I-DLM: Introspective Diffusion Language Models

<a href="https://news.ycombinator.com/item?id=47762641">Comments</a>

<!-- SC_OFF --><div class="md"><p>&#x200b;</p> <p>## Abstract</p> <p>Post-training makes language models more decisive without necessarily making them more accurate — and we find

<!-- SC_OFF --><div class="md"><p>Every &quot;AI agent&quot; that controls a computer today does this: generate text → parse it → execute → serialize result back to text → repeat.

<!-- SC_OFF --><div class="md"><p>Autoregressive LLM world models factorize next-state generation left-to-right, preventing them from conditioning on globally interdependent

<p>Today Inception Labs dropped <strong>Mercury 3</strong>: a diffusion-based LLM with a 1M-token context window doing <strong>1,068 tokens/sec on NVIDIA GPUs</strong>, claiming

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1u3f9hs/talk_text_diffusion_google_deepminds_brendan/"> <img alt="[Talk] Text Diffusion — Google DeepMind's

arXiv:2605.04215v1 Announce Type: cross Abstract: Diffusion-based Large Language Models (D-LLMs) represent a promising frontier in generative AI, offering fully parallel token

<!-- SC_OFF --><div class="md"><p><strong>Noam Brown</strong> — OpenAI Research Scientist and co-creator of o1, Libratus, and Cicero — sat down with Dwarkesh Patel for a 1h 20m