Search: “research”

The Grammar Does the Work: Functional vs. Lexical Dependency Length Minimization Across Universal Dependencies

arXiv:2607.01899v1 Announce Type: new Abstract: Dependency length minimization (DLM) is a well-documented processing universal, but previous studies report a single mean dependency distance (MDD) per

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The Moving Eye: Enhancing VLA Spatial Generalization via Hybrid Dynamic Data Collection

DGX agent

arXiv:2607.02322v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown remarkable promise in generalized robotic manipulation. However, their spatial generalization remains fra

researcharxiv-cs-ro

3 Jul 2026

The risk of KV cache compression

arXiv:2607.01520v1 Announce Type: new Abstract: Transformer inference on long sequences is expensive because softmax attention repeatedly reads from a large KV cache. The prevalent approach to this bo

ThreadWeaver: Adaptive Threading for Efficient Parallel Reasoning in Language Models

arXiv:2512.07843v2 Announce Type: replace-cross Abstract: Scaling inference-time computation has enabled Large Language Models (LLMs) to achieve strong reasoning performance, but their inherently sequ

Towards Cellular-Scale Interpretability in Pathology Foundation Models for Biomarker Assessment

arXiv:2511.05150v2 Announce Type: replace-cross Abstract: Molecular biomarker testing in pathology is often costly and tissue-consuming, limiting scalable clinical deployment. Artificial intelligence

Uncertain but Useful: Leveraging CNN Training Variability into Data Augmentation

arXiv:2509.05238v2 Announce Type: replace-cross Abstract: Deep learning (DL) has transformed neuroimaging by delivering state-of-the-art performance with reduced computation times. Yet, the numerical

UniSE: A Unified Framework for Decoder-Only Autoregressive LM-Based Speech Enhancement

arXiv:2510.20441v2 Announce Type: replace-cross Abstract: Neural audio codecs have largely promoted the application of language models (LMs) for speech applications. However, the effectiveness of auto

Unlocking Speech-Text Compositional Powers: Instruction-Following Speech Language Models without Instruction Tuning

arXiv:2607.02214v1 Announce Type: new Abstract: Instruction tuning for speech language models (SLMs) is substantially more challenging than for text-based large language models (LLMs), as it requires

Unveiling the Non-Monotonic Effect of Privacy on Generalization under Byzantine Robustness

arXiv:2607.01492v1 Announce Type: new Abstract: Recent work has established a fundamental trilemma between Byzantine robustness, local differential privacy (LDP), and optimization error in distributed

Using embeddings to predict spoken word duration and pitch in Mandarin monosyllabic words

arXiv:2607.02002v1 Announce Type: new Abstract: Time-normalized f0 contours of Mandarin words in conversational speech have been shown to be predictable in part from their contextualized embeddings (C

VaSST: Variational Inference for Symbolic Regression using Soft Symbolic Trees

arXiv:2602.23561v2 Announce Type: replace-cross Abstract: Symbolic regression (SR) has gained recent traction in AI-driven scientific discovery for learning closed-form physical laws. Yet existing met

Verifiable Knowledge Expansion through Retrieval-Grounded Formal Concept Analysis

arXiv:2607.01773v1 Announce Type: new Abstract: Ontology construction requires deciding which objects, attributes, and structural relations should be accepted as valid knowledge. Language models can p

World Wide Models: Literary Tools for Cultural AI

arXiv:2607.02369v1 Announce Type: cross Abstract: LLMs stage a new form of cultural encounter that is massive, automated, and monolingual. Literary disciplines have always negotiated cultural struggle

X-LogSMask: Expand Transformer for Graph-Structured Data

arXiv:2607.01553v1 Announce Type: cross Abstract: Transformers have become general-purpose architectures, but their all-to-all self-attention is poorly matched to graph data, whose interactions are sp

ZENITH: Automated Gradient Norm Informed Stochastic Optimization

arXiv:2601.15212v2 Announce Type: replace Abstract: Training deep computer vision models requires manual oversight or hyperparameter tuning of the learning rate (LR) schedule. While existing adaptive

Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis

arXiv:2607.01918v1 Announce Type: new Abstract: We present Zeus, a unified tuning-free Time Series Foundation Model (TSFM) that delivers superior performance across diverse analysis tasks without any

3D Point World Models: Point Completion Enables More Accurate Dynamics Learning

arXiv:2607.00148v1 Announce Type: cross Abstract: Learning predictive models of the world enables robotic control through planning, potentially allowing robots to improvise solutions on new tasks. How

A Geometric Perspective on Composable Emotion Steering in Text-to-Speech Models

arXiv:2607.00946v1 Announce Type: cross Abstract: While prior work has explored emotion control in hybrid text-to-speech systems, the geometric properties of these modules, and their implications for

A Methodology for Investigating AI Patterns Prevalence in Software Repositories

arXiv:2607.00558v1 Announce Type: cross Abstract: As Artificial Intelligence(AI)-based applications take off, a clear understanding of AI patterns can uplift the quality of AI applications. Many AI pa

Active View Selection with Perturbed Gaussian Ensemble for Tomographic Reconstruction

arXiv:2603.06852v2 Announce Type: replace Abstract: Sparse-view computed tomography (CT) is critical for reducing radiation exposure to patients. Recent advances in radiative 3D Gaussian Splatting (3D

AdaBoosting Text Prompts for Vision-Language Models

arXiv:2607.00684v1 Announce Type: new Abstract: The classification accuracy of pretrained Vision-Language Models (VLMs) relies on the quality of the text prompts. Handcrafted templates and Large Langu

Adaptive Perturbation Selection for Contrastive Audio Decoding

arXiv:2607.00247v1 Announce Type: cross Abstract: Large audio-language models (LALMs) frequently hallucinate by overriding acoustic evidence with language priors. While contrastive decoding (CD) offer

AEGIS: A Multi-Task Joint-Embedding Predictive Architecture for Mammography

arXiv:2607.00277v1 Announce Type: new Abstract: We present Aegis, a joint-embedding predictive architecture for breast cancer detection and density assessment in mammography. We train three Vision Tra

Affogato: Open-Vocabulary Affordance Grounding with Automated Data Generation at Scale

arXiv:2506.12009v2 Announce Type: replace Abstract: Affordance grounding aims to localize where to interact with an object, a fundamental capability for embodied agents. Yet progress is bottlenecked b

Aionoscope: Debugging Latent-State Accessibility in Time-Series Representations

arXiv:2607.00956v1 Announce Type: cross Abstract: Time-series models are often evaluated by what they can forecast or classify, but those scores do not show whether their representations preserve the

ALEE: Any-Language Evaluation of Embeddings via English-Centric Minimal Pairs

arXiv:2607.00171v1 Announce Type: new Abstract: Text embeddings are standard for semantic similarity tasks, yet their evaluation remains an open challenge. Current benchmarks are static, cover only a

AVSR-Diff: Scale-Agnostic Diffusion Priors for Temporally Consistent Arbitrary-Scale Video Super-Resolution

arXiv:2607.00987v1 Announce Type: new Abstract: Diffusion models have significantly advanced video super-resolution (VSR) but remain largely constrained to fixed upsampling scales. Conversely, while c

Balancing Expressivity and Learnability in Quantum Kernel Bandit Optimization

arXiv:2607.01080v1 Announce Type: new Abstract: We investigate Gaussian process (GP) bandit optimization with quantum kernels, assuming the mean reward function lies in the reproducing kernel Hilbert

Beyond Pixel Overlap: A Framework for Decomposing Segmentation Evaluation Metrics

arXiv:2607.00886v1 Announce Type: new Abstract: Evaluation metrics are central to binary target segmentation because they determine how progress is measured, compared, and interpreted. In this paper,

Breaking the Weak Recovery Limit in Random Phase Retrieval with Learned Regularizers

arXiv:2509.15026v2 Announce Type: replace-cross Abstract: We seek to recover an unknown signal from nonlinear amplitude-only measurements, a challenging inverse problem. Strong theoretical guarantees

Bridging Quantum Computing Paradigms toward Semiconductor Yield: A Controlled CV-versus-DV Comparison on Wafer-Map Defect Classification

arXiv:2607.00961v1 Announce Type: cross Abstract: Realizing quantum neural networks (QNNs) in industry requires knowing which quantum computing paradigm suits which task. Motivated by AI accelerators

Calibrated Test-Time Guidance for Bayesian Inference

arXiv:2602.22428v2 Announce Type: replace-cross Abstract: Test-time guidance is a widely used mechanism for steering pretrained diffusion models toward outcomes specified by a reward function. Existin

CAT: Confidence-Adaptive Thinking for Efficient Reasoning of Large Reasoning Models

arXiv:2607.00862v1 Announce Type: cross Abstract: Large Reasoning Models (LRMs) have achieved remarkable success on complex tasks by leveraging long chain-of-thought (CoT) trajectories, yet they frequ

CausalMix: Data Mixture as Causal Inference for Language Model Training

arXiv:2607.01104v1 Announce Type: cross Abstract: In Large Language Model (LLM) training, data mixing plays a pivotal role in determining model performance. Recent methods optimize mixture weights via

Characterizing and Identifying Separable Graphical Models

arXiv:2607.01057v1 Announce Type: cross Abstract: We study a broad class of graphical models whose independencies correspond to vertex separation in mixed graphs with directed, undirected, and bidirec

Closed-loop coupling of personalised and foundation models for real-time treatment guidance with MRI

arXiv:2607.00500v1 Announce Type: cross Abstract: Image-guided therapies, including radiotherapy, biopsy and deep brain stimulation, rely on real-time targeting of anatomical structures. However, in t

Constructing Epistemic AI Literacy: Detecting Epistemic Aims and Processes in Student-AI Co-Programming

arXiv:2607.00211v1 Announce Type: new Abstract: Epistemic thinking plays a central role in students' learning processes when applying generative artificial intelligence (GenAI), particularly in progra

Controllable Diffusion-Based Lesion Inpainting for Scalable Histopathology Data Augmentation

arXiv:2601.08127v2 Announce Type: replace-cross Abstract: Expert-annotated training data remains the critical bottleneck for AI in histopathology, particularly for rare pathologies where even dozens o

Controllable Narrative Rendering for Enhanced Assisted Writing

arXiv:2607.00009v1 Announce Type: cross Abstract: Despite the remarkable proficiency of large language models (LLMs) in basic writing assistance, their utility in creative writing is fundamentally hin

Cross-Domain Generalization Failure in Lightweight Intrusion Detection Models for IIoT Networks

arXiv:2607.00553v1 Announce Type: cross Abstract: Lightweight machine learning models are increasingly proposed for intrusion detection in Industrial Internet of Things (IIoT) networks due to their su

Cross4D-JEPA: Dense Cross-modal Correspondence Distillation for 4D Point Cloud Representation Learning

arXiv:2607.00514v1 Announce Type: cross Abstract: Automatic understanding of dynamic 4D point clouds, the 3D-point sequences captured over time by depth sensors and LiDAR, is central to robotics and e

Crystalite: A Lightweight Transformer for Efficient Crystal Modeling

arXiv:2604.02270v2 Announce Type: replace-cross Abstract: Generative models for crystalline materials often rely on equivariant graph neural networks, which capture geometric structure well but are co

DART: Difficulty-Adaptive Routing for Zero-Shot Video Temporal Grounding

arXiv:2607.00672v1 Announce Type: new Abstract: Zero-shot video temporal grounding (VTG) localizes events in untrimmed videos from natural language queries without task-specific training. Existing met

DART-VLN: Test-Time Memory Decay and Anti-Loop Regularization for Discrete Vision-Language Navigation

arXiv:2607.01043v1 Announce Type: cross Abstract: Memory-based discrete vision-language navigation (VLN) agents must act under partial observability, yet even strong frozen backbones remain vulnerable

Decompose, Compare, and Decide: Multimodal LLMs are Implicit Few-Shot Learners

arXiv:2607.00125v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable abilities when analyzing images, yet translating these capabilities to few-shot im

Decoupled Guidance: Disentangling Subject and Context Pathways in Text-to-Image Personalization

arXiv:2607.00766v1 Announce Type: new Abstract: Text-to-image personalization aims to generate a user-provided subject in novel scenes described by text. However, most existing methods encode subject

Deep Learning-Driven Black-Box Doherty Power Amplifier with Pixelated Output Combiner and Extended Efficiency Range

arXiv:2603.16565v2 Announce Type: replace-cross Abstract: This article presents a deep learning-driven inverse design methodology for Doherty power amplifiers (PA) with multi-port pixelated output com

Deep Multitask Learning for Mixed-Type Outcomes with Shared Sparsity

arXiv:2607.00995v1 Announce Type: cross Abstract: Most existing multitask learning approaches are limited by their reliance on task-specific loss functions tailored to the scale and type of each outco