From the 2 of 8 linked papers with an AI index.
8 papers
Benefits and Limitations of Communication in Multi-Agent Reasoning
Michael Rizvi-Martel, Satwik Bhattamishra, Neil Rathi +2
The paper introduces a theoretical framework for analyzing how communication among multiple agents affects their ability to perform complex reasoning tasks, providing bounds on req…
From Expressivity to Sample Complexity: Narrow Teachers for Transformers via C-RASP
Michael Rizvi-Martel, Satwik Bhattamishra, Guillaume Rabusseau +1
The paper derives preliminary sample complexity bounds for learning C‑RASP constructions with Transformer models, linking their expressive power to learnability.
Discovering Interpretable Algorithms by Decompiling Transformers to RASP
Xinting Huang, Aleksandra Bakalova, Satwik Bhattamishra +2
Recent work has shown that the computations of Transformers can be simulated in the RASP family of programming languages. These findings have enabled improved understanding of the…
Provably Learning Attention with Queries
Satwik Bhattamishra, Kulin Shah, Michael Hahn +1
We study the problem of learning Transformer-based sequence models with black-box access to their outputs. In this setting, a learner may adaptively query the oracle with any seque…
Hardness of Learning Regular Languages in the Next Symbol Prediction Setting
Satwik Bhattamishra, Phil Blunsom, Varun Kanade
We study the learnability of languages in the Next Symbol Prediction (NSP) setting, where a learner receives only positive examples from a language together with, for every prefix,…
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…