Generalist Agent Benchmarks

• Generalist agent benchmarks are systematic evaluation frameworks that test AI agents' ability to perform diverse tasks across multiple domains with unified metrics.
• They employ methodologies such as multi-task testing, cross-domain validation, and modular task composition to quantify adaptability and transfer learning.
• These benchmarks are pivotal for tracking progress toward artificial general intelligence and comparing monolithic versus modular agent architectures.

A generalist agent benchmark is a systematic evaluation framework or dataset designed to measure the capabilities of artificial agents that can perform a wide range of tasks—often across different domains, modalities, and embodiments—using a single model or architecture. Unlike traditional “specialist” benchmarks tailored to a narrow skillset, generalist agent benchmarks are constructed to probe not only performance on specific tasks but also adaptability, robustness, transfer learning, and cross-task generalization within and between multiple environments and modalities. These benchmarks are pivotal for tracking progress toward artificial general intelligence and enabling rigorous comparison between monolithic and modular agentic systems.

1. Definitions and Fundamental Principles

Generalist agents are distinguished by their capacity to perform multiple, often heterogeneous, tasks across diverse settings—with a single set of parameters, shared representations, or coordinated policy modules. Accordingly, a generalist agent benchmark must satisfy several intertwined requirements:

• Task Diversity: The benchmark spans discrete, continuous, visual, textual, and (sometimes) multi-embodiment domains, e.g., simulation, robotics, language, vision, or web navigation.
• Unified Evaluation Protocol: All tasks are specified in a format that supports evaluation using a shared input/output representation or minimal adaptors (tokenization, embedding, or a unified API).
• Standardized Metrics: Success and failure are measured via normalized returns (relative to expert or random performance), step-wise accuracy, or multi-dimensional reward models that aggregate multiple facets of behavior.
• Generalization and Adaptability: Agent performance is systematically probed beyond training distribution, such as cross-domain, cross-task, or zero/few-shot transfer scenarios.
• Benchmark Scalability: The benchmark design allows expansion to new tasks, modalities, or dynamic complexity, facilitating continual progress evaluation as agent capabilities improve.

These principles are instantiated in prominent generalist agent benchmarks, each tailored to test the limits of current architectures.

2. Representative Benchmarks and Datasets

The following table summarizes core generalist agent benchmarks, their domains, and distinguishing features:

Benchmark	Domain(s)	Notable Features
Gato (Reed et al., 2022)	Vision, Control, Text	604+ tasks over RL, robotics, VQA, image captioning, dialogue
Mind2Web (Deng et al., 2023)	Web Automation	2,000+ tasks, 137 real websites, manual multi-step actions, DOM diversity
MCU (Zheng et al., 2023)	Minecraft, Open-world	3,452 atomic tasks, compositional, multi-dimensional difficulty, infinite scaling
LEO (Huang et al., 2023)	3D Vision, Embodiment	Object-centric 3D tasks: captioning, QA, navigation, manipulation
SRM (Miao et al., 24 Mar 2025)	Virtual Agent, OS/Web	Step-wise, multi-dimensional (helpfulness, success odds, efficiency, relevance, coherence), 4 platforms
OpenHands/Versa (Wang et al., 23 Jul 2024, Soni et al., 3 Jun 2025)	Software, Web	Unified code, bash, web-browsing, file-access, evaluated on SWE-Bench, GAIA, The Agent Company
Meta MMO (Choe et al., 7 Jun 2024)	Multi-agent RL, Games	Multi-task, multi-agent, composable minigames, unified action/obs space
Generalist Hanabi (Sudhakar et al., 17 Mar 2025)	Multi-agent, Language	Variable-player, text-based MARL, cross-partner collaboration
AdaDemo (Mu et al., 11 Apr 2024)	Robotics	RLBench, Adroit; adaptive demonstration expansion, data efficiency
Agent S2 (Agashe et al., 1 Apr 2025)	GUI Automation, OS	Compositional generalist-specialist, GUI grounding, proactive planning
InfiGUIAgent (Liu et al., 8 Jan 2025)	GUI, Multimodal	Hierarchical/expectation-reflection reasoning, AndroidWorld, ScreenSpot

Most benchmarks deliberately span multiple tasks, modalities, or both, and are extensible for future research.

3. Methodologies for Evaluation

Generalist agent benchmarks employ a range of evaluation methodologies:

• Multi-Task and Multi-Domain Testing: Agents are evaluated on broad suites (e.g., Gato’s 604 tasks include Atari, DeepMind Lab, Meta-World, and robotic tasks; Mind2Web covers multi-domain web tasks).
• Cross-Generalization Splits: Benchmarks like Mind2Web partition tasks into cross-task, cross-website, and cross-domain splits to quantify generalization.
• Normalized Performance Metrics: RL tasks use expert- and random-normalized returns (e.g., 100% = expert, 0% = random; Gato (Reed et al., 2022)). Web/GUI/OS tasks report step or task completion rates, with stringent criteria for overall success (e.g., all steps correct).
• Multi-Dimensional Reward Modeling: SRM (Miao et al., 24 Mar 2025) introduces step-wise, multi-dimensional rewards (helpfulness, odds of success, efficiency, task relevance, coherence) for fine-grained agent assessment:

$$\text{OS}_i = \frac{1}{N} \sum_{j=1}^{N} \mathbb{I}(a_{i,j}=a^*)$$

• Scaling Studies: Benchmarks such as Gato and LEO report learning curves, in-distribution vs. out-of-distribution performance, and scaling law analyses.
• Modular and Compositional Task Creation: MCU (Zheng et al., 2023) uses atomic task composition (via AND/OR operators, constraints) and aligns heuristic metrics to human ratings (up to 91.5%).
• Systematic Error and Ablation Analysis: Several works (e.g., Magentic-One (Fourney et al., 7 Nov 2024)) employ autogen evaluation suites to disentangle errors attributable to orchestration, specialist modules, and context management.

Reproducibility is supported via open-source code, datasets, and evaluation scripts.

4. Architectural and Algorithmic Considerations

Benchmark design both informs and is informed by agent architectures:

• Unified Sequence Models: Transformer-based architectures process multi-modal token sequences (Gato, LEO), facilitating shared representations and prompt-based conditioning for generalization.
• Compositional and Modular Agents: Systems such as Magentic-One and Agent S2 organize agents into orchestration layers (Orchestrator/Manager, Worker/Specialist) and modular planners for scalable multi-task coordination.
• Retrieval- and Demonstration-Augmented Agents: REGENT (Sridhar et al., 6 Dec 2024) leverages retrieval-based policy interpolation for fast adaptation:

$$\pi_{\text{REGENT}}^\theta(s_t, r_{t-1}, c_t) = \exp(-\lambda d(s_t, s')) \pi_R(s_t, c_t) + (1 - \exp(-\lambda d(s_t, s'))) \cdot \sigma(\pi_\theta(s_t, r_{t-1}, c_t))$$

• Generalist-Specialist Frameworks: Hybrid training pipelines (e.g., GSL (Jia et al., 2022)) use specialists to overcome performance plateaus by providing high-quality demonstrations to guide generalist optimization.
• Proactive Hierarchical Planning and GUI Grounding: Agent S2 (Agashe et al., 1 Apr 2025) introduces hierarchical plan refinement and mixture-of-expert grounding for complex GUI environments.

Efficient evaluation and training require principled task sampling, trajectory management, and observed context encoding.

5. Generalization, Transfer, and Adaptation

Generalist benchmarks are constructed to stress agents on generalization axes:

• Prompt-Based Few-Shot Adaptation: Prompt conditioning (Gato), in-context learning (REGENT), and dynamic subgoal refactoring (Agent S2) are exploited for rapid adaptation.
• Variable Context and Action Spaces: Text-based state/action abstractions (Generalist Hanabi (Sudhakar et al., 17 Mar 2025)) accommodate variable player settings and enable robust zero-shot collaboration.
• Transfer Across Embodiments: Agents are assessed for their ability to carry skills across different hardware avatars (e.g., both simulated and real normed control, as in Gato and AdaDemo).
• Cross-Modality: Benchmarks require agents to combine and route information across vision, language, audio, and structured input (e.g., InfiGUIAgent (Liu et al., 8 Jan 2025), InfantAgent-Next (Lei et al., 16 May 2025)).
• Automated Capability Expansion: Alita (Qiu et al., 26 May 2025) autonomously generates Model Context Protocols (MCPs) for capability growth, enabling ongoing evolution as new types of tasks emerge.

Performance on these axes is a key determinant of agent design quality and benchmark value.

6. Limitations and Future Directions

Present generalist agent benchmarks exhibit several limitations and emerging areas:

• Incomplete Coverage: Although benchmarks such as MCU and Mind2Web pursue task diversity, no single benchmark exhaustively spans the possible range of real-world tasks; most focus on specific verticals or simulator limitations.
• Stepwise Process Supervision: The trend is towards step-wise, multi-dimensional, and outcome-sensitive metrics (SRM (Miao et al., 24 Mar 2025)), yet many established RL and vision benchmarks still employ outcome-only reward signals.
• Data Efficiency and Scalability: Frameworks such as AdaDemo (Mu et al., 11 Apr 2024) and REGENT (Sridhar et al., 6 Dec 2024) point to adaptive or retrieval-based learning as critical for scalable, data-efficient generalist agents.
• Automated Evaluation and Human Alignment: Human calibration (e.g., in MCU), reproducible isolation (AutoGenBench (Fourney et al., 7 Nov 2024)), and automated replay analysis are essential for trustworthy comparison.
• Ethical and Societal Risks: Increased generality amplifies challenges around safety, deployment risk, unintended behaviors, and adversarial exploitation, requiring careful review and mitigation beyond technical benchmarking.

As agentic architectures and evaluation methodologies co-evolve, benchmarks will increasingly emphasize robustness, adaptability, and compositional understanding—bridging from artificial generalist agents towards more human-aligned, trustworthy general intelligence.

7. Significance and Ongoing Research

Generalist agent benchmarks are integral to measuring the trajectory of AI research beyond narrow specialist tasks. Recent work demonstrates that unified or modular agent architectures—trained and tested on diverse, rigorously constructed benchmarks—can approach, match, or even surpass the performance of specialist agents in their own domains, while exhibiting transfer, adaptability, and compositional skill.

The continuing development of scalable, extensible, and multi-faceted benchmarks—coupled with open-source code, structured evaluation metrics, and collaborative community practices—will remain central to advancing both agentic capabilities and their responsible deployment in increasingly complex real-world domains.