GPUs, training clusters, MLOps, and deployment
there's this network that launched recently routing crypto mining hardware toward AI training workloads. miners seem happy with the econo...
![[2604.01989] Attention at Rest Stays at Rest: Breaking Visual Inertia for Cognitive Hallucination Mitigation](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
Abstract page for arXiv paper 2604.01989: Attention at Rest Stays at Rest: Breaking Visual Inertia for Cognitive Hallucination Mitigation
Abstract page for arXiv paper 2512.18809: FedVideoMAE: Efficient Privacy-Preserving Federated Video Moderation
The paper presents Landscaper, an open-source Python package for analyzing loss landscapes in neural networks using multi-dimensional top...
The paper discusses the systemic risks posed by algorithmic collisions in interconnected AI systems, highlighting the need for improved g...
The paper presents BitHydra, a framework for executing bit-flip inference cost attacks on large language models (LLMs), demonstrating how...
This paper explores the balance between watermark strength and speculative sampling efficiency in language models, proposing a new approa...
The paper presents a novel method for post-training quantization (PTQ) of diffusion models, addressing inefficiencies in existing calibra...
This paper presents a zero-shot reinforcement learning framework for occlusion-aware plant manipulation, achieving high success rates in ...
This paper explores a bi-level online provisioning and scheduling problem, focusing on network resource allocation with varying constrain...
This paper presents a novel framework for inverting Self-Organizing Maps (SOMs) to recover original inputs from activation patterns, intr...
The paper presents Clust-PSI-PFL, a novel framework for personalized federated learning that addresses challenges posed by non-IID data t...
This article introduces ECHO, a benchmark for evaluating long-range graph propagation in graph neural networks (GNNs), addressing a criti...
This paper presents SiLU network constructions that optimize approximation efficiency for basic operations, particularly the square funct...
The paper presents FedCoLLM, a federated co-tuning framework that enhances the performance of both Large Language Models (LLMs) and Small...
The paper presents E2E-GRec, a novel end-to-end framework that integrates Graph Neural Networks (GNNs) with recommender systems, addressi...
This paper explores how autoregressive large language models (LLMs) assess thematic fit in event representation, achieving state-of-the-a...
The paper presents InTAct, a novel method for continual learning that mitigates catastrophic forgetting by using interval-based task acti...
The paper presents PASS, a novel algorithmic framework that utilizes visual prompts to enhance structural sparsity in neural networks, im...
This paper explores the theoretical framework behind normalization layers in neural networks, demonstrating their role in controlling cap...
This paper presents Distribution Matching Policy Optimization (DMPO), a novel reinforcement learning method aimed at enhancing reasoning ...
The paper introduces a new dimensionless data-quality parameter for language model pretraining, establishing a quality-aware scaling law ...
The paper introduces KVComm, a novel framework for efficient communication between Large Language Models (LLMs) using selective KV pair s...
Get the latest news, tools, and insights delivered to your inbox.
Daily or weekly digest • Unsubscribe anytime