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...
Rolling coverage from San Jose, including NVIDIA CEO Jensen Huang’s keynote, news highlights, live demos and on‑the‑ground color through ...
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...
![[2602.13977] WoVR: World Models as Reliable Simulators for Post-Training VLA Policies with RL](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
The paper presents WoVR, a novel reinforcement learning framework that enhances the reliability of world models for Vision-Language-Actio...
The paper presents SpargeAttention2, a novel trainable sparse attention method that enhances the efficiency of diffusion models by combin...
AsyncVLA introduces an asynchronous control framework for robotic navigation, enhancing real-time performance by decoupling semantic reas...
This article examines the impact of AI feedback on peer reviews, revealing both benefits and challenges faced by reviewers when using an ...
This article presents a collaborative inference framework for deploying Vision Transformers on edge devices, addressing computational cha...
The paper presents HybridFlow, a two-step generative policy designed to improve robotic manipulation by enhancing real-time interaction c...
The paper presents Pailitao-VL, a multi-modal retrieval system designed for real-time industrial search, addressing key challenges in ret...
The paper presents MASFly, a novel framework for dynamic adaptation of LLM-based multi-agent systems at test time, enhancing task perform...
This article presents a novel multi-modal sensing and fusion framework for mmWave beamforming in connected vehicles, enhancing communicat...
The paper presents AISA, a novel defense mechanism for large language models (LLMs) that enhances safety against jailbreak attacks by act...
This paper introduces the concept of capability calibration for large language models (LLMs), emphasizing the importance of accurate conf...
This paper introduces a method for enhancing Causal Foundation Models (CFMs) by incorporating partial causal graph information, improving...
This article explores the role of Edge AI in biodiversity monitoring, analyzing 82 studies to assess system types, architectural trade-of...
This paper explores the algorithmic simplification of neural networks through a method called Mosaic-of-Motifs, demonstrating how structu...
This paper presents an end-to-end autoencoder framework for downlink non-orthogonal multiple access (NOMA) over Rayleigh fading channels,...
This article examines the effectiveness of large language models (LLMs) in crisis communication, particularly focusing on multilingual tr...
This article presents a novel reference-free evaluation framework for assessing the quality of flowchart image-to-code generation, utiliz...
The paper presents 'Inner Loop Inference,' a method for enhancing pretrained Transformers by iteratively refining outputs during inferenc...
D2-LoRA introduces a novel method for efficient fine-tuning in machine learning, achieving significant accuracy improvements while minimi...
The paper introduces AdaCorrection, a framework that enhances the efficiency of Diffusion Transformers by correcting cache misalignment, ...
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