Alex Pentland — accepted AI-conference papers

2026

ATLAS: Adaptive Transfer Scaling Laws for Multilingual Pretraining, Finetuning, and Decoding the Curse of Multilinguality

Shayne Longpre, Sneha Kudugunta, Niklas Muennighoff, I-Hung Hsu, Isaac Rayburn Caswell, Alex Pentland +3

ICLR 2026poster

Scaling laws research has focused overwhelmingly on English—yet the most prominent AI models explicitly serve billions of international users. In this work, we undertake the largest multilingual scaling laws study to date, totaling 774 multilingual training experiments, spanning 10M-8B model paramet…

Cited by 0 Source ↗Scholar ↗

2026

Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks

Zexue He, Yu Wang, Churan Zhi, Yuanzhe Hu, Tzu-Ping Chen, Lang Yin +8

ICML 2026poster

Existing evaluations of agents with memory typically assess **memorization** and **action** in isolation. One class of benchmarks evaluates memorization by testing recall of past conversations or text but fails to capture how memory is used to guide future decisions. Another class focuses on agents …

Cited by 0 Source ↗Scholar ↗

2026

FLARE-AI: Flaw Reporting for AI

Shayne Longpre, Elaine Zhu, Carson Ezell, Avijit Ghosh, Sean McGregor, Kevin Paeth +12

ICML 2026poster

Flaw reporting for deployed AI systems is fundamental to identifying system failures and improving AI safety. Yet the AI reporting ecosystem is fragmented: researchers who identify flaws often do not know what or where to report, and groups who receive reports rarely share them with other relevant s…

Cited by 0 Source ↗Scholar ↗

2026

Position: AI Should Facilitate Democratic Deliberation at Scale

José Ramón Enríquez, Jiaxin Pei, Alex Pentland

ICML 2026oral

AI systems can strengthen democracy by supporting deliberation at scale by addressing cognitive, social, platform-design, and market-driven frictions, while preserving human agency. Unlike proposals such as liquid democracy that restructure representation through vote delegation, in this position pa…

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2025

Bridging the Data Provenance Gap Across Text, Speech, and Video

Shayne Longpre, Nikhil Singh, Manuel Cherep, Kushagra Tiwary, Joanna Materzynska, William Brannon +37

ICLR 2025poster

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 datasets beyond text. In this work we conduct the largest and first-of-its-kind longitudinal audit across modalities --- pop…

Cited by 1 Source ↗PDF ↗Scholar ↗

2025

Position: AI Agents Need Authenticated Delegation

Tobin South, Samuele Marro, Thomas Hardjono, Robert Mahari, Cedric Deslandes Whitney, Alan Chan +1

ICML 2025oral

The rapid deployment of autonomous AI agents creates urgent challenges in the areas of authorization, accountability, and access control in task delegation. This position paper argues that authenticated and auditable delegation of authority to AI agents is a critical component of mitigating practica…

Cited by 0 Source ↗PDF ↗Scholar ↗

2025

ReCAP: Recursive Context-Aware Reasoning and Planning for Large Language Model Agents

Zhenyu Zhang, Tianyi Chen, Weiran Xu, Alex Pentland, Jiaxin Pei

NeurIPS 2025poster

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 drift, loss of goal information, and recurrent failure cycles, while hierarchical prompting methods often weaken cross-leve…

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2024

Consent in Crisis: The Rapid Decline of the AI Data Commons

Shayne Longpre, Robert Mahari, Ariel N. Lee, Campbell S. Lund, Hamidah Oderinwale, William Brannon +43

NeurIPS 2024poster

General-purpose artificial intelligence (AI) systems are built on massive swathes of public web data, assembled into corpora such as C4, RefinedWeb, and Dolma. To our knowledge, we conduct the first, large-scale, longitudinal audit of the consent protocols for the web domains underlying AI training…

Cited by 36 Source ↗Scholar ↗

2024

LePaRD: A Large-Scale Dataset of Judicial Citations to Precedent

Robert Mahari, Dominik Stammbach, Elliott Ash, Alex Pentland

ACL 2024long

We present the Legal Passage Retrieval Dataset, LePaRD. LePaRD contains millions of examples of U.S. federal judges citing precedent in context. The dataset aims to facilitate work on legal passage retrieval, a challenging practice-oriented legal retrieval and reasoning task. Legal passage retrieval…

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2024

Leveraging Large Language Models for Learning Complex Legal Concepts through Storytelling

Hang Jiang, Xiajie Zhang, Robert Mahari, Daniel Kessler, Eric Ma, Tal August +5

ACL 2024long

Making legal knowledge accessible to non-experts is crucial for enhancing general legal literacy and encouraging civic participation in democracy. However, legal documents are often challenging to understand for people without legal backgrounds. In this paper, we present a novel application of large…

Cited by 13 Source ↗PDF ↗code ↗Scholar ↗

2024

Position: A Safe Harbor for AI Evaluation and Red Teaming

Shayne Longpre, Sayash Kapoor, Kevin Klyman, Ashwin Ramaswami, Rishi Bommasani, Borhane Blili-Hamelin +17

ICML 2024oral

Independent evaluation and red teaming are critical for identifying the risks posed by generative AI systems. However, the terms of service and enforcement strategies used by prominent AI companies to deter model misuse have disincentives on good faith safety evaluations. This causes some researcher…

Cited by 5 Source ↗PDF ↗Scholar ↗

2024

Position: Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them?

Shayne Longpre, Robert Mahari, Naana Obeng-Marnu, William Brannon, Tobin South, Katy Ilonka Gero +2

ICML 2024spotlight

New capabilities in foundation models are owed in large part to massive, widely-sourced, and under-documented training data collections. Existing practices in data collection have led to challenges in tracing authenticity, verifying consent, preserving privacy, addressing representation and bias, re…

Cited by 3 Source ↗PDF ↗Scholar ↗

2023

The Law and NLP: Bridging Disciplinary Disconnects

Robert Mahari, Dominik Stammbach, Elliott Ash, Alex Pentland

EMNLP 2023short findings

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 same time, the legal system is experiencing an access to justice crisis, which could be partially alleviated with NLP. I…

Cited by 0 Source ↗Scholar ↗

2021

Adaptive Methods for Real-World Domain Generalization

Abhimanyu Dubey, Vignesh Ramanathan, Alex Pentland, Dhruv Mahajan

CVPR 2021poster

Invariant approaches have been remarkably successful in tackling the problem of domain generalization, where the objective is to perform inference on data distributions different from those used in training. In our work, we investigate whether it is possible to leverage domain information from the u…

Cited by 113 PDF ↗Scholar ↗

2021

One More Step Towards Reality: Cooperative Bandits with Imperfect Communication

Udari Madhushani, Abhimanyu Dubey, Naomi Leonard, Alex Pentland

NeurIPS 2021poster

The cooperative bandit problem is increasingly becoming relevant due to its applications in large-scale decision-making. However, most research for this problem focuses exclusively on the setting with perfect communication, whereas in most real-world distributed settings, communication is often over…

Cited by 28 Source ↗PDF ↗Scholar ↗

2020

Learning Quadratic Games on Networks

Yan Leng, Xiaowen Dong, Junfeng Wu, Alex Pentland

ICML 2020poster

Individuals, or organizations, cooperate with or compete against one another in a wide range of practical situations. Such strategic interactions are often modeled as games played on networks, where an individual’s payoff depends not only on her action but also on that of her neighbors. The current…

Cited by 28 Source ↗PDF ↗Scholar ↗

2019

Urban Swarms: A new approach for autonomous waste management

Antonio Luca Alfeo, Eduardo Castelló Ferrer, Yago Lizarribar Carrillo, Arnaud Grignard, Luis Alonso Pastor, Dylan T. Sleeper +6

ICRA 2019poster

Modern cities are growing ecosystems that face new challenges due to the increasing population demands. One of the many problems they face nowadays is waste management, which has become a pressing issue requiring new solutions. Swarm robotics systems have been attracting an increasing amount of atte…

Cited by 60 Source ↗Scholar ↗