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GeoDistill-Refine: Silhouette-First Geometry Distillation for Annotation-Free Spacecraft Segmentation

arXiv:2608.07405v1 Announce Type: cross Abstract: Foundation segmentation models can provide supervision for spacecraft imagery without manual training masks, but their predictions vary with textual p

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GPTKB 2.0: Browsing, Querying, and Auditing a Disambiguated LLM-Derived Knowledge Base

DGX agent

arXiv:2608.06992v1 Announce Type: cross Abstract: We present a web demo for exploring a large-scale disambiguated knowledge base (KB) materialized from a large language model (LLM). GPTKB 2.0 contains

researcharxiv-cs-ai

10 Aug 2026

Grammar Engineering Meets LLMs: Development of Cantonese and Irish ParGram Treebanks

arXiv:2608.07283v1 Announce Type: new Abstract: Grammar engineering requires expertise in linguistic formalism and computational implementation, especially in parallel grammar projects that balance cr

HFS: Holistic Query-Aware Frame Selection for Efficient Video Understanding

arXiv:2512.11534v2 Announce Type: replace-cross Abstract: Key frame selection is essentially a set-level optimization problem: the quality of the selected subset depends on the interactions among fram

HNR-DAC: Hard-Negative Reranking and Distribution-Aligned Classification for Scientific Claim Verification

arXiv:2608.07204v1 Announce Type: new Abstract: Scientific claim verification over a cited paper requires predicting the claim--paper relation and identifying the paragraphs that justify that predicti

Hoverflie: An empirical investigation of rotor shrouds to transform micro air vehicles into multi-modal hovercraft

arXiv:2608.06707v1 Announce Type: new Abstract: Small rotorcraft intended for use indoors or around the built environment have extremely limited flight duration. This paper presents the design and exp

researcharxiv-cs-ro

How Long Reasoning Chains Influence LLMs' Judgment of Answer Factuality

arXiv:2604.06756v2 Announce Type: replace Abstract: Large language models (LLMs) has been widely adopted as a scalable surrogate for human evaluation, yet such judges remain imperfect and susceptible

How Molecular Generative Models Organize Molecular Identity

arXiv:2608.06956v1 Announce Type: new Abstract: Generative models for matter are often evaluated as samplers over output representations, and their latent spaces are commonly used as proxies for navig

HRDiT: Training-Free High-Resolution Image Generation with Off-the-Shelf Diffusion Transformer Models

arXiv:2608.07003v1 Announce Type: new Abstract: Training-free text-to-high-resolution image generation has recently attracted growing research attention. However, existing studies on this task primari

IceHorizon: A Dataset for Horizon Detection in Ice-Covered Maritime Environments and Comparative Evaluation of Detection Methods

arXiv:2608.07018v1 Announce Type: cross Abstract: Horizon detection in images of ice-covered waters is a challenging problem for maritime navigation due to low contrast between water and sky, cluttere

Identifying the Key Biomechanical Features of Movement Adaptation during Exoskeleton-Assisted Locomotion

arXiv:2608.07140v1 Announce Type: new Abstract: The understanding of natural human adaptation during exoskeleton-assisted locomotion - particularly individual differences in adaptation behaviors and t

researcharxiv-cs-ro

Implicit Neural Speckle Denoising

arXiv:2608.06574v1 Announce Type: cross Abstract: Speckle fundamentally limits coherent imaging by introducing multiplicative, spatially correlated noise that obscures scene structure. Removing speckl

Impressive new paper from Meta. (bookmark it) Scaling laws assume model size and training data act on loss independently. This work introduc…

Impressive new paper from Meta. (bookmark it) Scaling laws assume model size and training data act on loss independently. This work introduces Skaling law, which couples capacity and data through a si

researchdair-ai--x

Interaction Creates Dynamical AI Behavior Absent in Isolation

arXiv:2608.07457v1 Announce Type: new Abstract: What will happen when AI agents interact in daily life, e.g. when one AI starts bossing another around? We find a counterintuitive answer that opens new

International Transfer of Stochastic Cortical Self-Reconstruction

arXiv:2608.07092v1 Announce Type: cross Abstract: Stochastic cortical self-reconstruction (SCSR) enables personalized mapping of gray matter atrophy, a hallmark of neurodegenerative disorders such as

INTRYGUE: Induction-Aware Entropy Gating for Reliable RAG Uncertainty Estimation

arXiv:2603.21607v2 Announce Type: replace Abstract: While retrieval-augmented generation (RAG) significantly improves the factual reliability of LLMs, it does not eliminate hallucinations, so robust u

Investigating Quantum-Embedded Transformers on Classical Datasets for Cross-Modality Classification

arXiv:2608.06846v1 Announce Type: cross Abstract: We test whether a parameterized quantum circuit (PQC) improves a hybrid quantum-classical model's performance on classical datasets, using an interfac

Is SwiGLU's Open Positive Tail Necessary? Evidence from Closed-Tail Gating with MemGLU

arXiv:2608.07323v1 Announce Type: new Abstract: We test whether decoder-only language-model FFNs require SwiGLU's open positive tail. We introduce MemGLU as a closed-tail comparator derived from a mem

Ising Acceleration for Multi-Robot Multi-Target Planning

arXiv:2608.06803v1 Announce Type: cross Abstract: Ising machines are emerging as promising hardware for combinatorial optimization. With recent advances in CMOS Ising technology, they are becoming att

researcharxiv-cs-ro

LiFTER: A Grounded Neuro-Symbolic Microscope for Continuous-Time Dynamic Graph Forecasting

arXiv:2608.06765v1 Announce Type: new Abstract: Continuous-time dynamic graph models predict future links by compressing past interactions into neural states. Although effective for forecasting, this

Local Epistemic Uncertainty Guided Active Sampling for Plug-and-play Diffusive Image Restoration

arXiv:2608.06981v1 Announce Type: new Abstract: Diffusion models have demonstrated remarkable effectiveness in image restoration tasks. However, when guiding image reconstruction, existing Diffusion M

Machine Learning-Based Inter-Crystal Scatter Recovery for Ultra-High Resolution PET Imaging

arXiv:2608.07155v1 Announce Type: new Abstract: Inter-crystal scatter (ICS) events pose a significant challenge in ultrahigh- resolution positron emission tomography (UHR-PET), especially as detector

Mathematical Principles and Experimental Discoveries of the Emergence of Symbolic Patterns in Artificial Neural Networks

arXiv:2608.06839v1 Announce Type: new Abstract: Artificial Neural networks (ANNs) are often treated as black-box models, making explainability a central challenge in deep learning. Many engineering me

Mean-square and sublinear convergence of a stochastic proximal point algorithm in metric spaces of nonpositive curvature

arXiv:2510.10697v2 Announce Type: replace-cross Abstract: We define a stochastic variant of the proximal point algorithm in the general setting of nonlinear Hadamard spaces for approximating zeros of

Measurements Automatically Extracted from Zero Echo Time MRI Using Deep Learning Image Segmentation and Geometric Modeling Agree with Expert Manual Readings

arXiv:2608.07368v1 Announce Type: cross Abstract: Computed tomography (CT) remains the reference for 3D osseous morphometry in femoroacetabular impingement (FAI) but requires ionizing radiation and ma

Measuring Concept Content in Text from LLM Activations: ESG Evidence from Concept Vectors and Linear Probes

arXiv:2608.07208v1 Announce Type: cross Abstract: Existing measures of how much a text is about a concept read the surface of the text: dictionary word shares, topic proportions, embedding similaritie

Measuring the Cross-Lingual Comprehension Gap: How the language of the evidence shapes what language models understand

arXiv:2608.06506v1 Announce Type: new Abstract: Language models are often evaluated as though capabilities demonstrated in English remain equally available when the same content is presented in other

MiGHT-EHR: A Multi-task Graph Transformer for Heterogeneous Temporal Electronic Health Records

arXiv:2608.06430v1 Announce Type: new Abstract: Learning from Electronic Health Records (EHRs) has gained significant attention due to its potential to improve clinical prediction. However, effective

Mixture of Geodesic Factor Analyzers on Riemannian Homogeneous Spaces

arXiv:2608.06971v1 Announce Type: cross Abstract: This paper introduces Mixtures of Geodesic Factor Analyzers (MGFA) on Riemannian homogeneous spaces. MGFA uses a geodesic factor model within each mix

Mobile Interaction for Assessing Fatigue, Sleep, and Activity in Neurodegenerative and Chronic Diseases

arXiv:2608.06380v1 Announce Type: cross Abstract: Fatigue, sleep, or disturbances in daily activities are common symptoms among patients with neurodegenerative disorders (NDD) and immune-mediated infl

Momba: Network Modernization Improves Multi-Objective Reinforcement Learning

arXiv:2608.07180v1 Announce Type: cross Abstract: Recent advances in deep reinforcement learning (RL) have shown that improving neural network architectures can yield substantial gains in sample effic

MotionStrata: Hierarchical Motion Latents for Compact Video Autoencoding

arXiv:2506.07136v2 Announce Type: replace Abstract: First-frame-conditioned video autoencoders represent a clip with persistent content and a compact motion code. Although this removes much of the app

Natural Language Processing Psychometrics

arXiv:2608.07316v1 Announce Type: cross Abstract: Natural Language Processing (NLP) models predicting mental health outcomes rarely specify what they measure: contextual knowledge, emotional content,

NxN E-valuation: Hypothesis Certification via a Conformal CRT Null

arXiv:2608.06621v1 Announce Type: new Abstract: We propose NxN E-valuation, a handy, e-value-based hypothesis-certification algorithm that lets a hypothesis be verified without building any case-speci

oldsymbol{lambda}-Orthogonality Regularization for Compatible Representation Learning

arXiv:2509.16664v2 Announce Type: cross Abstract: Retrieval systems rely on representations learned by increasingly powerful models. However, due to the high training cost and inconsistencies in learn

Optimizing Spectral Prediction in MXene-Based Metasurfaces Through Multi-Channel Spectral Refinement and Savitzky-Golay Smoothing

arXiv:2602.08406v2 Announce Type: replace-cross Abstract: The prediction of electromagnetic spectra for MXene-based solar absorbers, where MXenes are a family of two-dimensional transition metal carbi

PACE: Primitive-Aware Code Evolution for Automated Algorithm Design

arXiv:2608.07395v1 Announce Type: cross Abstract: Large Language Model (LLM)-based automated algorithm design typically evolves algorithms as complete, indivisible programs. While this whole-program p

Pathryoshka: Compressing Pathology Foundation Models via Multi-Teacher Knowledge Distillation with Nested Embeddings

arXiv:2511.23204v2 Announce Type: replace Abstract: Pathology foundation models (FMs) have driven significant progress in computational pathology. However, these high-performing models can easily exce

PCAE: Learning Ordered Representations in Latent Space for Intrinsic Dimension Estimation via Principal Component Autoencoder

arXiv:2601.19179v2 Announce Type: replace Abstract: Autoencoders have long been considered a nonlinear extension of Principal Component Analysis (PCA). Prior studies have demonstrated that linear auto

Pre-Inference Routing for Cost-Efficient Document Field Extraction

arXiv:2608.06607v1 Announce Type: new Abstract: Most document-extraction systems use a single model for all documents. This is simple but can be costly for easy cases and less effective for difficult

PRISM: Principled Reference Identification for Schrodinger Bridge Model

arXiv:2608.06893v1 Announce Type: new Abstract: Schrodinger bridge models restore a clean signal from a degraded observation by following the conditional bridges of a reference process, yet this refer

Progressive Content Refinement with Decaying Reward Joint LinUCB

arXiv:2608.06750v1 Announce Type: cross Abstract: Iterative refinement has significantly enhanced Large Language Model (LLM) performance; however, existing methods ranging from feedback-based Self-Ref

Provable Training Data Identification for Large Language Models

arXiv:2510.09717v3 Announce Type: replace-cross Abstract: Identifying training data of large-scale models is critical for copyright litigation, privacy auditing, and ensuring fair evaluation. However,

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models

arXiv:2608.06901v1 Announce Type: new Abstract: Vision-language models (VLMs) have achieved remarkable generalization across diverse multimodal tasks through large-scale pre-training, yet their rapidl

PsychoAgent: An Affect-Sensitive Cognitive Architecture for Conflict-Aware Memory in LLM Agents

arXiv:2608.07438v1 Announce Type: new Abstract: Human-like cognition does not select past experience by topical similarity alone: affective significance and unresolved conflict also shape what becomes

PTQ4SNN: Membrane-Aware Post-Training Quantization for Spiking Neural Networks

arXiv:2608.07066v1 Announce Type: new Abstract: Spiking neural networks (SNNs) enable sparse and event-driven computation, but their low-bit deployment remains incomplete because recurrent membrane st

QFCQT: A Chaotically Gated Quantformer Framework for Volatile Time-Series Forecasting

arXiv:2608.07363v1 Announce Type: new Abstract: Forecasting non-stationary time series remains difficult due to long-range dependencies, local volatility bursts, structural shifts, and nonlinear oscil

Quantum Generative Diffusion Model: A Fully Quantum-Mechanical Model for Generating Quantum State Ensemble

arXiv:2401.07039v5 Announce Type: replace-cross Abstract: Mixed quantum states are the native description of many physically important quantum systems, making their generation a fundamental task in qu