RESEARCH

NLP

ARE: scaling up agent environments and evaluations

September 22, 2025

Abstract

We introduce Meta Agents Research Environments (ARE), a research platform for scalable creation of environments, integration of synthetic or real applications, and execution of agentic orchestrations. ARE provides simple abstractions to build complex and diverse environments, each with their own rules, tools, content, and verifiers, helping to bridge the gap between model development and real-world deployment. We also propose Gaia2, a benchmark built in ARE and designed to measure general agent capabilities. Beyond search and execution, Gaia2 requires agents to handle ambiguities and noise, adapt to dynamic environments, collaborate with other agents, and operate under temporal constraints. Unlike prior benchmarks, Gaia2 runs asynchronously, surfacing new failure modes that are invisible in static settings. Our experiments show that no system dominates across the intelligence spectrum: stronger reasoning often comes at the cost of efficiency, and budget scaling curves plateau, highlighting the need for new architectures and adaptive compute strategies. Perhaps more importantly, ARE abstractions enable continuous extension of Gaia2 to other environments, empowering the community to rapidly create new benchmarks tailored to their domains. In AI’s "second half", progress increasingly depends on defining meaningful tasks and robust evaluations to drive frontier capabilities forward.

Download the Paper

AUTHORS

Written by

Pierre Andrews

Amine Benhalloum

Matteo Bettini

Virginie Do

Romain Froger

Emilien Garreau

Jean-Baptiste Gaya

Hugo Laurençon

Maxime Lecanu

Kunal Malkan

Dheeraj Mekala

Gregoire Mialon

Pierre Ménard

Gerard Moreno-Torres Bertran

Ulyana Piterbarg

Mathieu Rita

Andrey Rusakov

Thomas Scialom

Mengjue Wang

Amar Budhiraja

Mike Plekhanov

Ian Yu

Publisher

arXiv

Related Publications

June 05, 2026

CONVERSATIONAL AI

RANKING AND RECOMMENDATIONS

Superintelligent Retrieval Agent: The Next Frontier of Agentic Retrieval

Zeyu Yang, Qi Ma, Jason Chen, Anshumali Shrivastava

June 05, 2026

May 26, 2026

HUMAN & MACHINE INTELLIGENCE

THEORY

Misalignment Between Backpropagation and the Hierarchy of Brain Responses to Images

Josephine Raugel, Max Seitzer, Marc Szafraniec, Huy V. Vo, Jérémy Rapin, Patrick Labatut, Piotr Bojanowski, Valentin Wyart, Jean Remi King

May 26, 2026

May 20, 2026

HUMAN & MACHINE INTELLIGENCE

RESEARCH

EgoBabyVLM: Benchmarking Cross-Modal Learning from Naturalistic Egocentric Video Data

Dongyan Lin, Phillip Rust, Angel Villar Corrales, Alvin W. M. Tan, Mahi Luthra, Charles-Eric Saint-James, Rashel Moritz, Sheila Krogh-Jespersen, Vanessa Stark, Surya Parimi, Jiayi Shen, Youssef Benchekroun, Yosuke Higuchi, Martin Gleize, Tom Fizycki, Nicolas Hamilakis, Manel Khentout, Sho Tsuji, Balázs Kégl, Juan Pino, Michael C. Frank, Emmanuel Dupoux

May 20, 2026

May 18, 2026

CONVERSATIONAL AI

RESEARCH

GIM: Evaluating models via tasks that integrate multiple cognitive domains

Rohit Patel, Alexandre Rezende, Steven McClain

May 18, 2026

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.