57% of enterprises have watched AI agents be confidently wrong. The fix is an agentic context layer, but who has one?

A VB Pulse survey of 101 enterprises finds 57% traced a wrong AI agent answer to bad context, and only 25% have a governed context layer in production.
GitHub

got-r1_unleashing_reasoning_capability_of_autoregressive_visual_generation_with_.md

description [ICLR 2026][Image Generation][Autoregressive Image Generation] GoT-R1 transfers the success of "exploring reasoning strategies via Reinforcement Learning" (like GRPO in LLMs) to ...
GitHub

BitDance: Scaling Autoregressive Generative Models with Binary Tokens

For visual generation, discrete autoregressive models often struggle with poor tokenizer reconstruction, difficulties in sampling from large vocabularies, and slow token-by-token generation speeds. We ...
IEEE

A CTC Alignment-Based Non-Autoregressive Transformer for End-to-End Automatic Speech Recognition

Abstract: Recently, end-to-end models have been widely used in automatic speech recognition (ASR) systems. Two of the most representative approaches are connectionist temporal classification (CTC) and ...
IEEE

CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning From Sporadic Temporal Data

Abstract: Learning temporal patterns from multivariate longitudinal data is challenging especially in cases when data is sporadic, as often seen in, e.g., healthcare applications where the data can ...