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20242026
most citedLong-Horizon AI Research for Grothendieck Constant: A Case Study in Human-AI Mathematical Collaboration

1 citations · 2 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CL2026

Multi-Target Few-Shot Prompting with Diffusion Language Models

Anton Xue, Alan Li, Rahul Saha

Few-shot prompting is useful when instructions and rewards are hard to specify. One such application is editing by example, where a demonstrated formatting change or API migration…

cs.AI2026★ 1 cited

Long-Horizon AI Research for Grothendieck Constant: A Case Study in Human-AI Mathematical Collaboration

Alan Li, Rahul Saha, Anton Xue +4

AI agents are increasingly used in mathematics research, but it is often unclear how to use them effectively. Towards this, we present an extensive case study of how AI was used to…

cs.CC2026★ 1 cited

New Lower and Upper Bounds for the Grothendieck Constant

Rahul Saha, Alan Li, Anton Xue +4

We establish new bounds on the Grothendieck constant : \[ \frac{6π}{11} \le K_G \le \fracπ{2\log(1+\sqrt2)} - 10^{-4}. \] Methodologically, our lower bound approach differs fr…

cs.DS2026

The Grothendieck Constant is Less Than

Alan Li, Rahul Saha, Anton Xue +4

We prove that the Grothendieck constant . This improves on the work of Braverman, Makarychev, Makarychev, and Naor (2011), who proved…

cs.LG2025

The Transformer Cookbook

Andy Yang, Christopher Watson, Anton Xue +6

We present the transformer cookbook: a collection of techniques for directly encoding algorithms into a transformer's parameters. This work addresses the steep learning curve of su…

cs.LG2025

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning

Thomas T. Zhang, Behrad Moniri, Ansh Nagwekar +4

Layer-wise preconditioning methods are a family of memory-efficient optimization algorithms that introduce preconditioners per axis of each layer's weight tensors. These methods ha…

cs.AI2024

Logicbreaks: A Framework for Understanding Subversion of Rule-based Inference

Anton Xue, Avishree Khare, Rajeev Alur +2

We study how to subvert large language models (LLMs) from following prompt-specified rules. We first formalize rule-following as inference in propositional Horn logic, a mathematic…