7 citations · 17 across the 17 of their papers we have counts for
18 papers
Permission Manifests for Web Agents
Samuele Marro, Alan Chan, Xinxing Ren +12
The rise of Large Language Model (LLM)-based web agents represents a significant shift in automated interactions with the web. Unlike traditional crawlers that follow simple conven…
Identity Management for Agentic AI: The new frontier of authorization, authentication, and security for an AI agent world
Tobin South, Subramanya Nagabhushanaradhya, Ayesha Dissanayaka +18
The rapid rise of AI agents presents urgent challenges in authentication, authorization, and identity management. Current agent-centric protocols (like MCP) highlight the demand fo…
ReCAP: Recursive Context-Aware Reasoning and Planning for Large Language Model Agents
Zhenyu Zhang, Tianyi Chen, Weiran Xu +2
Long-horizon tasks requiring multi-step reasoning and dynamic re-planning remain challenging for large language models (LLMs). Sequential prompting methods are prone to context dri…
Can AI Truly Represent Your Voice in Deliberations? A Comprehensive Study of Large-Scale Opinion Aggregation with LLMs
Shenzhe Zhu, Shu Yang, Michiel A. Bakker +2
Large-scale public deliberations generate thousands of free-form contributions that must be synthesized into representative and neutral summaries for policy use. While LLMs have be…
The Automated but Risky Game: Modeling and Benchmarking Agent-to-Agent Negotiations and Transactions in Consumer Markets
Shenzhe Zhu, Jiao Sun, Yi Nian +3
AI agents are increasingly used in consumer-facing applications to assist with tasks such as product search, negotiation, and transaction execution. In this paper, we explore a fut…
Analyzing sequential activity and travel decisions with interpretable deep inverse reinforcement learning
Yuebing Liang, Shenhao Wang, Jiangbo Yu +3
Travel demand modeling has shifted from aggregated trip-based models to behavior-oriented activity-based models because daily trips are essentially driven by human activities. To a…
Measuring risks inherent to our digital economies using Amazon purchase histories from US consumers
Alex Berke, Kent Larson, Sandy Pentland +1
What do pickles and trampolines have in common? In this paper we show that while purchases for these products may seem innocuous, they risk revealing clues about customers' persona…
Evaluating Amazon Effects and the Limited Impact of COVID-19 With Purchases Crowdsourced from US Consumers
Alex Berke, Dana Calacci, Alex +2
We leverage a recently published dataset of Amazon purchase histories, crowdsourced from thousands of US consumers, to study how online purchasing behaviors have changed over time,…
Authenticated Delegation and Authorized AI Agents
Tobin South, Samuele Marro, Thomas Hardjono +5
The rapid deployment of autonomous AI agents creates urgent challenges around authorization, accountability, and access control in digital spaces. New standards are needed to know…
Bridging the Data Provenance Gap Across Text, Speech and Video
Shayne Longpre, Nikhil Singh, Manuel Cherep +40
Progress in AI is driven largely by the scale and quality of training data. Despite this, there is a deficit of empirical analysis examining the attributes of well-established data…
Temporal fingerprints: Identity matching across fully encrypted domain
Shahar Somin, Keeley Erhardt, Alex 'Sandy' Pentland
Technological advancements have significantly transformed communication patterns, introducing a diverse array of online platforms, thereby prompting individuals to use multiple pro…
Leveraging Large Language Models for Learning Complex Legal Concepts through Storytelling
Hang Jiang, Xiajie Zhang, Robert Mahari +8
Making legal knowledge accessible to non-experts is crucial for enhancing general legal literacy and encouraging civic participation in democracy. However, legal documents are ofte…
Verifiable evaluations of machine learning models using zkSNARKs
Tobin South, Alexander Camuto, Shrey Jain +5
In a world of increasing closed-source commercial machine learning models, model evaluations from developers must be taken at face value. These benchmark results-whether over task…
Don't forget private retrieval: distributed private similarity search for large language models
Guy Zyskind, Tobin South, Alex Pentland
While the flexible capabilities of large language models (LLMs) allow them to answer a range of queries based on existing learned knowledge, information retrieval to augment genera…
zkTax: A pragmatic way to support zero-knowledge tax disclosures
Alex Berke, Tobin South, Robert Mahari +2
Tax returns contain key financial information of interest to third parties: public officials are asked to share financial data for transparency, companies seek to assess the financ…
LePaRD: A Large-Scale Dataset of Judges Citing Precedents
Robert Mahari, Dominik Stammbach, Elliott Ash +1
We present the Legal Passage Retrieval Dataset LePaRD. LePaRD is a massive collection of U.S. federal judicial citations to precedent in context. The dataset aims to facilitate wor…
The Law and NLP: Bridging Disciplinary Disconnects
Robert Mahari, Dominik Stammbach, Elliott Ash +1
Legal practice is intrinsically rooted in the fabric of language, yet legal practitioners and scholars have been slow to adopt tools from natural language processing (NLP). At the…
Building a healthier feed: Private location trace intersection driven feed recommendations
Tobin South, Nick Lothian, Alex "Sandy" Pentland
The physical environment you navigate strongly determines which communities and people matter most to individuals. These effects drive both personal access to opportunities and the…