IDE Publications

Brendan Lucier | Microsoft Research

Nicole Immorlica | Yale University, Microsoft Research

Mert Demirer | MIT

This paper develops a model that predicts that (1) AI-executed steps co-occur in chains, (2) dispersion of AI-exposed steps lowers AI execution at the job level, and (3) adjacency to AI-executed steps increases the likelihood that a step is AI-executed.

Jim Sterne

This paper proposes a systematic framework for measuring the decentralization of crypto ecosystems over time and compare commonly used decentralization metrics. 

Daniel Yue

The result uncovered in this working paper suggest that closed model dominance reflects powerful drivers beyond model capabilities and price – whether switching costs, brand loyalty, or information frictions – with the economic magnitude of these hidden factors proving far larger than previously recognized, reframing open models as a largely latent, but high-potential, source of value in the AI economy.

Hayden Rome

Jeffery Li

Chirag Falor

In this paper, scholars present the first broad survey of how algorithmic progress has improved memory usage (space complexity) by analyzing 118 of the most important algorithm problems in computer science, reviewing the 800+ algorithms used to solve them.

Alex Fogelson

Understanding the intent behind scientific citations is critical for advancing scholarly search and knowledge mapping. This paper reflects on the methodological use of large language models (LLMs) for multi-class citation intent classification.

Ryan Steed
Terrance Liu
Zhiwei Steven Wu

This paper explores differential privacy, an increasingly popular tool for preserving individuals’ privacy by adding statistical uncertainty when sharing sensitive data. 

This paper provides insights on privacy policies and regulations, the effects of those policies and potential future action to protect consumer data privacy.