE Solvers Reliable? Why Training Paradigms and Update Strategies Matter

arXiv:2602.06842v2 Announce Type: replace-cross Abstract: Deep learning-based hybrid iterative methods (DL-HIMs) integrate classical numerical solvers with neural operators, utilizing their complement

Attribution via Distributional Paths for Information Revelation

arXiv:2606.03885v1 Announce Type: new Abstract: Feature attribution methods explain predictions by assigning importance scores to input features. Path-based methods such as Integrated Gradients are es

AutoTail-BSFGM: Class-Balance-Aware Fine-Tuning for Chinese Scholarly Text Classification

arXiv:2606.03576v1 Announce Type: new Abstract: Scholarly text classification supports literature organization, subject indexing, and research intelligence, but Chinese scholarly corpora often contain

AvatarMix: Identity-Preserving Cross-Avatar Composition for Outfit Personalization

arXiv:2606.03506v1 Announce Type: new Abstract: Existing 3D avatar outfit transfer methods face distinct challenges: approaches that lift 2D edits to 3D often suffer from outfit or identity quality de

BA-T: An Iterative Transformer for Two-View Bundle Adjustment

arXiv:2606.03287v1 Announce Type: new Abstract: Feed-forward models for 3D reconstruction have achieved strong performance using deep cross-view attention to exchange information across images. Howeve

BAHSD: Bridging the Long-tail Gap via Adaptive Distillation in Black-box Sequential Recommendation

arXiv:2606.03091v1 Announce Type: cross Abstract: Sequential recommendation systems are widely adopted but often deployed as black-box APIs, which has driven recent interest in model extraction to rep

Balancing Symmetry and Efficiency in Graph Flow Matching

arXiv:2602.18084v2 Announce Type: replace Abstract: Equivariance is central to graph generative models, as it ensures the model respects the permutation symmetry of graphs. However, strict equivarianc

BaltiVoice: A Speech Corpus and Fine-tuned Whisper ASR System for the Balti Language

arXiv:2606.03504v1 Announce Type: cross Abstract: We present BaltiVoice, a 16.8-hour read-speech corpus for Balti (ISO 639-3: bft), a Tibetic language spoken in Gilgit-Baltistan, Pakistan, with no pri

BEAST3D: Animal behavioral analysis and neural encoding from multi-view video via Gaussian splatting

arXiv:2606.02937v1 Announce Type: cross Abstract: Multi-view video recordings are increasingly used to capture the 3D movements of animals in experimental settings, yet extracting rich 3D representati

Beyond Compression: Quantifying Spectral Accessibility in Vision Representations

arXiv:2606.03795v1 Announce Type: new Abstract: Vision-language models map visual features into a shared embedding space through learned projection layers, yet it remains unclear how these transformat

Beyond False Stability: High-Noise Drift Gating for Test-Time Adversarial Defenses in Vision-Language Models

arXiv:2606.03730v1 Announce Type: new Abstract: Vision-language models (VLMs) such as CLIP show strong zero-shot generalization but remain highly vulnerable to adversarial attacks. Adversarial trainin

Beyond Gradient Descent: Adam for Analog Ising Machines

arXiv:2606.03917v1 Announce Type: cross Abstract: As Moore's law reaches its limits, Ising machines offer a promising alternative computing approach for difficult optimization problems. However, many

Beyond Semantics: Modeling Factual and Affective Perceptual Experiences from Vision-Language Data

arXiv:2606.03345v1 Announce Type: cross Abstract: We present P-Topics (Perception Topics) modeling, a novel problem for understanding how images are perceived affectively and across cultures. The goal

Beyond Single Solution: Multi-Hypothesis Collaborative Deep Unfolding Network for Image Compressive Sensing

arXiv:2606.03666v1 Announce Type: new Abstract: Recent deep unfolding networks (DUNs) have advanced Compressive Sensing (CS) by effectively integrating iterative optimization with deep learning archit

Beyond the Literal: Decomposing Pragmatic Intent in Multimodal Meme Understanding

arXiv:2606.03604v1 Announce Type: new Abstract: When asked what a meme or sarcastic post means, Large Vision Language Models (LVLMs) tend to describe what the image shows rather than what the author i

Beyond 'To whom it may concern': Tailoring Machine Translation to Audience and Intent

arXiv:2606.03259v1 Announce Type: new Abstract: Translation quality depends on purpose: the same source text demands different translations depending on audience, tone, and communicative intent. Yet M

Bootstrap Your Generator: Unpaired Visual Editing with Flow Matching

arXiv:2606.03911v1 Announce Type: new Abstract: Modern generative models possess a deep understanding of visual content, yet training them for image editing typically requires massive datasets of pair

Bregman meets Levy: Stochastic mirror descent with heavy-tailed noise in continuous and discrete time

arXiv:2606.03769v1 Announce Type: cross Abstract: We study the robustness of stochastic mirror descent (SMD) under heavy-tailed noise, focusing on whether the method retains its convergence guarantees

Bridging Auxiliary Constraints to Resolve Instruction Following in Large Reasoning Models

arXiv:2606.03624v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) have demonstrated impressive capabilities in many tasks, yet they struggle with reliably following multiple instructions,

Building Reliable Long-Form Generation via Hallucination Rejection Sampling

arXiv:2606.03628v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved remarkable progress in open-ended text generation, yet they remain prone to hallucinating incorrect or unsu

Buzz, Choose, Forget: A Meta-Bandit Framework for Bee-Like Decision Making

arXiv:2510.16462v3 Announce Type: replace Abstract: This work introduces MAYA, a sequential imitation learning model based on multi-armed bandits, designed to reproduce and predict individual bees' de

BYORn: Bootstrap Your Own Responses to Defend Large Vision-Language Models Against Backdoor Attacks

arXiv:2606.02947v1 Announce Type: cross Abstract: Supervised fine-tuning is the predominant approach for adapting autoregressive vision-language models to downstream tasks. Recent work has shown that

CAD-to-CT Registration of Cylindrical Objects via Ellipse-Based Axis Estimation

arXiv:2606.02935v1 Announce Type: new Abstract: Accurate registration of CAD models to CT scans is essential for establishing ground truth geometry in volumetric imaging. Obtaining reliable object mas

Can LLM Rerankers Predict Their Own Ranking Performance?

arXiv:2606.03535v1 Announce Type: cross Abstract: Retrieval effectiveness varies substantially across queries, making it important to estimate ranking quality before relevance judgments are available.

Can Local Learning Match Self-Supervised Backpropagation?

arXiv:2601.21683v2 Announce Type: replace Abstract: While end-to-end self-supervised learning with backpropagation (global BP-SSL) has become central for training modern AI systems, theories of local

CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery

arXiv:2606.03602v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the fundamental limitations of purely statistical methods, such as statistical dis

Clustered Self-Assessment: A Simple yet Effective Method for Uncertainty Quantification in Large Language Models

arXiv:2606.03846v1 Announce Type: cross Abstract: Large language models (LLMs) demonstrate remarkable performance across diverse tasks, but they often generate responses that appear plausible while be