Transformers learn to implement preconditioned gradient descent for in-context learning (2023 · 37 citations)
The paper asks whether transformers merely can represent optimization algorithms or whether ordinary training actually drives them to do so.
Linear attention is (maybe) all you need (to understand transformer optimization) (2023 · 10 citations)
Training Transformers is notoriously hard. Plain SGD lags well behind adaptive optimizers such as Adam, and the loss landscape shows unusual properties that classical optimization theory does not account for. Full Transformers are too complex to analyze directly, so the authors look for the simplest model that still…
ChemVLR: Prioritizing Reasoning in Perception for Chemical Vision-Language Understanding (2026)
ChemVLR addresses a gap in chemical vision-language modeling. Existing chemical VLMs such as ChemVLM and TinyChemVL map images of molecules or reactions directly to answers. They give no interpretable rationale, and they make little use of the LLM's latent chemical knowledge. ChemVLR instead reasons explicitly during…

Co-authors here

7 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

Papers of our library cite their works 38 times (of the 149,439 papers whose bibliographies we read); 12 citing papers with a page here →.

Updated 3 November 2026