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UMKC Announces New Master of Science in Artificial Intelligence
Ai Infrastructure

UMKC Announces New Master of Science in Artificial Intelligence

UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...

AI News - General · 4 min ·
[2603.10047] Toward Epistemic Stability: Engineering Consistent Procedures for Industrial LLM Hallucination Reduction
Llms

[2603.10047] Toward Epistemic Stability: Engineering Consistent Procedures for Industrial LLM Hallucination Reduction

Abstract page for arXiv paper 2603.10047: Toward Epistemic Stability: Engineering Consistent Procedures for Industrial LLM Hallucination ...

arXiv - AI · 4 min ·
[2512.18388] Exploration vs. Fixation: Scaffolding Divergent and Convergent Thinking for Human-AI Co-Creation with Generative Models
Machine Learning

[2512.18388] Exploration vs. Fixation: Scaffolding Divergent and Convergent Thinking for Human-AI Co-Creation with Generative Models

Abstract page for arXiv paper 2512.18388: Exploration vs. Fixation: Scaffolding Divergent and Convergent Thinking for Human-AI Co-Creatio...

arXiv - AI · 4 min ·

All Content

[2602.18511] Beyond Pass-by-Pass Optimization: Intent-Driven IR Optimization with Large Language Models
Llms

[2602.18511] Beyond Pass-by-Pass Optimization: Intent-Driven IR Optimization with Large Language Models

The paper presents IntOpt, an intent-driven IR optimizer that enhances program optimization by separating high-level intent from low-leve...

arXiv - AI · 4 min ·
[2602.19392] Spiking Graph Predictive Coding for Reliable OOD Generalization
Machine Learning

[2602.19392] Spiking Graph Predictive Coding for Reliable OOD Generalization

The paper introduces Spiking Graph Predictive Coding (SIGHT), a novel approach to enhance out-of-distribution (OOD) generalization in gra...

arXiv - Machine Learning · 3 min ·
[2602.19362] LLMs Can Learn to Reason Via Off-Policy RL
Llms

[2602.19362] LLMs Can Learn to Reason Via Off-Policy RL

The paper presents a novel off-policy reinforcement learning algorithm, OAPL, for Large Language Models (LLMs) that enhances reasoning ca...

arXiv - Machine Learning · 4 min ·
[2602.19332] Training-Free Cross-Architecture Merging for Graph Neural Networks
Machine Learning

[2602.19332] Training-Free Cross-Architecture Merging for Graph Neural Networks

The paper presents H-GRAMA, a training-free framework for merging heterogeneous Graph Neural Networks (GNNs), allowing efficient model in...

arXiv - Machine Learning · 3 min ·
[2602.18478] ZUNA: Flexible EEG Superresolution with Position-Aware Diffusion Autoencoders
Machine Learning

[2602.18478] ZUNA: Flexible EEG Superresolution with Position-Aware Diffusion Autoencoders

The paper presents ZUNA, a 380M-parameter masked diffusion autoencoder designed for EEG signal superresolution and channel infilling, dem...

arXiv - Machine Learning · 3 min ·
[2602.19330] CTS-Bench: Benchmarking Graph Coarsening Trade-offs for GNNs in Clock Tree Synthesis
Machine Learning

[2602.19330] CTS-Bench: Benchmarking Graph Coarsening Trade-offs for GNNs in Clock Tree Synthesis

The paper introduces CTS-Bench, a benchmark suite for evaluating graph coarsening trade-offs in Graph Neural Networks (GNNs) for Clock Tr...

arXiv - Machine Learning · 4 min ·
[2602.19271] Taming Preconditioner Drift: Unlocking the Potential of Second-Order Optimizers for Federated Learning on Non-IID Data
Machine Learning

[2602.19271] Taming Preconditioner Drift: Unlocking the Potential of Second-Order Optimizers for Federated Learning on Non-IID Data

This paper presents FedPAC, a framework to enhance the stability and accuracy of second-order optimizers in federated learning on non-IID...

arXiv - AI · 4 min ·
[2602.18471] Charting the Future of AI-supported Science Education: A Human-Centered Vision
Ai Infrastructure

[2602.18471] Charting the Future of AI-supported Science Education: A Human-Centered Vision

This article discusses the transformative potential of AI in science education, proposing a human-centered framework for its ethical inte...

arXiv - AI · 4 min ·
[2602.18470] Transforming Science Learning Materials in the Era of Artificial Intelligence
Generative Ai

[2602.18470] Transforming Science Learning Materials in the Era of Artificial Intelligence

This article explores how AI is reshaping science learning materials, enhancing personalization, accessibility, and interactivity while a...

arXiv - AI · 4 min ·
[2602.18469] The Landscape of AI in Science Education: What is Changing and How to Respond
Ai Infrastructure

[2602.18469] The Landscape of AI in Science Education: What is Changing and How to Respond

This article explores the transformative impact of AI on science education, highlighting changes in educational practices and the need fo...

arXiv - AI · 4 min ·
[2602.18464] How Well Can LLM Agents Simulate End-User Security and Privacy Attitudes and Behaviors?
Llms

[2602.18464] How Well Can LLM Agents Simulate End-User Security and Privacy Attitudes and Behaviors?

This paper investigates the effectiveness of large language model (LLM) agents in simulating user attitudes and behaviors towards securit...

arXiv - AI · 4 min ·
[2602.19207] HybridFL: A Federated Learning Approach for Financial Crime Detection
Machine Learning

[2602.19207] HybridFL: A Federated Learning Approach for Financial Crime Detection

The paper presents HybridFL, a federated learning approach designed for financial crime detection, which integrates horizontal and vertic...

arXiv - AI · 3 min ·
[2602.18460] The Doctor Will (Still) See You Now: On the Structural Limits of Agentic AI in Healthcare
Robotics

[2602.18460] The Doctor Will (Still) See You Now: On the Structural Limits of Agentic AI in Healthcare

This article examines the limitations of agentic AI in healthcare, highlighting the gap between commercial promises and operational reali...

arXiv - AI · 4 min ·
[2602.19169] Virtual Parameter Sharpening: Dynamic Low-Rank Perturbations for Inference-Time Reasoning Enhancement
Machine Learning

[2602.19169] Virtual Parameter Sharpening: Dynamic Low-Rank Perturbations for Inference-Time Reasoning Enhancement

The paper introduces Virtual Parameter Sharpening (VPS), a novel technique for enhancing inference-time reasoning in transformer models t...

arXiv - AI · 3 min ·
[2602.18458] The Story is Not the Science: Execution-Grounded Evaluation of Mechanistic Interpretability Research
Robotics

[2602.18458] The Story is Not the Science: Execution-Grounded Evaluation of Mechanistic Interpretability Research

The article presents a novel evaluation framework for mechanistic interpretability research, utilizing AI agents to enhance research rigo...

arXiv - Machine Learning · 3 min ·
[2602.19143] Incremental Learning of Sparse Attention Patterns in Transformers
Machine Learning

[2602.19143] Incremental Learning of Sparse Attention Patterns in Transformers

This paper explores how transformers learn through incremental acquisition of sparse attention patterns, revealing shifts in learning dyn...

arXiv - Machine Learning · 3 min ·
[2602.19142] Celo2: Towards Learned Optimization Free Lunch
Llms

[2602.19142] Celo2: Towards Learned Optimization Free Lunch

The paper 'Celo2: Towards Learned Optimization Free Lunch' presents a novel learned optimizer that significantly reduces the computationa...

arXiv - AI · 3 min ·
[2602.19131] Test-Time Learning of Causal Structure from Interventional Data
Machine Learning

[2602.19131] Test-Time Learning of Causal Structure from Interventional Data

The paper presents TICL, a novel method for causal structure learning from interventional data, enhancing generalization across diverse s...

arXiv - AI · 3 min ·
[2602.19126] Robust Predictive Uncertainty and Double Descent in Contaminated Bayesian Random Features
Machine Learning

[2602.19126] Robust Predictive Uncertainty and Double Descent in Contaminated Bayesian Random Features

This paper presents a robust Bayesian approach to random feature regression, addressing prior and likelihood misspecification through Hub...

arXiv - Machine Learning · 4 min ·
[2602.18447] ConfSpec: Efficient Step-Level Speculative Reasoning via Confidence-Gated Verification
Llms

[2602.18447] ConfSpec: Efficient Step-Level Speculative Reasoning via Confidence-Gated Verification

The paper presents ConfSpec, a novel framework for efficient step-level speculative reasoning in large language models, achieving signifi...

arXiv - AI · 3 min ·
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Related Topics

Machine Learning Large Language Models Generative AI Natural Language Processing Computer Vision Robotics & Embodied AI

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