Search: “research”

Hybrid Probabilistic Zonotopes for Identifiable and Refinable Predictive Uncertainty

arXiv:2608.05454v1 Announce Type: new Abstract: Probabilistic prediction heads in neural networks typically output either a Gaussian mixture or a single conformal region. Neither separates the distinc

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HyTBE: Hyperbolic Target-Background Expert Model for Cross-Domain Infrared Small Target Detection

DGX agent

arXiv:2608.05771v1 Announce Type: cross Abstract: Infrared small target detection (IRSTD) has achieved substantial progress under domain-consistent evaluation, yet detector performance often degrades

researcharxiv-cs-ai

7 Aug 2026

I concur, and the point is broader than just for Biology. Science does not advance by 'pure thinking' alone. We need to do experiments, and …

I concur, and the point is broader than just for Biology. Science does not advance by 'pure thinking' alone. We need to do experiments, and interpret the results of those experiments, which in turn ra

researchyann-lecun--x

Integrating Human Linguistic Insights into AI: Theory-Driven Representation for Multilingual Text-to-Speech

arXiv:2204.07228v2 Announce Type: replace Abstract: This paper explores the integration of human linguistic insights into multilingual text-to-speech (TTS) systems by evaluating the Featurally Undersp

Invisible Shortcuts: Why Vision Encoders Know Your Camera

arXiv:2608.05424v1 Announce Type: new Abstract: Deep vision models exploit shortcuts, relying on cues that correlate with supervision signals. Prior work has focused on visible biases, such as object-

Is Self-Pretraining really useful to improve diagnosis in medical Time Series?

arXiv:2608.06122v1 Announce Type: cross Abstract: Inspired by recent evidence that transformer architectures benefit from Self-PreTraining (SPT) on long-context benchmarks, we investigate whether simi

KVAE: Family of Tokenizers for Multimodal Generative Models

arXiv:2608.05798v1 Announce Type: new Abstract: Latent diffusion modeling (LDM), a prominent paradigm, utilizes tokenizers to map input signal to compressed representation. This dependency positions t

LC-Implicit-QAOA: Active-Workspace-Capped Exact Objective-and-Gradient Evaluation for Training over Bounded QUBO Light Cones

arXiv:2608.05610v1 Announce Type: cross Abstract: QAOA training repeatedly queries an objective and all shared gradients, making exact evaluation a feasibility bottleneck even when QUBO terms have bou

Learning from Failures: Retrieval-Centric CoT via Hard Negatives for Unified Multimodal Retrieval

arXiv:2608.06060v1 Announce Type: new Abstract: Unified multimodal retrieval aims to identify candidates that satisfy complex user intent expressed through heterogeneous inputs. Although Large Vision-

LELA: an LLM-based Entity Linking Approach with Zero-Shot Domain Adaptation

arXiv:2601.05192v2 Announce Type: replace Abstract: Entity linking (mapping ambiguous mentions in text to entities in a knowledge base) is a foundational step in tasks such as knowledge graph construc

LILAC: An Idempotent Neural Speech Codec

arXiv:2608.05727v1 Announce Type: cross Abstract: Neural Audio Codecs are widely adopted in speech generation and editing. However, existing neural audio codecs are not idempotent: across the paper's

Mapping Armenian Paris: Extracting and Geocoding Commercial Advertisements from the 20th-Century Diaspora Press

arXiv:2608.05911v1 Announce Type: new Abstract: This paper presents an end-to-end, IIIF-based pipeline that turns the digitised Armenian press of France into an interactive map of the 20th-century Par

ML-for-ML

arXiv:2608.06046v1 Announce Type: cross Abstract: AI training workloads are growing rapidly, making their time, energy, and infrastructure costs increasingly important. In shared cloud clusters, train

MOSAIK: Multi-Patch Content-Aware Spatial Allocation of Image Tokens for Efficient Generation

arXiv:2608.05450v1 Announce Type: new Abstract: Pixel-space diffusion models avoid the reconstruction ceiling of latent diffusion models by generating directly in image space. However, their substanti

Multi-Year Geospatial Reasoning using Interannually-Consistent Historical Predictions as a Free Input Modality

arXiv:2608.05979v1 Announce Type: new Abstract: Machine learning, and deep networks in particular, are increasingly used to derive higher-level Earth observation (EO) products such as annual land-cove

Multivariate Time Series Forecasting needs Cross Variable Loss

arXiv:2608.05742v1 Announce Type: cross Abstract: Multivariate time series forecasting presents unique challenges because future variables often co-evolve under shared system dynamics. While existing

Muon on the Stiefel Manifold Admits an Exact Closed-Form Update

arXiv:2608.06218v1 Announce Type: cross Abstract: We study Muon, a recently proposed matrix-aware optimization method, in the context of the Stiefel manifold. This manifold consists of matrices with o

Near-sensor Computing for Rapid Visuotactile Perception

arXiv:2608.05725v1 Announce Type: new Abstract: Visuotactile sensors reconstruct dense contact geometry from measured surface gradients, but host-based processing increases power consumption and intro

researcharxiv-cs-ro

On Same-Sample and Independent-Sample Stochastic Extragradient for Monotone Variational Inequalities

arXiv:2608.06182v1 Announce Type: cross Abstract: We study stochastic extragradient (SEG) methods for solving monotone variational inequality problems (VIPs) over a feasible set. Although extragradien

On the Anisotropy of Score-Based Generative Models

arXiv:2510.22899v2 Announce Type: replace Abstract: We investigate the role of network architecture in shaping the inductive biases of modern score-based generative models. To this end, we introduce t

Once a Response, Always a Response: Detecting LLM-generated Text via Latent Prompt Restoration

arXiv:2608.05741v1 Announce Type: cross Abstract: Large language models (LLMs) can generate fluent and convincing text at scale, creating growing risks for misinformation dissemination, educational mi

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization

arXiv:2608.05240v1 Announce Type: cross Abstract: One-bit post-training quantization represents each weight using only its sign, requiring all deployment contexts to share the same binary weight matri

One Ranking, Any Budget: Matryoshka Evidence-to-Context Frame Selection for Long-Video Understanding

arXiv:2608.05707v1 Announce Type: new Abstract: Frame selection is essential for applying Large Multimodal Models (LMMs) to long videos due to severe frame redundancy and limited context windows. Sinc

Online Reasoning Calibration: Test-Time Training Enables Generalizable Conformal LLM Reasoning

arXiv:2604.01170v2 Announce Type: replace-cross Abstract: While test-time scaling has enabled large language models to solve highly difficult tasks, state-of-the-art results come at exorbitant compute

Optimal Rates for Learning with Monotone Adversaries

arXiv:2608.06337v1 Announce Type: cross Abstract: A monotone adversary observes an i.i.d. labeled sample and appends a finite number of further examples of its choice, every one of them labeled correc

OTLesMix: Wasserstein Barycenter and Optimal Transport Map for Synthetic Lesion Generation with Diverse Shapes and Locations

arXiv:2608.06264v1 Announce Type: new Abstract: The development of deep learning over the past decade has revolutionized medical imaging segmentation, allowing the extraction of precise descriptors fr

Patient Pose Assessment Using a CT-Based Framework for Synthetic Data Generation

arXiv:2608.06126v1 Announce Type: new Abstract: An adequate diagnostic quality of radiographs is essential for reliable diagnoses and treatment planning. The patient's pose during radiography is one o

Perturbation Sensitivity at Convergence: A Simple Signal for Identifying Spuriously Correlated Samples

arXiv:2608.05419v1 Announce Type: cross Abstract: Models trained by empirical risk minimization on data containing spurious correlations achieve high average accuracy while failing on subpopulations w

Physics-Based Molecular Fingerprints from Spectral Graph Theory Provide Efficient Geometry-Aware Measures of Chemical Similarity

arXiv:2608.05336v1 Announce Type: cross Abstract: Molecular representations are essential for the evaluation of molecular similarity and the development of structure-property relationships. Despite th

Pixel-TTS: Image based Text Rendering for Robust Text-to-Speech

arXiv:2606.14750v2 Announce Type: replace-cross Abstract: Recent advances in pixel-based text modeling show that representing text as images enables models to exploit visual cues for language understa

Posture and Sustainment Optimization Under Adversarial Uncertainty

arXiv:2608.05256v1 Announce Type: new Abstract: Pre-commitment posture, the assignment of military assets to theater locations before conflict scenarios resolve, is a critical and formally unsolved pr

Potential Matching Optimal Transport: Continuous Normalizing Flows for Exact p-Wasserstein Dynamics

arXiv:2608.05666v1 Announce Type: new Abstract: We introduce Potential Matching Optimal Transport (PMOT), a potential-flow framework for general p-cost optimal transport with c_p(x,y)=|x-y|^p. PMOT pa

Predicting Social Media User Actions: A Hybrid Approach for Common and Rare Behavior Prediction on Bluesky

arXiv:2511.17241v2 Announce Type: replace Abstract: Understanding and predicting user behavior on social media platforms is crucial for content recommendation and platform design. While existing appro

ProDVI: Programmatic Dynamics Priors for Value Network Initialization

arXiv:2608.06015v1 Announce Type: cross Abstract: Deep Reinforcement Learning (RL) is notoriously sample inefficient. One contributing factor is that RL agents are typically initialized from scratch,

PromptForSegCXR: Prompt-Driven Multi-Organ and Multi-Disease Segmentation in Chest X-rays using a Multi-stage Fusion Mechanism

arXiv:2507.00673v2 Announce Type: replace-cross Abstract: Image segmentation is central to automated medical image analysis, enabling precise identification of anatomical structures and pathological r

Provably Efficient Self-Calibrating Quantum Fault Tolerance

arXiv:2608.05686v1 Announce Type: cross Abstract: Quantum error correction protects logical information only when every physical operation remains below the fault-tolerance threshold, a condition that

QEvict: Recoverable Quantized KV Eviction for Attention-Drift-Robust Long-Context Decoding

arXiv:2608.05326v1 Announce Type: cross Abstract: Autoregressive large language model inference is increasingly constrained by the memory footprint of the Key-Value (KV) cache. A dominant line of work

Quality Diversity for Reliable Data Driven Time-Use Optimization

arXiv:2608.05230v1 Announce Type: cross Abstract: The daily allocation of the finite 24-hour time budget is strongly associated with physical, mental, and cognitive health. While predictive models can

Reasoning Errors Have a Region and a Direction in the Residual-Stream Trajectory of LLMs

arXiv:2608.05660v1 Announce Type: cross Abstract: As language models are increasingly used for tasks that require verifiable reasoning, reliably distinguishing sound reasoning from flawed reasoning ha

Reducing belief in conspiracy theories as they unfold using large language models

arXiv:2608.06151v1 Announce Type: cross Abstract: The emergence of conspiracy theories in the wake of major events is a significant societal challenge. Here we test whether conversational dialogues wi

Refining Over Resampling: Test-Time Self-Correction for LLM Reasoning

arXiv:2608.05643v1 Announce Type: new Abstract: Test-time scaling improves LLM reasoning by using additional inference compute, but wider sampling alone can suffer from diminishing returns: new rollou

Relay, Don't Route: Adaptive Population Handoff for Cost-Efficient LLM-Driven Evolution

arXiv:2608.05651v1 Announce Type: cross Abstract: Large language model (LLM)-driven evolution has shown promise for program search and algorithm discovery, but relying on strong models throughout long

Respect Your Zero-Shot Uncertainty: Conservative Calibration for Test-Time-Adapted Vision-Language Models

arXiv:2608.05945v1 Announce Type: new Abstract: Test-time adaptation (TTA) can improve the recognition accuracy of vision-language models under distribution shift, but often degrades calibration, maki

RIG-RoPE: Relation- and Instance-Gated Rotary Positional Encoding with Duration-Aware Temporal Coordinates

arXiv:2608.05154v1 Announce Type: new Abstract: Rotary positional encoding (RoPE) is a core component of modern language models and has been extended to multimodal LLMs through multidimensional varian

RRC: Unlocking Generative Reward Models in LLM Reinforcement Learning via Ranking-Based Reward Construction

arXiv:2608.06310v1 Announce Type: cross Abstract: Recent advances in reward modeling show a paradigm shift from discriminative reward models to generative reward models. However, despite their strong

SciQNet: Two-Stage Multimodal Adaptation for Scientific Image Quality Assessment

arXiv:2608.05691v1 Announce Type: new Abstract: Scientific images are essential for communicating experimental observations, quantitative evidence and conceptual knowledge. Unlike natural images, thei

Shape-Aware Oriented Bounding Box (OBB) to Horizontal Bounding Box (HBB) Conversion

arXiv:2608.05858v1 Announce Type: new Abstract: Accurate object detection in aerial and satellite imagery is dependent upon the bounding box representation. This is especially true for spatially orien

Shapes from Examples: Foundations of Shape Learning in Recursive SHACL

arXiv:2607.27934v2 Announce Type: replace Abstract: SHACL shapes enable data graph validation, making automatic shape learning essential for knowledge graph applications. We investigate the well-known