research

Uncertainty-Aware Trust Estimation for Multi-LLM Systems via Structured Expert Judgement

arXiv:2607.20529v1 Announce Type: cross Abstract: Large Language Model (LLM) ensembles are increasingly used to improve reliability by combining predictions from multiple LLMs. However, existing aggre

Unified Video Dense Prediction from Disjoint Data

DGX agent

arXiv:2607.21592v1 Announce Type: new Abstract: Scene understanding requires simultaneous prediction about geometry, appearance, and semantics. However, existing task-specific annotations are fragment

researcharxiv-cs-cv

24 Jul 2026

Unsupervised Consensus-Based Anomaly Detection for Spatiotemporal Malaria Incidence in Ghana

arXiv:2607.21559v1 Announce Type: new Abstract: A consensus anomaly detection framework was applied to monthly malaria surveillance data from Ghana (2014-2023) to identify atypical transmission patter

Unsupervised Metal Artifact Reduction in Dental CBCT using Fine-tuned Cycle-Consistent Adversarial Networks

arXiv:2607.20977v1 Announce Type: new Abstract: Metal artifacts generated by dental implants significantly degrade cone-beam computed tomography (CBCT) volumes, obscuring critical anatomical structure

VeriSimpl: Robust Optimization Modeling from Natural Language using Simplification-based Verification

arXiv:2607.20474v1 Announce Type: new Abstract: Natural language interfaces can greatly benefit the accessibility and usability of optimization modeling, and recent advances in large language models (

WAT3R: Feedforward Underwater 3D Reconstruction

arXiv:2607.21023v1 Announce Type: new Abstract: Reliable feedforward underwater 3D reconstruction remains challenging due to severe light attenuation and backscattering, which degrade visual quality a

Where Animacy Lives in Large Language Models: Tracing the Circuits of the Animacy Concept

arXiv:2607.20995v1 Announce Type: new Abstract: Distinguishing animate from inanimate concepts in written language requires more than shallow text processing, as it involves recognizing complex select

researcharxiv-cs-cl

Writhe-Based Polymer Link Classification Using Machine Learning

arXiv:2607.20657v1 Announce Type: cross Abstract: Unique and rapid classification of knots and links is an open mathematical problem that is relevant to a range of (bio)physical systems, including pol

A Bayesian Framework for Built-in Input Dimension Reduction for Gaussian Process Modeling

arXiv:2607.19498v1 Announce Type: cross Abstract: Gaussian process (GP) modeling is widely used in computational science and engineering. However, fitting a GP to high-dimensional inputs remains chall

A Confidence Interval for the ell_2 Expected Calibration Error

arXiv:2408.08998v4 Announce Type: replace-cross Abstract: Recent advances in machine learning have significantly improved prediction accuracy in various applications. However, ensuring the calibration

A Deep Learning Framework for Predicting Solar EUV Irradiance During Significant Flares

arXiv:2607.19597v1 Announce Type: cross Abstract: We present FlareEUV, a multimodal deep learning framework for predicting daily extreme ultraviolet (EUV) irradiance at 6.5 nm over three consecutive d

A Splitting Architecture for Exact Reduced Coulomb Friction

arXiv:2607.19599v1 Announce Type: cross Abstract: Existing approaches to frictional contact dynamics typically either modify the Coulomb law to improve numerical robustness or solve the exact law in a

researcharxiv-cs-ro

A Structure-Adaptive Random Feature Method for High-Dimensional Elliptic PDEs

arXiv:2607.19786v1 Announce Type: cross Abstract: Random-feature methods reduce high-dimensional elliptic PDE collocation to linear coefficient problems, but full-dimensional trial spaces overlook low

A Unified Variational Framework for Deep Weakly Supervised Image Segmentation

arXiv:2607.19669v1 Announce Type: new Abstract: We propose a unified variational framework for image segmentation under sparse pixel-level supervision. Our method is based on a simplex-constrained Pot

Adaptive Bayesian Online Learning via Expert Aggregation

arXiv:2607.20239v1 Announce Type: cross Abstract: Bayesian online learning promises uncertainty-aware prediction on data streams, but its performance hinges on inferential choices, including learning

Adaptive deep nonparametric regression from dependent data under covariate shift

arXiv:2607.20309v1 Announce Type: cross Abstract: Covariate shift often occurs because, in many real applications, the source and the target observations may be generated from different distributions.

Adaptive Multi-Expert Graph Transformer for Interpretable EEG-Based Diagnostics

arXiv:2607.19429v1 Announce Type: new Abstract: Electroencephalographic (EEG) abnormalities arise from dynamic changes in neural synchrony across spatial and temporal scales, yet many computational ap

Adaptive Visual Autoregressive Acceleration via Dual-Linkage Entropy Analysis

arXiv:2602.01345v2 Announce Type: replace Abstract: Visual AutoRegressive modeling (VAR) suffers from substantial computational cost due to the massive token count involved. Failing to account for the

AdaRoPE: Not All Attention Heads Should Rotate and Scale Equally

arXiv:2607.19363v1 Announce Type: new Abstract: Rotary Position Embedding (RoPE) is widely adopted in Transformers to encode positional information, yet standard implementations enforce a uniform freq

Adversarial Frontiers: Minimum-Norm Attack Ensembles for Robustness Evaluation

arXiv:2607.19855v1 Announce Type: new Abstract: Adversarial robustness is commonly evaluated with predefined attack ensembles, such as AutoAttack, at a single perturbation budget arepsilon and on a se

All of AI benchmarking at your fingertips

IBM, as part of a global effort, is developing tools to streamline AI benchmarking by making results easier to compare, validate, and reuse. The initiative is detailed in a blog post titled 'All of AI

researchibm-research

An Intelligent-Cloud Edge Multimodal Interaction System for Robots

arXiv:2607.14675v2 Announce Type: replace-cross Abstract: Robust human-robot interaction in complex environments requires accurate gesture perception, semantic scene understanding, and reliable task p

An Isotropy-Preserving Spectral Cap for Muon: Theory and Three Case Studies

arXiv:2607.19771v1 Announce Type: cross Abstract: Muon and related matrix-sign optimizers are increasingly used to pre-train large language models, but their effect on the internal geometry of individ

Analytic Distribution of Classifier-Free Guidance for Schedule Design

arXiv:2607.19725v1 Announce Type: new Abstract: Classifier-free guidance (CFG) is the default mechanism for conditional generation in diffusion models, but the distribution sampled by its deterministi

Anatomy of a Sound Neural Reasoner: One-Shot Amortization, First-Pass Poisoning, and Search Inertness in Clue-Rich Completion

arXiv:2607.19635v1 Announce Type: cross Abstract: Neural solvers are built to deduce, branch, and revise intermediate states. The Lattice Deduction Transformer (LDT) appears to do exactly that. In clu

Approximate Nearest Neighbor Search for Modern AI: A Projection-Augmented Graph Approach

arXiv:2603.06660v2 Announce Type: replace-cross Abstract: Approximate Nearest Neighbor Search (ANNS) is fundamental to modern AI applications. Most existing solutions optimize query efficiency but fai

Are Attributions of Consciousness to AI Chatbots Epistemically Innocent?

arXiv:2607.20001v1 Announce Type: cross Abstract: Artificial intelligence (AI) chatbots (e.g., ChatGPT) can communicate in strikingly humanlike ways. This has prompted many chatbot users to attribute

Associative Emotional Learning in Convolutional Neural Networks

arXiv:2607.19327v2 Announce Type: replace Abstract: Associative emotional learning enables organisms to adaptively link pleasant or unpleasant outcomes to the presence of predictive stimuli. Whereas c

Asymptotically Optimal Regret for Reinforcement Learning without Horizon Dependence

arXiv:2607.19854v1 Announce Type: new Abstract: We study horizon-free regret minimization for finite-horizon time-homogeneous tabular Markov decision processes with S states, A actions, horizon H, and

Attention Without Grounding: Causal Evaluation of Visual Explanations in Medical VLMs

arXiv:2607.18577v1 Announce Type: new Abstract: Attention and saliency heatmaps are widely used to explain medical Vision-Language Model (VLM) outputs on chest X-rays, yet whether they truly highlight

Attributes Should Come from Images, Not Class Names: Distribution-Conditioned Attribute Selection for Vision-Language Models

arXiv:2607.18695v1 Announce Type: new Abstract: A popular route to interpretable zero-shot classification asks a large language model (LLM) to describe each class name and prompts CLIP with the result

Audio-Zero: Label-Free Self-Evolution for Fine-Grained Audio Reasoning

arXiv:2607.20166v1 Announce Type: cross Abstract: Large Audio Language models (LALMs) have made rapid progress on acoustic understanding, yet they still struggle with fine-grained audio reasoning (e.g

AuditVotes: Elevating Provable Defense for GNNs with Efficient Augmentation and Conditional Smoothing

arXiv:2503.22998v3 Announce Type: replace-cross Abstract: Despite advancements in Graph Neural Networks (GNNs), adaptive attacks continue to challenge their robustness. Certified robustness via random

Avoiding Obfuscation with Prover-Estimator Debate

arXiv:2506.13609v2 Announce Type: replace Abstract: Training powerful AI systems to exhibit desired behaviors hinges on the ability to provide accurate human supervision on increasingly complex tasks.

Benchmarking Deep Learning Approaches for AEC Engineering Drawing Layout Detection and Information Extraction

arXiv:2607.18997v1 Announce Type: new Abstract: Information Extraction (IE) from Architecture, Engineering, and Construction (AEC) drawings remains hindered by manual inefficiency, while Layout Detect

Boltzmann-Expected Molecular Design with Decoupled Annealing Flows

arXiv:2607.19519v1 Announce Type: cross Abstract: Most 3D properties relevant to molecular design, including free energies and shape descriptors, are extit{expectations} over the Boltzmann distributio

Bounding Boxes to Improve Small Language Model Performance on Vision-Based Grading Tasks

arXiv:2607.18767v1 Announce Type: new Abstract: The deployment of Small Language Models (SLMs) in educational settings offers significant advantages in terms of privacy, cost, and scalability. However

Breaking the T^{3/4} Barrier for Regret Minimization With Bi-Dimensional CDFs

arXiv:2607.20258v1 Announce Type: new Abstract: We study regret minimization for learning CDF-related objectives of the form [ g(x)dotP_{XsimD}(Xle x), ] over [0,1]^2, where g is a known Lipschitz fun

BRIM: Workload-Balanced Dual-Sided Bit-Serial Sparse Inference Accelerator

arXiv:2607.19431v1 Announce Type: cross Abstract: Bit-serial accelerators exploit bit-level sparsity to reduce DNN inference cost, but existing designs exploit sparsity on only one operand, bounding t

Building Fast, Evaluating Slow: Pipeline Choices Dominate Autointerpretability Score Variance

arXiv:2607.19386v1 Announce Type: cross Abstract: Cross-paper comparison of sparse autoencoder (SAE) interpretability often relies on autointerpretability scores. In this evaluation pipeline, a langua

researcharxiv-cs-cl

CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders

arXiv:2607.18451v1 Announce Type: cross Abstract: Frozen encoders are chosen by how well a lightweight head reads a finding from their features, not whether the geometry separates it. Nearest-neighbor

Causal dictionary learning reveals and validates transcription-factor binding features in genomic language models

arXiv:2607.19618v1 Announce Type: cross Abstract: Genomic language models achieve strong performance across regulatory-genomics tasks, yet what these models internally represent remains opaque, and th

CGMap: A Geospatially Aware Deep Learning Framework for Crop Gap Mapping Using UAV

arXiv:2607.18779v1 Announce Type: new Abstract: In India, crop germination is primarily monitored by visual inspection and manual counting, which are prone to errors, despite their crucial role in det

ChemHyperMag: Physics-informed magnetic hypergraph learning improves molecular ADMET prediction

arXiv:2607.18332v2 Announce Type: replace-cross Abstract: Accurate prediction of ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) is important for drug discovery. Most predictors

ChronoStitch: Training-Free Composition of Visual KV Memories for Long-Horizon Temporal Reasoning

arXiv:2607.19547v1 Announce Type: new Abstract: Long-video question answering requires a model to preserve visual evidence over time without repeatedly reprocessing the same video. A practical approac

CLARK: Closed-loop Learning for Adaptive Reasoning over Knowledge Graphs

arXiv:2607.19996v1 Announce Type: new Abstract: Machine Learning models are widely used for automating classification tasks by extracting statistical patterns from data. However, their performance det

Classical Hardware Acceleration of Quantum Autoencoders for Real-Time Anomaly Detection in Collider Experiments

arXiv:2607.20302v1 Announce Type: new Abstract: Quantum machine learning (QML) algorithms in high energy physics (HEP) can efficiently represent and leverage long-range, high-order correlations in hig

Comparing Model-agnostic Feature Selection Methods through Relative Efficiency

arXiv:2508.14268v2 Announce Type: replace-cross Abstract: Feature selection and importance estimation in a model-agnostic setting is an ongoing challenge of significant interest. Wrapper methods are c