Search: “research”

Preverbal Uninflected and Underived Roots in Mapudungun. Wuno and Its Implications

arXiv:2608.04869v1 Announce Type: new Abstract: This study examines the grammatical status of preverbal uninflected and underived roots in Mapudungun, with particular focus on wuno 'return/re-'. Throu

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PriDyG: Privacy-preserving Dynamic Graph Inference with LLM-GNN Collaboration

DGX agent

arXiv:2608.04255v1 Announce Type: cross Abstract: Graph inference over relational data can expose sensitive edge information, and this risk becomes more severe in dynamic graphs, where repeated model

researcharxiv-cs-lg

6 Aug 2026

Prototype-based Self-Supervised Multimodal Learning for PPG and Accelerometry Signals

arXiv:2510.09764v2 Announce Type: replace Abstract: Modeling multi-modal time-series data is critical for capturing system-level dynamics, particularly in biosignals where modalities such as ECG, PPG,

PSI3D: Plug-and-Play 3D Stochastic Inference with Slice-wise Latent Diffusion Prior

arXiv:2512.18367v2 Announce Type: replace-cross Abstract: Diffusion models are highly expressive image priors for Bayesian inverse problems. However, most diffusion models cannot operate on large-scal

PURPOSE: Poisoning Conflict Resolution in RAG via Proxy-Fact-Grounded Updates

arXiv:2608.04756v1 Announce Type: cross Abstract: In Retrieval-Augmented Generation (RAG), post-retrieval conflict resolution arbitrates among noisy or contradictory retrieved passages. However, the r

RAG-Stack: Co-Optimizing RAG Serving Performance and Quality

arXiv:2608.03487v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG), which augments large language model (LLM) generation with information retrieved from databases, has become a wid

Random features for Grassmannian kernel approximation with bounded rank-one projections

arXiv:2608.04227v1 Announce Type: new Abstract: We propose a family of random feature maps for scalable kernel machines on low-dimensional subspaces, ie on the Grassmannian manifold. Such representati

Reachability in 3-VAS

arXiv:2608.04786v1 Announce Type: new Abstract: We settle the exact complexity of the reachability problem in (stateless) vector addition systems (VAS) in fixed low dimension. In dimensions 2-4 it has

Reasoning Core: Designing Broad Procedural Data for Completion-Supervised Reasoning Training

arXiv:2608.05148v1 Announce Type: new Abstract: Procedural generators produce useful verifiable reasoning problems at scale, but have received less attention as data for completion-supervised fine-tun

Reconstructing Persistent Worlds from Narratives for Narrative-Grounded Interactive Experiences

arXiv:2608.04037v1 Announce Type: cross Abstract: Designing narrative-grounded interactive experiences remains labor-intensive because interactive content must align with the underlying world implied

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs

arXiv:2608.04048v1 Announce Type: cross Abstract: Serving large language models (LLMs) under diverse deployment constraints requires flexible trade-offs between accuracy, memory footprint, and through

RegisterBridgeMM: A Register-Centric Framework for RGB-Infrared Object Detection

arXiv:2608.04833v1 Announce Type: new Abstract: RGB-infrared (RGB-IR) object detection benefits from complementary visible and thermal cues, but effective fusion remains challenging under illumination

Regularization can make diffusion models more efficient

arXiv:2502.09151v3 Announce Type: replace Abstract: Diffusion models are one of the key architectures of generative AI. Their main drawback, however, is the computational costs. This study indicates t

Reinforcement Learning and Consumption-Savings Behavior

arXiv:2510.20748v2 Announce Type: replace-cross Abstract: This paper demonstrates how reinforcement learning can explain two puzzling empirical patterns in household consumption behavior during econom

Relational Response Fields: A General Theory of Black-Box LLM Response Consistency and Recovery

arXiv:2608.04552v1 Announce Type: new Abstract: Black-box language-model reliability is commonly pursued by sampling, prompting, voting, verifying, or iteratively revising individual answers. We ask a

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling

arXiv:2608.04554v1 Announce Type: new Abstract: Predicting item difficulty from content can provide an initial estimate for newly developed questions before sufficient student responses are available.

Rethinking Pixel Mean Flows via Interval Denoiser

arXiv:2608.04818v1 Announce Type: new Abstract: Modern diffusion and flow-based models are increasingly moving toward few-step, latent-free generation to bypass the computational overhead of multi-ste

Revealed Rationality: Label-Free Evaluation and Regularization from Representation Theorems

arXiv:2608.05015v1 Announce Type: cross Abstract: Representation theorems in decision theory establish that behavior satisfies certain axioms if and only if it can be rationalized by a well-defined ob

Review Text as a Leading Indicator of Displayed Reputation in Platform Rating Systems: Evidence from 34 U.S. Short-Term Rental Markets

arXiv:2504.14053v2 Announce Type: replace-cross Abstract: Rating systems on accommodation platforms suffer from a familiar problem: nearly every listing displays a nearly perfect score, so the number

REZE: Recognition-Based Zero-Shot Extraction for Video Temporal Grounding

arXiv:2608.04480v1 Announce Type: new Abstract: Video temporal grounding (VTG) refers to the task of identifying the time interval in a video that corresponds to a given natural-language query. A comm

RiboSphere: Learning Unified and Efficient Representations of RNA Structures

arXiv:2603.19636v2 Announce Type: replace Abstract: Accurate RNA structure modeling remains difficult because RNA backbones are highly flexible, non-canonical interactions are prevalent, and experimen

Right Reset: Chunking by Prefix Removal

arXiv:2608.04330v1 Announce Type: new Abstract: Removing the left context from a causal language model reveals a useful kind of boundary: an edge where the model processes the same right-hand tokens w

RingSQL: Schema-Independent Synthetic Data Generation for Text-to-SQL Reinforcement Learning

arXiv:2601.05451v2 Announce Type: replace-cross Abstract: Recent advances in text-to-SQL have been driven by larger models, better datasets, and new training methods like RLVR. However, progress remai

RUTA: Principled Visual Token Allocation via Rate-Utility Optimization

arXiv:2608.04132v1 Announce Type: new Abstract: High-resolution images and long videos provide vision-language models with rich context for multimodal reasoning and fine-grained perception, but the re

Sample Complexity of Multicalibration for Multilevel Properties

arXiv:2608.04288v1 Announce Type: new Abstract: Calibration requires a predictor to be unbiased after conditioning on its own predictions. Multicalibration asks for this guarantee simultaneously acros

Searching for Sound-Meaning Collisions: Graph-Based Affordance Retrieval and Multi-Evaluator Ranking for Pun Translation at CLEF 2026 JOKER Task 2

arXiv:2608.04299v1 Announce Type: new Abstract: Fifteen years ago, Low proposed that pun translators should stop searching for equivalent words and instead search for new points of contact between sou

Season: Spectrum-Aware Orthogonal Gradient Refinement for Transfer-Based Adversarial Attacks

arXiv:2608.04441v1 Announce Type: new Abstract: Transfer-based adversarial attacks often transfer poorly across heterogeneous architectures because CNNs favor local textures while Vision Transformers

Seeking Physics in Diffusion Noise

arXiv:2603.14294v3 Announce Type: replace-cross Abstract: Do video diffusion models encode signals predictive of physical plausibility? We probe intermediate denoising representations of pretrained Di

Segmentation Pre-training for Label-Efficient Lumbar Spine Degeneration Grading

arXiv:2608.04810v1 Announce Type: new Abstract: Automated assessment of degenerative pathology in the lumbar spine on magnetic resonance imaging (MRI) requires access to large-scale datasets of expert

SHIELD: A Segmented Hierarchical Memory Architecture for Energy-Efficient LLM Inference on Edge NPUs

arXiv:2604.07396v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) inference on edge Neural Processing Units (NPUs) is fundamentally constrained by limited on-chip memory capacity. A

SiMDex: Mining Similar Egocentric Videos for Cross-Embodiment Dexterous Manipulation

arXiv:2608.04196v1 Announce Type: cross Abstract: Recent years have witnessed an explosive trend of scaling ego-centric human videos for robot manipulation, yet it remains unclear which data actually

Simile Understanding in Text-to-Image Models: An Evaluation Framework

arXiv:2608.04750v1 Announce Type: cross Abstract: Similes provide a compact and expressive way to describe visual characteristics in text prompts. Recent text-to-image models (t2i models) can produce

Spend Bits Where Queries Look: KV Cache Vector Quantization with Attention-Preserving Transforms

arXiv:2608.04074v1 Announce Type: cross Abstract: Long-context LLM decoding reads the key-value (KV) cache at every step. Loading it takes longer than computing attention over it, so throughput is ban

SSTQ:Privacy-Preserving Vector Quantization via Subsampled Stochastic TurboQuant

arXiv:2608.05127v1 Announce Type: cross Abstract: Achieving local differential privacy in distributed optimization while maintaining low communication cost remains challenging. Existing vector quantiz

Stable Density Ridges: Consistency and Convergence of Subspace Constrained Mean Shift

arXiv:2608.05112v1 Announce Type: cross Abstract: The Subspace Constrained Mean Shift (SCMS) algorithm is a popular nonparametric method for extracting density ridges, which serve as a low-dimensional

Static Timing Orchestration for Tree-Structured Robot Control Firmware

arXiv:2608.04600v1 Announce Type: new Abstract: As robotic systems become increasingly complex, generating control firmware from structural description files has emerged as a promising paradigm for re

researcharxiv-cs-ro

Statistical learning theory and Occam's razor: Regularization

arXiv:2608.04049v1 Announce Type: cross Abstract: The principle of Occam's razor, which instructs us to prefer simplicity in inductive inference, has attracted much scrutiny both in the philosophy of

Stochastic Emulation using Generalized Stratified Sampling for Performance-Based Risk Optimization of Structures

arXiv:2608.05006v1 Announce Type: new Abstract: Metamodels are instrumental in reducing the computational burden associated with nested reliability analyses and optimization loops in Performance-Based

Sublogarithmic Swap Regret in Multiplayer General-Sum Games via Hybrid Regularization

arXiv:2608.04149v1 Announce Type: cross Abstract: Swap regret governs the rate at which uncoupled learning dynamics converge to correlated equilibria in multiplayer general-sum games. Under full-infor

SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discovery

arXiv:2608.04930v1 Announce Type: cross Abstract: Bayesian causal discovery seeks to determine the posterior distribution of causal theories, which are interpreted as directed acyclic graphs (DAGs) th

The Cost of Binarizing Survival Outcomes in Clinical Prognostic Modeling

arXiv:2608.04046v1 Announce Type: cross Abstract: Survival analysis is an established framework for analyzing time-to-event data, yet many clinical machine learning studies still binarize the outcome

The Effect of Perceived Race and Gender on Police Language Use: Experimental Evidence from VR Simulations

arXiv:2608.05050v1 Announce Type: cross Abstract: Against the backdrop of violence in police interactions with the U.S. public, we explore how deferentially police officers speak to virtual characters

The Personalization Mirage: How LLMs Fabricate User Profiles, and Why Self-Monitoring Misleads

arXiv:2608.04570v1 Announce Type: new Abstract: Personalized LLMs with persistent memory are increasingly deployed, yet the faithfulness of their user models remains unexamined. We study over-inferenc

The Sample Complexity of Distributionally Robust PAC Learning under Cressie--Read Divergences

arXiv:2608.04686v1 Announce Type: new Abstract: We study distributionally robust PAC learning for the 0--1-loss, where adversarial perturbations of the data distribution are constrained by a Cressie--

Towards Understanding Gradient Flow Dynamics of Homogeneous Neural Networks Beyond the Origin

arXiv:2502.15952v3 Announce Type: replace Abstract: Recent works exploring the training dynamics of homogeneous neural network weights under gradient flow with small initialization have established th

Towards Valid B-Rep Generation: Training-Free Wireframe Anomaly Detection and Repair

arXiv:2608.04955v1 Announce Type: new Abstract: Multi-stage boundary representation (B-Rep) generation leverages intermediate wireframes to synthesize CAD models. However, geometric and topological ri

Transferable Dual-Stream Representations for Mesoscale-Preserving Sea Surface Temperature Downscaling

arXiv:2608.04230v1 Announce Type: cross Abstract: Deep learning models for scientific spatio-temporal downscaling often minimize reconstruction error while failing to preserve physically meaningful mu

TRCoRSurg: Temporal-Relational Co-Reasoning for Surgical Video Triplet Recognition

arXiv:2608.04606v1 Announce Type: new Abstract: Understanding complex surgical scenes requires recognizing multiple interdependent entities, such as instruments, actions, and targets, while maintainin