read more: https://
alphaxiv.org/abs/2605.10938
@askalphaxiv
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ELF: A New Approach to Diffusion Language Models in Embedding Space
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“ELF: Embedded Language Flows” Most diffusion LMs still treat language as discrete tokens, or keep forcing continuous states back into token space during generation. This paper runs diffusion almost entirely in continuous embedding space, then convert to tokens only at the
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Using AI assistants for LaTeX formula extraction in research papers
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Need a quick way to extract a formula?
— alphaXiv (@askalphaxiv) 13 mai 2026
alphaXiv’s assistant actually supports LaTeX generation!
Just simply highlight the equations in the paper, and ask the assistant for the LaTeX code, then copy it to the workspace of your choice. pic.twitter.com/mQd0a0w6VzNeed a quick way to extract a formula? alphaXiv’s assistant actually supports LaTeX generation! Just simply highlight the equations in the paper, and ask the assistant for the LaTeX code, then copy it to the workspace of your choice.
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Technical Analysis of Attention Drift in Speculative Decoding Models
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“Attention Drift: What Autoregressive Speculative Decoding Models Learn” Speculative decoding makes LLM inference faster, but drafters break under small template changes and long context. But why? This paper shows that as the drafter predicts more tokens, its attention drifts
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Improving LLM Embedding Representations via Mean-Pooling of Generated Tokens
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“The Truth Lies Somewhere in the Middle of the Generated Tokens” LLMs don’t store the meaning of a prompt in one hidden state. As they generate, the meaning gets spread across many token embeddings. So this paper propose a mean-pool over generated token embeddings instead of
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Open Source Reinforcement Learning Training Scripts Released
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You can also check out our training scripts here: https://
github.com/NovaSky-AI/Sky
RL/tree/main/examples/train/rlm
… Thanks to @sumanthrh and @charlie_ruan from the SkyRL team for their responsiveness in resolving issues and for providing an amazing RL library for the community! We'd also like to thank @a1zhang for his -
Reinforcing Recursive Language Models for Long-Context Tasks
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Reinforcing Recursive Language Models Can a 4B model learn to recursively call itself to answer hard long-context questions? We RL fine-tuned a small model to behave as a native RLM. On evidence selection across scientific papers, our 4B RLM matches Sonnet 4.6 in quality