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| Format: | Preprint |
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2026
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| Online-Zugang: | https://arxiv.org/abs/2602.11964 |
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| author | Froger, Romain Andrews, Pierre Bettini, Matteo Budhiraja, Amar Cabral, Ricardo Silveira Do, Virginie Garreau, Emilien Gaya, Jean-Baptiste Laurençon, Hugo Lecanu, Maxime Malkan, Kunal Mekala, Dheeraj Ménard, Pierre Bertran, Gerard Moreno-Torres Piterbarg, Ulyana Plekhanov, Mikhail Rita, Mathieu Rusakov, Andrey Vorotilov, Vladislav Wang, Mengjue Yu, Ian Benhalloum, Amine Mialon, Grégoire Scialom, Thomas |
| author_facet | Froger, Romain Andrews, Pierre Bettini, Matteo Budhiraja, Amar Cabral, Ricardo Silveira Do, Virginie Garreau, Emilien Gaya, Jean-Baptiste Laurençon, Hugo Lecanu, Maxime Malkan, Kunal Mekala, Dheeraj Ménard, Pierre Bertran, Gerard Moreno-Torres Piterbarg, Ulyana Plekhanov, Mikhail Rita, Mathieu Rusakov, Andrey Vorotilov, Vladislav Wang, Mengjue Yu, Ian Benhalloum, Amine Mialon, Grégoire Scialom, Thomas |
| contents | We introduce Gaia2, a benchmark for evaluating large language model agents in realistic, asynchronous environments. Unlike prior static or synchronous evaluations, Gaia2 introduces scenarios where environments evolve independently of agent actions, requiring agents to operate under temporal constraints, adapt to noisy and dynamic events, resolve ambiguity, and collaborate with other agents. Each scenario is paired with a write-action verifier, enabling fine-grained, action-level evaluation and making Gaia2 directly usable for reinforcement learning from verifiable rewards. Our evaluation of state-of-the-art proprietary and open-source models shows that no model dominates across capabilities: GPT-5 (high) reaches the strongest overall score of 42% pass@1 but fails on time-sensitive tasks, Claude-4 Sonnet trades accuracy and speed for cost, Kimi-K2 leads among open-source models with 21% pass@1. These results highlight fundamental trade-offs between reasoning, efficiency, robustness, and expose challenges in closing the "sim2real" gap. Gaia2 is built on a consumer environment with the open-source Agents Research Environments platform and designed to be easy to extend. By releasing Gaia2 alongside the foundational ARE framework, we aim to provide the community with a flexible infrastructure for developing, benchmarking, and training the next generation of practical agent systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_11964 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments Froger, Romain Andrews, Pierre Bettini, Matteo Budhiraja, Amar Cabral, Ricardo Silveira Do, Virginie Garreau, Emilien Gaya, Jean-Baptiste Laurençon, Hugo Lecanu, Maxime Malkan, Kunal Mekala, Dheeraj Ménard, Pierre Bertran, Gerard Moreno-Torres Piterbarg, Ulyana Plekhanov, Mikhail Rita, Mathieu Rusakov, Andrey Vorotilov, Vladislav Wang, Mengjue Yu, Ian Benhalloum, Amine Mialon, Grégoire Scialom, Thomas Artificial Intelligence We introduce Gaia2, a benchmark for evaluating large language model agents in realistic, asynchronous environments. Unlike prior static or synchronous evaluations, Gaia2 introduces scenarios where environments evolve independently of agent actions, requiring agents to operate under temporal constraints, adapt to noisy and dynamic events, resolve ambiguity, and collaborate with other agents. Each scenario is paired with a write-action verifier, enabling fine-grained, action-level evaluation and making Gaia2 directly usable for reinforcement learning from verifiable rewards. Our evaluation of state-of-the-art proprietary and open-source models shows that no model dominates across capabilities: GPT-5 (high) reaches the strongest overall score of 42% pass@1 but fails on time-sensitive tasks, Claude-4 Sonnet trades accuracy and speed for cost, Kimi-K2 leads among open-source models with 21% pass@1. These results highlight fundamental trade-offs between reasoning, efficiency, robustness, and expose challenges in closing the "sim2real" gap. Gaia2 is built on a consumer environment with the open-source Agents Research Environments platform and designed to be easy to extend. By releasing Gaia2 alongside the foundational ARE framework, we aim to provide the community with a flexible infrastructure for developing, benchmarking, and training the next generation of practical agent systems. |
| title | Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2602.11964 |