Search: “research”

Truncated Step-Level Sampling with Process Rewards for Retrieval-Augmented Reasoning

arXiv:2602.23440v4 Announce Type: replace Abstract: Reinforcement learning has emerged as an effective paradigm for training large language models to interleave reasoning with search engine calls. How

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Tubular Neighbourhoods of Pfaffian Sets and Applications to Neural Networks

DGX agent

arXiv:2607.08370v1 Announce Type: cross Abstract: We derive bounds for the volume of tubular neighbourhoods of smooth Pfaffian hypersurfaces, generalising known results for algebraic varieties. The bo

researcharxiv-cs-lg

10 Jul 2026

TypeProbe: Recovering Type Representations from Hidden States of Pre-trained Code Models

arXiv:2607.08339v1 Announce Type: cross Abstract: State-of-the-art code models achieve impressive performance, yet the extent to which they internally encode type information remains poorly understood

Understanding Layer Patching in Model Size Interpolation

arXiv:2607.08170v1 Announce Type: new Abstract: Zero-shot model size interpolation aims to create new models of intermediate target sizes by combining existing models without additional training. Rece

Unit-Independent Low-Rate Wrist GSR Processing for Stress Detection Using Phasic nSCR Features

arXiv:2607.08007v1 Announce Type: cross Abstract: Galvanic skin response (GSR) is widely used for stress detection, but wrist-based GSR remains challenging because its absolute amplitude can differ su

Unlocking Temporal Generalization in Hamiltonian Video Dynamics Models

arXiv:2607.07763v1 Announce Type: new Abstract: World models are typically trained to predict discrete-time physical dynamics with a fixed step size baked into the model weights, preventing prediction

Unveiling Public Opinion: A Study of Sentiment Analysis Using LSTM and Traditional Models

arXiv:2607.07772v1 Announce Type: new Abstract: In this age of social media, sites like Twitter have become meeting places for people to share their views and feelings on a wide range of issues and cu

UtterTune: LoRA-Based Target-Language Pronunciation Edit and Control in Multilingual Text-to-Speech

arXiv:2508.09767v3 Announce Type: replace-cross Abstract: We propose UtterTune, a lightweight method for adapting a multilingual text-to-speech (TTS) system built on a large language model (LLM). It i

Vanilla SGD with Momentum Survives Heavy-Tailed Noise: Convergence Analysis without Gradient Clipping or Normalization

arXiv:2607.08104v1 Announce Type: new Abstract: Stochastic gradient descent (SGD) is a cornerstone of modern optimization. While its performance under heavy-tailed noise is often addressed through spe

VEGAS: Human-Aligned Video Caption Evaluation via Gaze

arXiv:2607.08489v1 Announce Type: cross Abstract: Vision-language models excel at video captioning, yet typically generate descriptions that fail to capture individual viewers' attention. We propose V

Vision-Language Memory for Spatial Reasoning

arXiv:2511.20644v2 Announce Type: replace Abstract: Spatial reasoning is a critical capability for intelligent robots, yet current vision-language models (VLMs) still fall short of human-level perform

VocaDet: Sample-Driven Open-Vocabulary Object Detection and Segmentation via Visual Tokenization and Vector Database Retrieval

arXiv:2607.08541v1 Announce Type: cross Abstract: Open-vocabulary object detection and segmentation aim to recognize arbitrary objects beyond predefined categories. Although recent vision-language and

Wat3R: Underwater 3D Geometry Learning without Annotations

arXiv:2607.08772v1 Announce Type: new Abstract: Estimating 3D geometry in underwater environments presents unique challenges due to light attenuation, scattering, and the absence of large-scale, high-

Whareformer: Learning to Track What is Where in Long Egocentric Videos

arXiv:2607.08537v1 Announce Type: new Abstract: The recently established 'Out of Sight, Not out of Mind' (OSNOM) task for egocentric videos focuses on tracking objects that are moved by the camera wea

What LLM Forecasters Know but Don't Say: Probing Internal Representations for Calibration and Faithfulness

arXiv:2607.08046v1 Announce Type: cross Abstract: Large language models fine-tuned for forecasting can be accurate yet poorly calibrated, and their chain-of-thought (CoT) reasoning may not faithfully

When Debiasing Backfires: Counterintuitive Side Effects of Preprocessing-Based Stereotype Mitigation

arXiv:2607.07937v1 Announce Type: new Abstract: Preprocessing-based methods for stereotype mitigation, such as pre-/post-training on debiased corpora, are widely used in NLP. While these approaches re

When Implausible Tokens Get Reinforced: Tail-Aware Credit Calibration for LLM Reinforcement Learning

arXiv:2607.07976v1 Announce Type: cross Abstract: Reinforcement learning (RL) has achieved remarkable success in enhancing the reasoning capabilities of large language models (LLMs). However, widely u

X-ACTA: eXtended Analytic Center Tension distribution Algorithm for fixed and mobile cable-driven-parallel-robot

arXiv:2607.08265v1 Announce Type: new Abstract: Steering Cable-Driven Parallel Robots (CDPRs) beyond their Wrench-Feasible Workspace (WFW) augments their capabilities in challenging scenarios such as

researcharxiv-cs-ro

ZipDepth: Bringing Lightweight Zero-Shot Monocular Depth Anywhere, on Any Device

arXiv:2607.08771v1 Announce Type: new Abstract: Monocular depth estimation has seen remarkable progress through foundation models achieving robust zero-shot generalization, yet their computational dem

A Good Initialization is All You Need for Faithful Visual Attribution

arXiv:2607.06726v1 Announce Type: new Abstract: Faithful visual attribution identifies which image regions support a model prediction. Search-based perturbation methods lead the insertion--deletion fa

A Multi-Analyst LLM Pipeline for Auditable Rule Discovery Across 68 Public Physiological Corpora

arXiv:2607.06802v1 Announce Type: cross Abstract: Open physiological corpora are heterogeneous: they use different sensors, labels, sampling rates, recording settings, and clinical endpoints. They can

A Unified Detection Framework for AI-Related Content and Artifacts

arXiv:2607.07527v1 Announce Type: cross Abstract: Artificial intelligence (AI) is a double-edged sword: while it has achieved remarkable success across a wide range of domains, its deployment also cal

A Word-Level Digital Reader of the Prasthanatrayi with Sankara's Bhasya: Corpus, Method, and an Open, Offline Reading Aid for the Advaita Vedanta Canon

arXiv:2607.07282v1 Announce Type: new Abstract: The Prasthanatrayi -- the ten principal Upanisads, the Brahmasutra, and the Bhagavadgita, with Sankara's commentaries (bhasya) -- is the foundational co

Accurate, Interdisciplinary and Transparent Structure-property Understanding with Deep Native Structural Reasoning

arXiv:2607.07708v1 Announce Type: cross Abstract: Structure-property relationships are foundational to biology, chemistry and materials science, where function, reactivity and physical response emerge

AirPASS: Over-the-Air Federated Learning via Pinching Antenna Systems

arXiv:2607.06768v1 Announce Type: cross Abstract: This paper investigates over-the-air federated learning (AirFL) in wireless systems where the access point is equipped with a multi-waveguide pinching

Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks

arXiv:2603.24705v3 Announce Type: replace-cross Abstract: Discrete choice models are fundamental tools in management science, economics, and marketing for understanding and predicting decision-making.

An Hybrid Quantum-Classical Diffusion Model for Image Generation

arXiv:2607.07072v1 Announce Type: new Abstract: Quantum diffusion models provide a physics-consistent route to generative learning by formulating noising and denoising directly on quantum states. Howe

Any-Dimensional Learning by Sampling

arXiv:2607.07680v1 Announce Type: cross Abstract: Many machine learning models are defined for inputs of different sizes, such as point clouds containing different numbers of points, sequences of toke

Approximate full conformal prediction in an RKHS

arXiv:2601.13102v3 Announce Type: replace-cross Abstract: Full conformal prediction is a framework that implicitly formulates distribution-free confidence prediction regions for a wide range of estima

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions

arXiv:2607.07611v1 Announce Type: new Abstract: Background: Graph neural networks improve computational prediction of polypharmacy side effects, but standard binary cross-entropy training allocates eq

AT-Attn: Temporal-Aware Cross-Attention for Longitudinal Multimodal Alzheimer's Disease Diagnosis

arXiv:2607.07091v1 Announce Type: cross Abstract: In longitudinal Alzheimer's disease (AD) diagnosis support, clinical and imaging information is often collected at irregular visits. Integrating these

Attention in Geometry: Scalable Spatial Modeling via Adaptive Density Fields and FAISS-Accelerated Kernels

arXiv:2601.06135v3 Announce Type: replace-cross Abstract: Spatial computation in geographic systems increasingly requires query-conditioned, local, interpretable aggregation under metric constraints.

Audio Sentiment Analysis via Distillation and Cross-Modal Integration of Generated Multilingual Transcripts

arXiv:2607.06611v1 Announce Type: cross Abstract: Automatically recognizing the sentiment, positive or negative, from speech is a challenging task, requiring both the analysis of vocal inflections and

Best-Arm Identification with Generative Proxy

arXiv:2607.06879v1 Announce Type: new Abstract: Best-arm identification is a canonical model for data-driven decision-making, but in many applications each reward observation is costly. Motivated by t

Bi-PT: Bidirectional Cross-Attention Point Transformers for Four-Chamber Heart Reconstruction from Sparse Cardiac MRI Data

arXiv:2607.06923v1 Announce Type: new Abstract: We propose Bi-PT, a pipeline for reconstructing 3D four-chamber human heart meshes from clinical sparsely sampled cardiac magnetic resonance imaging (CM

Billions of Sketches Reveal Hidden Cultural Variation in Human Concepts

arXiv:2607.07267v1 Announce Type: cross Abstract: Claims about the universality of human concepts have been predominantly assessed through linguistic similarity across languages and cultures. However,

Bound to Disagree: Generalization Bounds via Certifiable Surrogates

arXiv:2602.23128v2 Announce Type: replace Abstract: Generalization bounds for deep learning models are typically vacuous, not computable or restricted to specific model classes. In this paper, we tack

BUS: Brain-Inspired Unsupervised Self-Reflection for Advanced Multimodal Reasoning

arXiv:2607.07361v1 Announce Type: new Abstract: Current Vision-Language Models (VLMs) often struggle to handle complex visual tasks that require consistent and fine-grained reasoning. Recent methods a

ButterflyMoE: Compression-Scalable Ternary Experts via Structured Butterfly Orbits

arXiv:2601.13563v5 Announce Type: replace-cross Abstract: In current Mixture of Experts (MoE) architectures, linear memory scaling is present, the memory grows as the number of experts increases. N in

CarbonCLIP: Enhance Carbon Prediction from Satellite Imagery via Integrated Street-View Semantics and Temporal Context Training

arXiv:2607.07292v1 Announce Type: cross Abstract: Accurately estimating urban carbon emissions is critical for sustainable urban planning, yet many existing approaches remain difficult to apply consis

Cardiac MRI Through-Plane Super-Resolution Guided by Reference and Memory

arXiv:2607.07581v1 Announce Type: new Abstract: Clinical cardiac MRI is commonly acquired with high in-plane resolution but coarse through-plane resolution to reduce scan time and accommodate breath-h

CEVAR: Centerline Embedding Extraction for Endovascular Aneurysm Repair

arXiv:2606.15667v2 Announce Type: replace Abstract: Long-term mortality rates after endovascular aneurysm repair (EVAR) remain elevated due to post-EVAR rupture caused by loss of seal in stent graft s

CoFINN: Conservation Flux Informed Neural Networks for Physics Problems Governed by Conservation Laws

arXiv:2607.06587v1 Announce Type: new Abstract: We present CoFINN (Conservation Flux Informed Neural Networks), a physics-informed deep learning framework for predicting compressible flow fields gover

Collaborative Synthetic Data Generation for Knowledge Transfer in Federated Learning

arXiv:2607.07565v1 Announce Type: cross Abstract: One-shot federated learning (OSFL) addresses the communication overhead of federated learning by limiting training to a single round, but doing so wit

ColorFM: An Optimization-to-Learning Framework for Color Transfer via Flow Matching

arXiv:2607.07119v1 Announce Type: new Abstract: Color transfer aims to align the color distribution of a source image with that of a reference image while preserving structural and semantic consistenc

Compass: Prostate Cancer Detection Needs Multi-View Context

arXiv:2607.06919v1 Announce Type: new Abstract: Artificial intelligence (AI) analysis of micro-ultrasound (muUS) has shown promise for prostate cancer (PCa) detection. However, most existing AI method

Comprehensive Evaluation of Large Language Model Responses: A Multi-Factor Scoring System

arXiv:2607.06940v1 Announce Type: cross Abstract: The remarkable performance of large language models (LLMs) in linguistic tasks underscores an urgent need for comprehensive evaluation of their respon

Computing with Stochastic Oracles in AI-Augmented Computation

arXiv:2607.06893v1 Announce Type: cross Abstract: The Stochastic-Oracle Turing Machine (SOTM) framework models AI-augmented computation as the interaction of a probabilistic Turing machine with an ora