mlsanityData to 14 Oct 2026

ARE: Scaling Up Agent Environments and Evaluations

arXiv 2509.17158 ↗First posted 21 Sep 2025cs.AI cs.CLcited by 30

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

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.

Key facts. Cited by 30 papers in the map (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 24 of its 38 references that are in the map. First posted on arXiv 21 Sep 2025.

Builds on

24 of 38 references.

Closest ancestors · 4

Same direction, newest first

  1. 2025Small Language Models are the Future of Agentic AI2 Jun
  2. 2025MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents3 Mar
  3. 2025Vending-Bench: A Benchmark for Long-Term Coherence of Autonomous Agents20 Feb
  4. 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains17 Jun

Other references · 20

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,371
  3. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  4. 2023GAIA: a benchmark for General AI Assistantscited by 1,128
  5. 2024Tulu 3: Pushing Frontiers in Open Language Model Post-Trainingcited by 1,053
  6. 2025BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agentscited by 647
  7. 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 575
  8. 2024Scaling Synthetic Data Creation with 1,000,000,000 Personascited by 417
  9. 2024AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agentscited by 401
  10. 2019On the Utility of Learning about Humans for Human-AI Coordinationcited by 349
  11. 2024ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilitiescited by 295
  12. 2025SWE-smith: Scaling Data for Software Engineering Agentscited by 285
8 more
  1. 2025WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learningcited by 165
  2. 2024RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learningcited by 151
  3. 2023Generative agent-based modeling with actions grounded in physical, social, or digital space using Concordiacited by 126
  4. 2025MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Serverscited by 109
  5. 2024Rule Based Rewards for Language Model Safetycited by 108
  6. 2025Interactive Debugging and Steering of Multi-Agent AI Systemscited by 68
  7. 2025MCP-RADAR: A Multi-Dimensional Benchmark for Evaluating Tool Use Capabilities in Large Language Modelscited by 48
  8. 2025Interactive Reasoning: Visualizing and Controlling Chain-of-Thought Reasoning in Large Language Modelscited by 9

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 25.

30 papers.

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  1. 2026Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligencecited by 54
  2. 2026APEX-Agentscited by 32
  3. 2026SkillCraft: Can LLM Agents Learn to Use Tools Skillfully?cited by 30
  4. 2026Immersion in the GitHub Universe: Scaling Coding Agents to Masterycited by 22
  5. 2025Nex-N1: Agentic Models Trained via a Unified Ecosystem for Large-Scale Environment Constructioncited by 17
  6. 2026ASTRA-bench: Evaluating Tool-Use Agent Reasoning and Action Planning with Personal User Contextcited by 14
  7. 2026DIVE: Scaling Diversity in Agentic Task Synthesis for Generalizable Tool Usecited by 13
  8. 2025OpenApps: Simulating Environment Variations to Measure UI-Agent Reliabilitycited by 12
  9. 2025M^3-Bench: Multi-Modal, Multi-Hop, Multi-Threaded Tool-Using MLLM Agent Benchmarkcited by 11
  10. 2026Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Applicationcited by 8
  11. 2026LongCoT: Benchmarking Long-Horizon Chain-of-Thought Reasoningcited by 6
  12. 2026Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scalecited by 6
18 more
  1. 2026Agentic Discovery of Neural Architectures: AIRA-Compose and AIRA-Designcited by 5
  2. 2026CreativityBench: Evaluating Agent Creative Reasoning via Affordance-Based Tool Repurposingcited by 4
  3. 2026Teach the Magnitude, Not the Direction: Verifier-Bounded Credit Assignment for Multi-Turn Multi-step LLM Agentscited by 4
  4. 2025Automating Benchmark Designcited by 3
  5. 2026The Agent's First Day: Benchmarking Learning, Exploration, and Scheduling in the Workplace Scenarioscited by 3
  6. 2026Scaling Small Agents Through Strategy Auctionscited by 3
  7. 2026Interactive Evaluation Requires a Design Sciencecited by 3
  8. 2026SentinelBench: A Benchmark for Long-Running Monitoring Agentscited by 3
  9. 2025Reliable agent engineering should integrate machine-compatible organizational principlescited by 2
  10. 2026Scalable Environments Drive Generalizable Agentscited by 2
  11. 2026The Path to Self-Evolving Clinical Systems: Scaling Medical Agents from Assistance to Autonomycited by 2
  12. 2026RCTs for Frontier AI Governance: Methodological Challenges and Solutions for Human Uplift Studiescited by 0
  13. 2026RevengeBench: Reverse Engineering Code-Space Policies from Behavioral Experimentscited by 0
  14. 2026Deploying and Evaluating a Smart-Agriculture Agentic Engine for Full-Season Soybean Farm Operationscited by 0
  15. 2026Trust and Task Completion in the World of Consumer AI Agentscited by 0
  16. 2026ReLiveGym: Evaluating Long-Lived Agents over Weeks of Replayed Realitycited by 0
  17. 2026AgentSpy: Making AI Agent Behavior Observablecited by 0
  18. 2026Proactive Agentic Orchestrationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments
  2. 2025Towards General Agentic Intelligence via Environment Scaling
  3. 2026The Meta-Agent Challenge: Are Current Agents Capable of Autonomous Agent Development?
  4. 2026Harbor Adapters and Harbor-Index: Infrastructure and a Curated Meta-Dataset for Large-Scale Agentic Evaluation
  5. 2026Apodex 1.1: Scaling Agentic Intelligence for Complex Work
  6. 2025Towards a Science of Scaling Agent Systems
  7. 2025SynthTools: A Framework for Scaling Synthetic Tools for Agent Development
  8. 2025AgentOrchestra: Orchestrating Multi-Agent Intelligence with the Tool-Environment-Agent(TEA) Protocol