activity
20222026
most citedVerifiable evaluations of machine learning models using zkSNARKs

7 citations · 17 across the 17 of their papers we have counts for

collaborators
Showing 2025Show all

8 papers · 1 filter

cs.CR2025

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…

cs.AI2025

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…

cs.CL2025★ 1 cited

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…

cs.AI2025

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…

cs.AI2025★ 1 cited

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…

cs.CY2025

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…

cs.CY2025

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,…

cs.CY2025★ 1 cited

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…