Search: “research”

MINT: Multimodal Imaging-to-Speech Knowledge Transfer for Early Alzheimer's Screening

arXiv:2602.23994v2 Announce Type: replace-cross Abstract: Alzheimer's disease is a progressive neurodegenerative disorder in which mild cognitive impairment (MCI) precedes dementia. Structural MRI pro

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Modeling the Developmental Shift in Telicity Acquisition

DGX agent

arXiv:2609.17996v1 Announce Type: new Abstract: Acquiring telicity, which is the distinction between bounded (e.g., ate an apple) and unbounded (e.g., ate apples) events, requires first language (L1)

researcharxiv-cs-cl

17 Sept 2026

Multi-Hop Knowledge Composition is Bound by Pretraining Exposure

arXiv:2606.09338v2 Announce Type: replace Abstract: Large Language Models fail at implicit multi-hop reasoning: a model answers 'When was X born?' and 'Who is Y's closest friend?' correctly but fails

Myovox: Reading Speech from the Muscles of the Face

arXiv:2609.17548v1 Announce Type: new Abstract: Myovox, from myo (muscle) and vox (voice), decodes open-vocabulary English text from 31-channel surface electromyography (sEMG) recorded from the muscle

NeuroECG: ECGFounder-Based Deep ECG Representation for EEG-Free Neurological Prognostication After Cardiac Arrest

arXiv:2609.18891v1 Announce Type: cross Abstract: Neurological prognostication after cardiac arrest commonly relies on electroencephalography (EEG). However, EEG demands high clinical resources. Bedsi

Not All Patches Are Equally Forgettable: Spatially Localized Domain Unlearning in Vision-Language Models

arXiv:2609.17790v1 Announce Type: new Abstract: Pre-trained vision-language models (VLMs) exhibit strong cross-domain recognition performance even without additional training. However, this robustness

Objective vs. Search: Decomposing What Makes a Good Tokeniser

arXiv:2609.19145v1 Announce Type: cross Abstract: Two dominant tokenisation algorithms are used by modern language models: byte-pair encoding (BPE) and UnigramLM. These differ along two orthogonal axe

On Dominant Manifolds in Reservoir Computing Networks

arXiv:2604.05967v2 Announce Type: replace Abstract: Understanding how training shapes the geometry of recurrent network dynamics is a central problem in time-series modeling. We study the emergence of

On-the-Fly Homographies Calibration for Multi-Camera Tracking

arXiv:2609.18582v1 Announce Type: cross Abstract: Precise multi-camera tracking traditionally relies on rigorous 3D site calibration, yet this requirement is often operationally impossible in large-sc

Online Multi-Camera 3D Tracking via ID Prediction over Recurrent Sparse Queries

arXiv:2609.18363v1 Announce Type: new Abstract: Online multi camera 3D tracking must maintain scene global identities across synchronized views, yet query-based trackers carry these identities only im

Online Multimodal Workload Assessment in Contact-Rich Physical Human-Robot Interaction

arXiv:2609.18031v1 Announce Type: new Abstract: Contact-rich physical human--robot interaction (pHRI) imposes time-varying demands associated with physical interaction, motor regulation, and physiolog

researcharxiv-cs-ro

Peak-Aware Short-Term Load Forecasting Across Distribution Grid Aggregation Levels

arXiv:2609.18588v1 Announce Type: new Abstract: For distribution system operators, short-term load forecasting (STLF) supports congestion management, voltage control, and asset protection. Most existi

Physics-based prediction, uncertainty quantification and decision-making for IN718 crystallographic texture intensity across LPBF defocus regimes

arXiv:2609.18863v1 Announce Type: new Abstract: Reliable prediction of crystallographic texture in laser powder bed fusion is critical for linking process conditions with anisotropic response and for

Preservation of Log-Concavity and Convergence of Wasserstein-Fisher-Rao Gradient Flows

arXiv:2609.18118v1 Announce Type: cross Abstract: We study the convergence of Wasserstein-Fisher-Rao (WFR) gradient flows for sampling from probability distributions known up to a normalisation consta

Preventing Model Collapse: A Fisher-Rao Perspective on the Dynamics of Training with Synthetic Data

arXiv:2609.18878v1 Announce Type: new Abstract: Large Language Models (LLMs) are now routinely trained using synthetic data, since high-quality human data has been exhausted by the ever increasing nee

Principled Koopman Representations with Kalman Inference for Efficient Time-Series Prediction

arXiv:2609.17815v1 Announce Type: cross Abstract: The Koopman operator has been widely used for time-series prediction in dynamical systems. However, prior work that learns latent ``Koopman spaces'' u

Prior-Free Competitive Ratios for Improving Bandits: Scale, Curvature and Horizon Are Free, but Not Jointly Under Noise

arXiv:2609.17595v1 Announce Type: new Abstract: In the improving multi-armed bandits problem, each of k arms has an unknown nondecreasing, discretely concave reward curve f_i, and pulling arm i for th

Probabilistic Linear Explanations

arXiv:2609.19077v1 Announce Type: cross Abstract: Formal explainability provides mathematically grounded justifications for individual predictions. However, abductive explanations often exceed human c

ProofVerifier: A Scalable, Diversity-Driven Framework for Natural-Language Proof Verification

arXiv:2602.02377v3 Announce Type: replace Abstract: While large language models (LLMs) have achieved strong performance on mathematical problems with verifiable answers, many advanced problems are pro

Provable Guarantees and Efficient Learning of Structural Equation Models with Latent Confounders

arXiv:2609.18535v1 Announce Type: new Abstract: Causal discovery aims to recover causal relationships from observed data. In various fields, exploring causal relationships among variables remains an i

Provable Guarantees for Spectral Structured Prediction

arXiv:2609.18527v1 Announce Type: new Abstract: Structured prediction is the simultaneous prediction of multiple labels, and is widely used in various fields, such as natural language processing and c

Q-BIOLAT: Binary Latent Protein Fitness Landscapes for QUBO-Based Optimization

arXiv:2603.27526v2 Announce Type: replace Abstract: Protein fitness optimization is a discrete search problem, and the representation used for prediction also determines the neighborhood graph travers

QiT: Quantum-Inspired Transformer for Visual Recognition Task

arXiv:2609.17789v1 Announce Type: cross Abstract: Quantum machine learning offers a compelling representational perspective: angle-encoded states inhabit Hilbert spaces in which periodic similarities

Quanta: A Self-Contained Python Library for Hybrid Retrieval over Quantised Embeddings, Lexical Indexes, and Knowledge Graphs

arXiv:2609.18248v1 Announce Type: cross Abstract: An advanced retrieval-augmented generation pipeline is typically assembled from three or four independently operated systems: an approximate nearest-n

Rank and computation of the pathlifting Jacobian of a DAG ReLU network

arXiv:2609.18682v1 Announce Type: cross Abstract: This paper provides a self-contained proof of the rank of the pathlifting Jacobian of a DAG ReLU network by performing an induction on the network's n

Rapid Loss of the Sierra Nevada's Largest Trees Driven by Fire

arXiv:2609.17925v1 Announce Type: new Abstract: Large trees disproportionately contribute to biomass storage, habitat structure, and ecosystem functioning. However, their distribution and health dynam

Reaching Every Position Without Searching: Rotating Sparse Wiring on the Hypercube as a Substitute for Attention

arXiv:2609.18145v1 Announce Type: new Abstract: Attention pays, at every layer and for every input, the cost of searching for whom to connect. We ask how far one can get with wiring that is fixed, spa

ReDIL-GNN: Resynthesis Domain Incremental Learning for Circuit Graph Neural Networks

arXiv:2609.18595v1 Announce Type: new Abstract: Logic resynthesis preserves circuit functionality while changing gate vocabulary, topology, and structural statistics, creating domain shift for circuit

Regional Explanations via Causal Sufficiency and Necessity

arXiv:2609.18049v1 Announce Type: new Abstract: Model explainability is essential for understanding and trusting machine learning models. Existing explainable AI methods often explain predictions thro

Regularized Least Squares Training of Quadratic Neural Networks with Applications to System Identification

arXiv:2609.17654v1 Announce Type: new Abstract: This paper proposes a least squares approach for the training of quadratic neural networks with regularization. The proposed methodology yields a lower

Relation Before Entity: Deferred Commitment in Language Model Factual Recall

arXiv:2609.17537v1 Announce Type: new Abstract: We ask whether relation-type information (e.g., capital-of) and entity-specific information (e.g., France to Paris) become causally active at the final-

Relationally Guided Use Case Modeling with LLMs

arXiv:2609.18291v1 Announce Type: cross Abstract: Use case flows are important elements of use case modeling because they support downstream software engineering activities, including requirements ana

Reliable learning in challenging environments

arXiv:2304.03370v3 Announce Type: replace Abstract: The problem of designing learners that provide guarantees that their predictions are provably correct is of increasing importance in machine learnin

Remembering Solomon Marcus

arXiv:2609.18224v1 Announce Type: cross Abstract: From the manifest of Andre Breton, through the transdisciplinary understanding, we arrive at a post-modern manifest. A talk by Laura De Marco (Harvard

Renyi Tracking Bounds for Langevin Dynamics with Moving Targets

arXiv:2609.17577v1 Announce Type: cross Abstract: We study Langevin diffusion and Langevin Monte Carlo (LMC) when the target distribution changes over time. Under a log-Sobolev inequality (LSI), we de

REPAIR: Resolving Long-Tail Confusion in Scientific Retrievers via Fact-Verified Iterative Refinement

arXiv:2609.18262v1 Announce Type: new Abstract: Precise retrieval of scientific information is fundamentally constrained by long-tailed concepts and high fact-sensitivity of scientific corpora. These

REQAP: Resilient Weight Packing and Quantization for Edge DNN Acceleration

arXiv:2609.17555v1 Announce Type: cross Abstract: Efficient deployment of Deep Neural Networks (DNNs) on edge accelerators requires aggressive model compression while maintaining reliability in fault-

Rethinking How We Evaluate Methodological Progress in Health AI

arXiv:2609.18134v1 Announce Type: cross Abstract: Methodological progress in artificial intelligence (AI) for electronic health records (EHRs) depends on our ability to determine which algorithms work

RLLBC-Lib: An Educational Code Library for Reinforcement Learning and Learning-Based Control

arXiv:2609.19074v1 Announce Type: cross Abstract: Reinforcement learning (RL) is an exciting concept as well as a remarkable success story worth sharing. However, RL builds on rather complex interacti

Robust and Efficient AI Frameworks for Scalable Material Design and Property Prediction

arXiv:2609.17646v1 Announce Type: cross Abstract: This thesis develops robust and efficient AI frameworks for accelerating crystalline materials discovery by addressing both major stages of the materi

SAM-on-the-Curve: Sharpness-Aware Mode Connectivity for Robust Weight-Space Interpolation

arXiv:2609.17748v1 Announce Type: new Abstract: Deep neural networks that are independently trained to similar performance can be connected by low-loss parametric curves in weight space, a phenomenon

Scalable Music Cover Retrieval Using Lyrics-Aligned Audio Embeddings

arXiv:2601.11262v2 Announce Type: replace-cross Abstract: Music Cover Retrieval, also known as Version Identification, aims to recognize distinct renditions of the same underlying musical work, a task

Securing quantum error correction against misleading advice from AI agents

arXiv:2609.19090v1 Announce Type: cross Abstract: Can an attacker turn influence over an artificial intelligence (AI) adviser into a harmful quantum error-correction update? We identify an ambiguity i

Selective Prediction and Uncertainty-Aware Referral for Pap Smear Classification

arXiv:2609.17545v1 Announce Type: new Abstract: Deep learning models for cervical cytology are almost always evaluated as if every prediction must be acted upon, yet a screening system deployed alongs

Semantic CSI Feedback for Beam Selection: When Task-Aware Embeddings from Sparse Pilots Outperform Full-Bandwidth Reconstruction

arXiv:2609.18368v1 Announce Type: cross Abstract: Classical CSI feedback in FDD massive MIMO transmits a compressed reconstruction of the channel, optimizing fidelity to the original signal regardless

Sharp margin-based generalization bounds for realizable SVM

arXiv:2609.17845v1 Announce Type: cross Abstract: Let the exact homogeneous hard-margin support vector machine be trained on (m) independent observations from a Borel probability law on a real Hilbert

Similarity Pairing with Energy Mover's Distance for Self-Supervised Pre-Training at the LHC

arXiv:2609.17738v1 Announce Type: cross Abstract: Many self-supervised methods for training foundation models at the Large Hadron Collider (LHC) rely on data augmentations to encourage the model to em

Simultaneous Arrival Control for Distributed Multi-Robot Systems with Curvature and Constant-Speed Constraints

arXiv:2607.14781v2 Announce Type: replace Abstract: The simultaneous arrival of multiple mobile robots at a target point is crucial for cooperation tasks such as cooperative encirclement, disaster rel

researcharxiv-cs-ro