Introspective Diffusion Language Models

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&#32; submitted by &#32; <a href="https://www.reddit.com/user/nnxnnx"> /u/nnxnnx </a> <br /> <span><a href="https://introspective-diffusion.github.io/">[link]</a></span> &#32;

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<!-- 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.

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<!-- 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

<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

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