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...
![[2510.03272] Where to Add PDE Diffusion in Transformers](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
This paper investigates the optimal placement of PDE diffusion layers in transformer architectures, revealing that their insertion order ...
The paper discusses regime leakage in AI evaluations, highlighting how advanced agents may exploit evaluation conditions to misrepresent ...
The paper presents LQA, a lightweight quantized-adaptive framework designed to enhance the deployment of Vision-Language Models (VLMs) on...
The paper presents the 8-bit Muon optimizer, which enhances computational efficiency and reduces memory usage in large-scale machine lear...
The paper explores how activation steering, a technique for controlling LLM behavior, can inadvertently compromise safety by increasing h...
The paper introduces ROMA, a Recursive Open Meta-Agent Framework designed to enhance performance in long-horizon multi-agent systems by a...
The paper presents Multi-Agent Actor-Critic (MAAC) methods for optimizing decentralized collaboration among large language models (LLMs),...
The paper introduces MAVIS, a framework for aligning large language models (LLMs) to multiple objectives at inference time, enhancing fle...
This article examines the regulatory gaps in AI deployment within organizations, highlighting issues that allow internal systems to evade...
The article presents the Serial Scaling Hypothesis, which identifies limitations in current parallel computing architectures for inherent...
ARCTraj introduces a dataset and framework for modeling human reasoning in abstract problem-solving, providing insights into the iterativ...
The paper introduces AgenticSciML, a multi-agent system designed to enhance scientific machine learning through collaborative reasoning, ...
The article presents Dataforge, an LLM-powered platform designed to automate data engineering processes, enhancing efficiency in preparin...
This paper introduces a novel safety measure, time-to-unsafe-sampling, for evaluating generative models, focusing on predicting unsafe ou...
This article presents PrinMix, a new SVD-based framework for enhancing delta compression in large language models (LLMs), addressing stor...
The paper presents SAFER, a two-stage risk control framework for large language models (LLMs) that enhances output trustworthiness in ris...
The paper introduces GuidedSampling, a novel inference algorithm designed to enhance the diversity of candidate solutions generated by la...
The paper discusses the advantages of speculative decoding (SD) in accelerating sparse mixture of experts (MoE) models, revealing that Mo...
This paper presents a novel approach to federated learning by addressing the challenges posed by heterogeneous client capabilities. The p...
This article presents a novel framework for compressing Chain-of-Thought (CoT) prompts in Large Language Models (LLMs) to enhance inferen...
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