GPUs, training clusters, MLOps, and deployment
UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...
Hey all, Our ML team spent some time this week getting training and deployments working for Gemma-4, and wanted to document all the thing...
I am a solo developer who has been using all three seriously. Here is what I actually think: GPT-4o — Strengths: Large context window, st...
![[2602.14233] Evaluating LLMs in Finance Requires Explicit Bias Consideration](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
This paper discusses the need for explicit bias consideration in evaluating Large Language Models (LLMs) used in finance, identifying fiv...
The paper presents the Hybrid Secure Routing Protocol (HSRP) for Mobile Ad-hoc Networks (MANETs), addressing security challenges through ...
This article presents a novel framework called Adversarial Network Imagination, which leverages Causal Large Language Models and Digital ...
This paper explores optimal batch size scheduling in deep learning, revealing that task difficulty influences the effectiveness of batch ...
This study explores AI-assisted channel adaptation in UAV-enabled cellular networks, focusing on the impact of adaptive channel control o...
The paper discusses the development of a Deep Research AI agent, Bioptic Agent, designed for drug asset scouting, particularly in non-U.S...
This paper evaluates the effectiveness of malicious prompt classifiers under true distribution shifts, revealing significant performance ...
This paper presents two novel regularization losses for enhancing the specialization of Sparse Mixture-of-Experts (MoE) models, improving...
The paper presents ReusStdFlow, a framework designed to enhance the reusability of workflows in Agentic AI by standardizing Domain Specif...
The ROAST technique enhances the control of large language models by utilizing on-distribution rollouts for more effective activation ste...
This paper presents a novel approach to lifted relational probabilistic inference, integrating inductive learning and deductive reasoning...
This paper presents a novel approach, Adaptive Entropy Annealing (aEPG), to enhance continual fine-tuning of large pretrained vision mode...
The paper presents EmbeWebAgent, a framework for embedding web agents into existing user interfaces, enhancing their robustness and actio...
The paper explores decentralized federated learning (DFL) using energy harvesting devices, addressing battery depletion issues and propos...
The article presents UniST-Pred, a novel framework for spatio-temporal traffic forecasting that effectively addresses disruptions in tran...
This paper explores the distinction between deception and hallucination failures in large language models (LLMs), proposing a mechanism-o...
The paper presents iML, a multi-agent framework for automated machine learning that enhances transparency and modularity, addressing limi...
This study explores the efficacy of reasoning traces in neural networks, introducing a large dataset to assess how well models generalize...
Pawsterior introduces a variational flow-matching framework to enhance simulation-based inference (SBI), addressing constraints in struct...
The paper presents AutoWebWorld, a framework that synthesizes verifiable web environments using Finite State Machines, enhancing the trai...
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