Search: “concepts”

EFFEKT: Efficient Federated Knowledge Transfer to Foundation Models

arXiv:2608.08138v1 Announce Type: new Abstract: Recent data protection laws have accelerated the adoption of Federated Learning (FL) for privacy-preserving decentralized training. Nevertheless, increa

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Research

Enhanced Real-Time 6-DOF Extended Reality Catheter Tracking for Evaluating Potential Improvement in Efficiency, Precision, and Depth Perception for Cardiac Interventions

DGX agent

arXiv:2608.07606v1 Announce Type: cross Abstract: Despite advances in 3D ultrasound, most percutaneous cardiac interventions still rely on 2D visualization, limiting depth perception and spatial under

researcharxiv-cs-ai

11 Aug 2026

Enhancing Knowledge Tracing through Leakage-Free and Recency-Aware Embeddings

arXiv:2508.17092v2 Announce Type: replace-cross Abstract: Knowledge Tracing (KT) aims to predict a student's future performance based on their sequence of interactions with learning content. Many KT m

researcharxiv-cs-ai

Explaining, Verifying, and Aligning Semantic Hierarchies in Vision-Language Model Embeddings

arXiv:2603.26798v2 Announce Type: replace-cross Abstract: Vision-language model (VLM) encoders such as CLIP enable strong retrieval and zero-shot classification in a shared image-text embedding space,

Applications

Exploring LLM Capabilities for Situational Understanding and COLREG compliance on real-world maritime navigation scenarios

arXiv:2608.08281v1 Announce Type: new Abstract: Recently, Large Language Models (LLMs) have shown considerable capability for situational understanding, reasoning, and decision making in different dom

applicationsarxiv-cs-ai

Forgotten History or Test-of-Time? Retrospect and Prospect on RAG from an IR Perspective

arXiv:2608.08445v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) is widely regarded as a novel paradigm born from the limitations of large language models (LLMs)--a mechanism to gr

GRACE: LLM-Grounded Semantic Metric Spaces for Scalable Mixed-Data Clustering

arXiv:2608.07881v1 Announce Type: new Abstract: Clustering mixed tabular data requires a unified metric space to bridge the inherent heterogeneity between continuous numerical measurements and discret

Is CLIP Cross-Eyed? Revealing and Mitigating Center Bias in the CLIP Family

arXiv:2604.05971v2 Announce Type: replace-cross Abstract: Recent research has shown that contrastive vision-language models such as CLIP often lack fine-grained understanding of visual content. While

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LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs

arXiv:2608.07733v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) are widely used across domains such as natural sciences, social network analysis, chip design, and recommendation systems

Lying mirror using structured surfaces

arXiv:2410.15521v2 Announce Type: replace-cross Abstract: We introduce an all-optical system, termed the 'lying mirror', to hide input information by transforming it into misleading, ordinary-looking

researcharxiv-cs-cv

Machine Learning and Data Analysis Using Posets: A Survey

arXiv:2404.03082v3 Announce Type: replace Abstract: Partially ordered sets (posets) are discrete mathematical structures that formalize the notion of comparison without forcing every pair of objects t

safetyarxiv-cs-lg

Private Etymology: Designing Relational Reuse of Shared Symbols in Long-Term Human-AI Interaction

arXiv:2608.08443v1 Announce Type: cross Abstract: Previous studies have shown that people can develop shared symbols, partner-specific expressions, personal idioms, inside jokes, and other parts of a

local-aiarxiv-cs-ai

Recurrent Neural Networks Beyond Time: Learning from Multiple Ordered Projections

arXiv:2608.09690v1 Announce Type: new Abstract: Recurrent neural networks (RNNs) are widely used for sequence learning, yet their application is commonly associated with temporal data, although recurr

SAKE: Structured Agentic Knowledge Extrapolation for Complex LLM Reasoning via Reinforcement Learning

arXiv:2505.15062v5 Announce Type: replace-cross Abstract: Knowledge extrapolation is the process of inferring novel information by combining and extending existing knowledge that is explicitly availab

The Spectral Neuron

arXiv:2608.08003v1 Announce Type: cross Abstract: As machine learned models increase in complexity and expressive power, features of simpler models, such as interpretability and control over the shape

local-aiarxiv-cs-lg

VCU-Bridge: Hierarchical Visual Connotation Understanding via Semantic Bridging

arXiv:2511.18121v2 Announce Type: replace-cross Abstract: While Multimodal Large Language Models (MLLMs) excel on benchmarks, their processing paradigm differs from the human ability to integrate visu

A foundation-model approach to pediatric headache classification from rs-fMRI

arXiv:2608.07287v1 Announce Type: new Abstract: Headache is the most common neurological disorder in children and substantially affects quality of life. We investigated whether resting-state functiona

Agentic AI: User Empowerment or Enclosure?

arXiv:2608.06510v1 Announce Type: cross Abstract: Agentic AI promises a more flexible form of digital agency: systems that can act on users' behalf, from filtering content to negotiating prices to sel

Cascading Through the Hierarchy: Regularizer-Induced Feature Detection as Phase Transitions in Deep Linear Neural Networks

model-releasesarxiv-cs-lg

arXiv:2608.06597v1 Announce Type: cross Abstract: A scientific theory of deep learning, comprising learning dynamics and statistical properties of learned models, is rapidly gaining attention. One of

DREAMS: Diverse Reactions of Engagement and Attention Mind States Dataset

arXiv:2608.06382v1 Announce Type: cross Abstract: Active attention and engagement are important in improving users' learning experiences. Engagement refers to the level of involvement and interest ind

researcharxiv-cs-cv

Dual-Node NVIDIA DGX Spark over Tailscale: A Remote-Access Testbed for Distributed LLM Training and Cyber-Threat-Intelligence Fine-Tuning

arXiv:2608.07226v1 Announce Type: cross Abstract: Compact AI systems make local language-model experimentation increasingly accessible, yet practical evidence for multi-node training on desktop-class

local-aiarxiv-cs-lg

Applications

Equivariant Sparse Autoencoders: Mechanistic Interpretability of Neural Networks on Symmetric Data

arXiv:2511.09432v2 Announce Type: replace Abstract: Machine learning (ML) models achieve remarkable performance but remain hard to interpret due to their scale and complexity. In particular, their act

applicationsarxiv-cs-lg

Human-AI Perceptual Alignment by Playing Hues and Cues

arXiv:2608.07141v1 Announce Type: new Abstract: Evaluating the perceptual alignment between Contrastive Vision-Language Models (CVLMs) and humans is typically constrained by traditional benchmarks tha

local-aiarxiv-cs-cv

Human-Centered Explainable AI for TinyML Edge Devices: A Pareto-Based Selection Framework with LLM-Guided Design

arXiv:2608.07091v1 Announce Type: cross Abstract: Edge Artificial Intelligence (Edge AI) enables the deployment of AI models directly on local edge devices, while such deployments are subject to stric

local-aiarxiv-cs-ai

Latent Fact-Checking: Detecting Misinformation through Activation Engineering

model-releasesarxiv-cs-cl

arXiv:2608.06417v1 Announce Type: cross Abstract: The proliferation of misinformation online has driven demand for scalable detection systems. While most existing approaches rely on surface-level ling

MI-MIDI: Mechanistic Interpretability of Text-to-MIDI Generation Models via Probing, Lenses and Steering

arXiv:2608.06638v1 Announce Type: cross Abstract: Mechanistic interpretability of music generation has concentrated on audio models, leaving symbolic models largely unexplored. We analyze two public t

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

Model Confidence Under Answer-Preserving Attacks: An Informativeness-Manipulability Frontier

model-releasesarxiv-cs-cl

arXiv:2608.06571v1 Announce Type: cross Abstract: Deployed vision-language systems often gate their answers on confidence, making confidence robustness relevant to oversight. We study confidence reado

Science Edge Evaluation: SEE the Missing Step Toward Real Scientific Discovery

arXiv:2608.06931v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly involved in scientific discovery, yet it remains unclear whether they can support complex real laboratory

Seeking SOTA: Time-Series Forecasting Must Adopt Taxonomy-Specific Evaluation to Dispel Illusory Gains

arXiv:2603.15506v2 Announce Type: replace-cross Abstract: We argue that the current practice of evaluating AI/ML time-series forecasting models, predominantly on benchmarks characterized by strong, pe

Social World Models

arXiv:2509.00559v3 Announce Type: replace Abstract: Humans intuitively navigate social interactions by simulating unspoken dynamics and reasoning about others' perspectives, even with limited informat

Tutorials

Analogy as Nonparametric Bayesian Inference over Relational Systems

arXiv:2006.04156v2 Announce Type: replace Abstract: Our inferences in the real world are rarely naive - we acquire experiences through our lifetime that can help us more quickly understand the structu

tutorialsarxiv-cs-ai

BlockPython: A Process-Aware Agent-Supported Platform for the Transition from Block-Based to Python Programming

arXiv:2608.05716v1 Announce Type: new Abstract: The transition from block-based to text-based programming requires learners to convert visible program structures into abstract textual expressions, whi

DARAD: Dual Adapters and Ranking-Aware Distillation for Continual Remote Sensing Image-Text Retrieval

arXiv:2608.06059v1 Announce Type: new Abstract: With the rapid growth of Earth observation technologies, remote sensing archives are rapidly expanding, making remote sensing image-text retrieval (RS-I

safetyarxiv-cs-cv

Evaluating and Improving Pedagogical Fit in LLM-Based AI Tutors with the Pedagogical Suitability Index

arXiv:2608.05411v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as AI tutors, but a correct answer is not always a pedagogically appropriate one. In classroom learni

Evidential Rule Learning for Interpretable Classification with Abstention

arXiv:2608.05859v1 Announce Type: cross Abstract: Interpretable classification often requires more than accurate predictions for real-life deployment: models should be transparent about the evidence b

Sliding Sensors: Configurable Confidence in State Estimation for Continuum Robots

arXiv:2608.05410v1 Announce Type: new Abstract: Continuum robots often operate in uncertain environments, where accurate state estimation is essential for safe interactions. Estimate uncertainty is in

researcharxiv-cs-ro

Tutorials

An entropic explanation of insistence on sameness in autism

arXiv:2608.04616v1 Announce Type: new Abstract: An information theory-based framework is proposed in attempt to explain insistence on sameness in autism as an instance of a general behavior pattern in

tutorialsarxiv-cs-lg

Approximate Multi-Objective Search Under Rulebooks

arXiv:2608.04398v1 Announce Type: cross Abstract: Robotic planning often involves multiple objectives with complex priority relationships, such as safety, efficiency, and regulatory compliance. Rulebo

Do LLMs Know What Is Private Internally? Probing and Steering Contextual Privacy Norms in Large Language Model Representations

arXiv:2604.00209v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed in high-stakes settings, yet they frequently violate contextual privacy by disclosing private

researcharxiv-cs-cl

From Score Matrices to Football-Aware Match-State Simulation: An Auditable LLM Harness for Exact-Score Reranking

arXiv:2608.05030v1 Announce Type: new Abstract: Football score forecasting combines a strong statistical core with a difficult contextual edge. Dynamic Poisson-family models estimate team strength, ex

Improving Auto-Design of Neural PDE Solvers with a Domain-Specific Language

arXiv:2608.04384v1 Announce Type: new Abstract: Neural PDE solver auto-design is fundamentally a search-space representation problem. In the space of unrestricted Python programs, valid solvers form a

IslamicTurathBench: A Multi-Task, Multi-Discipline Benchmark for Evaluating Large Language Models on the Islamic Scholarly Tradition (turath)

model-releasesarxiv-cs-cl

arXiv:2608.04703v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for question answering, education, and research, including in religious and cultural domains where an

Tutorials

NeuMoSync: End-to-End Neuromodulatory Control for Plasticity and Adaptability in Continual Learning

arXiv:2608.04358v1 Announce Type: new Abstract: Continual learning (CL) requires models to learn tasks sequentially, yet deep neural networks often suffer from plasticity loss and poor knowledge trans

tutorialsarxiv-cs-ai

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

A game theory for foundation models shows new paths to rational cooperation through similarity inference

arXiv:2608.03958v1 Announce Type: new Abstract: As autonomous agents powered by foundation models are increasingly integrated into social and economic systems, understanding the principles governing t

5 Aug 2026

Activation-Guided Neuron Intervention to Induce Alzheimer's-Related Computational Language Phenotypes in a Large Language Model

arXiv:2608.03067v1 Announce Type: new Abstract: Changes in spontaneous speech provide an early signal of cognitive dysfunction in Alzheimer's disease (AD) that large language models (LLMs) can detect.

researcharxiv-cs-cl

5 Aug 2026

Adversarial Fast-Moving Real-World Domains as Test Beds for Benchmarking AI Scientist Capabilities

arXiv:2608.03569v1 Announce Type: new Abstract: Benchmarking the ability of AI scientists to generate novel ideas is notoriously difficult. Existing benchmarks in this field have made progress in eval