Psychology Today

Very Awkward: AI Prompt Slips in the Age of 'Chatfishing'

As more and more people are using AI apps daily, reports of an embarrassing new phenomenon pop up all over social media: ...
Tech Times

Speech Recognition Accuracy Score Hides Its Worst Errors: Semantic Metrics Offer a Fix

Speech recognition accuracy benchmarks report low error rates while leaving the most critical words wrong. Researchers now ...
Investopedia

Understanding Rounding Errors: Causes, Consequences, and Examples

Will Kenton is an expert on the economy and investing laws and regulations. He previously held senior editorial roles at Investopedia and Kapitall Wire and holds a MA in Economics from The New School ...
Hackaday

TurboQuant: Reducing LLM Memory Usage With Vector Quantization

Large language models (LLMs) aren’t actually giant computer brains. Instead, they are massive vector spaces in which the probabilities of tokens occurring in a specific order is encoded. Billions of ...
GitHub

[CVPR 2026] Fine-Grained Post-Training Quantization for Large Vision Language Models with Quantization-Aware Integrated Gradients

Ziwei Xiang*, Fanhu Zeng*, Hongjian Fang*, Rui-Qi Wang, Renxing Chen, Yanan Zhu, Yi Chen, Peipei Yang, Xu-Yao Zhang TL;DR: We introduce a fine-grained post-training quantization method for large ...
IEEE

Towards Accurate Post-Training Quantization of Vision Transformers via Error Reduction

Abstract: Post-training quantization (PTQ) for vision transformers (ViTs) has received increasing attention from both academic and industrial communities due to its minimal data needs and high time ...