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...
I keep seeing people focus heavily on prompt optimization. But in practice, a lot of failures I’ve observed don’t come from the prompt it...
Hi everyone : ) I just released a new research prototype It’s a lossless BF16 compression format that stores weights in 12 bits by replac...
![[2602.22217] RAGdb: A Zero-Dependency, Embeddable Architecture for Multimodal Retrieval-Augmented Generation on the Edge](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
The paper presents RAGdb, a novel architecture for Retrieval-Augmented Generation (RAG) that simplifies multimodal data processing by eli...
The paper presents PRAC, a novel method for compressing activations in large language models, achieving significant memory savings while ...
This article presents a multi-agent LLM framework for financial trading, emphasizing fine-grained task decomposition to enhance decision-...
This article presents a novel approach to reducing epistemic uncertainty in AI models through invariant transformation and resampling tec...
This paper presents a novel framework for modeling cardiac blood flow patterns using disease-sensitive latent interaction graphs, enhanci...
This paper evaluates the stochasticity in Deep Research Agents (DRAs), highlighting how variability in their outputs can impact research ...
This paper presents a novel approach to mitigate the 'legibility tax' in large language models by decoupling the prover-verifier game, al...
This paper explores generalization bounds for Stochastic Gradient Descent (SGD) in homogeneous neural networks, revealing that slower ste...
The paper introduces NoRA, a novel approach to Low-Rank Adaptation (LoRA) that overcomes the limitations of linear methods by utilizing m...
This article presents a Shapley-based framework for fair feature attribution in multi-output prediction, addressing the limitations of ex...
The paper presents ESAA, an architecture for autonomous agents using event sourcing to enhance state management and execution in LLM-base...
This paper explores how contextual influences affect the moral decision-making of large language models (LLMs) in scenarios akin to troll...
The paper presents a method for enhancing the performance of local large language models (LLMs) on resource-constrained edge devices thro...
The paper presents a framework for improving AI diagnostic alignment in clinical settings by preserving AI-generated reports as immutable...
The paper introduces Certified Circuits, a framework that enhances the stability and accuracy of circuit discovery in neural networks, ad...
FactGuard introduces an innovative framework for detecting video misinformation using reinforcement learning, enhancing the capabilities ...
This paper introduces a novel approach called CEEH, which combines difficulty-aware entropy regularization with reinforcement learning to...
This article presents a novel approach to address privacy heterogeneity in differentially private federated learning (DP-FL), proposing a...
FlexMS is a new framework designed for benchmarking deep learning models used in mass spectrum prediction within metabolomics, addressing...
This paper presents Layer-wise MIA-risk-aware DP-SGD, a method to reduce Membership Inference Attack risks in machine learning models by ...
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