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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 ·
Ai Infrastructure

Most people are using AI wrong—and it’s capping what they can do

1 is a fluke. 2 is a coincidence. 3 is a pattern. Lately I’ve been noticing something. The problems I’m solving are getting more complex…...

Reddit - Artificial Intelligence · 1 min ·
Ai Infrastructure

Most people are using AI wrong—and it’s capping what they can do

1 is a fluke. 2 is a coincidence. 3 is a pattern. Lately I’ve been noticing something. The problems I’m solving are getting more complex…...

Reddit - Artificial Intelligence · 1 min ·

All Content

[2602.20419] CREDIT: Certified Ownership Verification of Deep Neural Networks Against Model Extraction Attacks
Machine Learning

[2602.20419] CREDIT: Certified Ownership Verification of Deep Neural Networks Against Model Extraction Attacks

The paper introduces CREDIT, a method for certified ownership verification of deep neural networks to combat model extraction attacks, en...

arXiv - Machine Learning · 3 min ·
[2602.20360] Momentum Guidance: Plug-and-Play Guidance for Flow Models
Machine Learning

[2602.20360] Momentum Guidance: Plug-and-Play Guidance for Flow Models

The paper introduces Momentum Guidance (MG), a novel technique for enhancing flow-based generative models, achieving significant improvem...

arXiv - Machine Learning · 3 min ·
[2602.20309] QuantVLA: Scale-Calibrated Post-Training Quantization for Vision-Language-Action Models
Machine Learning

[2602.20309] QuantVLA: Scale-Calibrated Post-Training Quantization for Vision-Language-Action Models

QuantVLA introduces a novel post-training quantization framework for Vision-Language-Action models, enhancing efficiency without addition...

arXiv - Machine Learning · 4 min ·
[2602.20307] In-context Pre-trained Time-Series Foundation Models adapt to Unseen Tasks
Llms

[2602.20307] In-context Pre-trained Time-Series Foundation Models adapt to Unseen Tasks

This paper presents In-Context Time-series Pre-training (ICTP), a framework that enhances time-series foundation models (TSFMs) with in-c...

arXiv - Machine Learning · 3 min ·
[2602.11184] KBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models
Llms

[2602.11184] KBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models

The paper presents KBVQ-MoE, a novel framework for improving vector quantization in Mixture of Experts (MoE) large language models, addre...

arXiv - Machine Learning · 4 min ·
[2602.10693] VESPO: Variational Sequence-Level Soft Policy Optimization for Stable Off-Policy LLM Training
Llms

[2602.10693] VESPO: Variational Sequence-Level Soft Policy Optimization for Stable Off-Policy LLM Training

The paper introduces VESPO, a novel approach for stable off-policy training of large language models (LLMs) that addresses training stabi...

arXiv - Machine Learning · 3 min ·
[2602.07906] AceGRPO: Adaptive Curriculum Enhanced Group Relative Policy Optimization for Autonomous Machine Learning Engineering
Llms

[2602.07906] AceGRPO: Adaptive Curriculum Enhanced Group Relative Policy Optimization for Autonomous Machine Learning Engineering

The paper presents AceGRPO, a novel approach for enhancing autonomous machine learning engineering through adaptive curriculum and group ...

arXiv - Machine Learning · 4 min ·
[2602.00044] When LLMs Imagine People: A Human-Centered Persona Brainstorm Audit for Bias and Fairness in Creative Applications
Llms

[2602.00044] When LLMs Imagine People: A Human-Centered Persona Brainstorm Audit for Bias and Fairness in Creative Applications

This paper introduces the Persona Brainstorm Audit (PBA), a method for assessing bias in Large Language Models (LLMs) used in creative ap...

arXiv - AI · 4 min ·
[2601.09708] Fast-ThinkAct: Efficient Vision-Language-Action Reasoning via Verbalizable Latent Planning
Machine Learning

[2601.09708] Fast-ThinkAct: Efficient Vision-Language-Action Reasoning via Verbalizable Latent Planning

The paper presents Fast-ThinkAct, a novel framework for efficient Vision-Language-Action reasoning that reduces inference latency while m...

arXiv - Machine Learning · 3 min ·
[2512.16602] Refusal Steering: Fine-grained Control over LLM Refusal Behaviour for Sensitive Topics
Llms

[2512.16602] Refusal Steering: Fine-grained Control over LLM Refusal Behaviour for Sensitive Topics

The paper introduces Refusal Steering, a method for controlling Large Language Models' refusal behavior on sensitive topics without retra...

arXiv - AI · 4 min ·
[2511.05722] OckBench: Measuring the Efficiency of LLM Reasoning
Llms

[2511.05722] OckBench: Measuring the Efficiency of LLM Reasoning

The paper introduces OckBench, a benchmark designed to measure the efficiency of token usage in large language models (LLMs), highlightin...

arXiv - AI · 4 min ·
[2510.00037] On Robustness of Vision-Language-Action Model against Multi-Modal Perturbations
Machine Learning

[2510.00037] On Robustness of Vision-Language-Action Model against Multi-Modal Perturbations

This paper evaluates the robustness of Vision-Language-Action (VLA) models against various multi-modal perturbations, proposing a new met...

arXiv - AI · 4 min ·
[2509.25684] LD-MoLE: Learnable Dynamic Routing for Mixture of LoRA Experts
Llms

[2509.25684] LD-MoLE: Learnable Dynamic Routing for Mixture of LoRA Experts

The paper presents LD-MoLE, a novel Learnable Dynamic Routing mechanism for Mixture of LoRA Experts, enhancing token-dependent expert all...

arXiv - AI · 4 min ·
[2507.12442] Characterizing State Space Model and Hybrid Language Model Performance with Long Context
Llms

[2507.12442] Characterizing State Space Model and Hybrid Language Model Performance with Long Context

This article explores the performance of State Space Models (SSMs) and hybrid language models in processing long-context inputs, highligh...

arXiv - Machine Learning · 4 min ·
[2503.04398] Semantic Parallelism: Redefining Efficient MoE Inference via Model-Data Co-Scheduling
Llms

[2503.04398] Semantic Parallelism: Redefining Efficient MoE Inference via Model-Data Co-Scheduling

This paper introduces Semantic Parallelism, a new paradigm for efficient MoE inference that enhances model-data co-scheduling to minimize...

arXiv - Machine Learning · 4 min ·
[2502.05310] Oracular Programming: A Modular Foundation for Building LLM-Enabled Software
Llms

[2502.05310] Oracular Programming: A Modular Foundation for Building LLM-Enabled Software

The paper introduces 'oracular programming,' a paradigm that integrates traditional computations with LLMs to enhance software reliabilit...

arXiv - AI · 4 min ·
[2508.03616] Hidden Dynamics of Massive Activations in Transformer Training
Machine Learning

[2508.03616] Hidden Dynamics of Massive Activations in Transformer Training

This paper analyzes the emergence of massive activations during transformer training, revealing predictable patterns and offering a frame...

arXiv - AI · 3 min ·
[2508.01012] AutoEDA: Enabling EDA Flow Automation through Microservice-Based LLM Agents
Llms

[2508.01012] AutoEDA: Enabling EDA Flow Automation through Microservice-Based LLM Agents

The article presents AutoEDA, a framework that utilizes microservice-based LLM agents to automate Electronic Design Automation (EDA) proc...

arXiv - AI · 4 min ·
[2602.21198] Learning from Trials and Errors: Reflective Test-Time Planning for Embodied LLMs
Llms

[2602.21198] Learning from Trials and Errors: Reflective Test-Time Planning for Embodied LLMs

This article presents a novel approach called Reflective Test-Time Planning for embodied LLMs, enabling robots to learn from mistakes thr...

arXiv - Machine Learning · 4 min ·
[2602.21189] Why Pass@k Optimization Can Degrade Pass@1: Prompt Interference in LLM Post-training
Llms

[2602.21189] Why Pass@k Optimization Can Degrade Pass@1: Prompt Interference in LLM Post-training

The paper explores the trade-off between Pass@k and Pass@1 performance metrics in large language models, revealing how optimizing for Pas...

arXiv - Machine Learning · 4 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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