GPUs, training clusters, MLOps, and deployment
Hey guys, I'm an independent researcher working on a project that tries to address a very specific failure mode in LLMs and embedding bas...
I built a cognitive architecture where all computation reduces to three bit operations: XOR, MAJ, POPCNT. No GEMM. No GPU. No floating-po...
I'm profoundly ambivalent re: how to feel about this; is it great -- what a scrappy, bold pivot! Or wildly dumb - its so far from their c...
![[2506.02634] KVCache Cache in the Wild: Characterizing and Optimizing KVCache Cache at a Large Cloud Provider](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
This paper characterizes and optimizes KVCache, a caching mechanism for large language model (LLM) serving at a major cloud provider, hig...
This paper presents a novel algorithm for training resistive networks using Generalized Equilibrium Propagation, aiming to enhance energy...
This article presents a novel graph transformer model, incorporating cardinality-preserving attention channels, to enhance molecular prop...
The paper presents SwiftRepertoire, a framework for synthesizing immune signatures using few-shot learning techniques, enabling efficient...
The paper presents Caprese, a low-rank distillation method designed to enhance reasoning capabilities in large language models (LLMs) whi...
The paper presents SCOPE, a novel routing framework for language models that dynamically predicts cost and performance, enhancing efficie...
This article evaluates CPU-intensive stream data processing in edge computing systems, highlighting performance and power consumption opt...
This paper introduces the Halo Architecture, a new framework for infinite-depth reasoning using rational arithmetic, aiming to enhance th...
The paper presents a novel reinforcement learning framework for unlearning targeted concepts in text-to-image diffusion models, enhancing...
This paper explores the use of Kolmogorov-Arnold Networks (KAN) for predicting flow delays in communication networks, enhancing efficienc...
This paper presents a novel method using generative adversarial training to address reward hacking in real-time human-AI music interactio...
The paper presents EGGROLL, an enhanced Evolution Strategy for optimizing large-scale models, achieving significant speed improvements an...
The paper presents PAC, a collaborative edge computing framework designed for resource-efficient fine-tuning of personal large language m...
The paper explores how algorithmic primitives and compositional geometry can enhance reasoning capabilities in large language models (LLM...
This article discusses the challenges and requirements for benchmarking Time Series Foundation Models (TSFMs), highlighting issues of inf...
This article presents an experimental evaluation of ROS-Causal, a framework for causal discovery in human-robot spatial interactions, dem...
The paper introduces Bridged Clustering, a semi-supervised framework that learns predictors from unpaired datasets by clustering inputs a...
This article explores the phenomenon of 'attention collapse' in large language models (LLMs) and introduces Inheritune, a method for crea...
The paper presents RACE Attention, a novel linear-time attention mechanism designed for long-sequence training, significantly improving e...
The paper presents TKN, a transformer-based neural network designed for real-time video prediction, achieving a remarkable prediction rat...
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