Why LLMs Aren't Scientists Yet? In our latest AI4Science talk, Prof. Dhruv Kumar (
@gargdhruv36
) and Dhruv Trehan (
@dhruvtrehan9
) from @lossfunk discussed how agentic LLM systems can support science in a whole new way, from generating research ideas to mentoring young researchers
@askalphaxiv
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How Agentic LLMs Are Transforming Scientific Research and Mentorship
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Solving the Loop: Attractor Models for Transformer Language and Reasoning
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“Solve the Loop: Attractor Models for Language and Reasoning” Looped Transformers can refine their thoughts internally, but they are usually unstable and tied to a fixed number of loops. So this paper turned recurrence into a fixed-point problem, where a Transformer first makes
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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