AgentBoard: LLM Evaluation Suite

• AgentBoard is an open-source analytical evaluation suite designed to benchmark LLM agents in multi-turn, partially observable settings using a POMDP framework.
• It introduces innovative metrics like Progress Rate, enabling fine-grained assessment of intermediate agent behaviors beyond binary success rates.
• The platform integrates nine diverse environments—from embodied AI to web interaction—facilitating detailed sub-skill profiling and comparative analysis of agent performance.

AgentBoard is an open-source analytical evaluation suite designed for the rigorous benchmarking of LLM agents operating in multi-turn, partially observable settings. Developed to address the limitations of prior agent evaluation protocols focused on final success metrics and fully observable, one-shot tasks, AgentBoard formalizes agentic evaluation as a sequence-based process, introducing new metrics and unified benchmarking infrastructure that support fine-grained analysis of intermediate agent behaviors, subgoal achievement, and sub-skill proficiency (Ma et al., 2024).

1. Motivation and Design Principles

AgentBoard was constructed to bridge several key methodological gaps in agentic LLM evaluation. Traditional benchmarks generally conflate all trajectory outcomes to a success/failure dichotomy, providing little insight into incremental progress or the nuanced failure cases that impede robust agent design. Tasks in AgentBoard are formalized as partially observable Markov decision processes (POMDPs), where the agent’s policy $\pi_\theta$ operates on a stream of observations and actions across multiple time steps:

$$a_t \sim \pi_\theta(\cdot \mid e_{1:t-1},\, u_\mathrm{task}),$$

with $e_{1:t-1} = (a_1, o_1, \ldots, a_{t-1}, o_{t-1})$ and $u_\mathrm{task}$ encodes the initial task configuration and goal. This design allows tracking of agent performance under realistic conditions of partial observability and temporal feedback.

AgentBoard environments are annotated with ordered subgoals $(g_1, \ldots, g_K)$, enabling measurement of incremental achievement (progress) rather than an exclusive focus on the final goal. The framework’s Python-based toolkit includes unified wrappers for nine distinct environments, a general agent runner supporting both local and API-based LLMs, metric collectors, and integrated visualization dashboards for interpretable output across evaluation axes (Ma et al., 2024).

2. Benchmark Suite and Task Taxonomy

AgentBoard spans nine environments and over 1,000 individual instances, structured across four principal categories:

• Embodied AI: AlfWorld (household manipulation, 134 tasks), ScienceWorld (scientific experiments, 90 tasks), BabyAI (grid-based navigation, 112 tasks).
• Text-Game Planning: Jericho (interactive fiction), PDDL-formulated planning tasks (Gripper, Barman, BlocksWorld, Tyreworld), all translated to natural language actions and observations.
• Web Interaction: WebShop (e-commerce browsing/search), WebArena (multi-tab selection and DOM-level actions).
• Tool Use: Tool-Query (function-calling for external knowledge), Tool-Operation (productivity workflows on APIs like Google Sheets, Todoist).

All environments are deterministic to ensure that policy variations are attributable to the agent rather than stochastic world dynamics. Each is framed as a POMDP:

$$P(\tau) = P(s_0) \prod_{t=0}^{T-1}\left[\pi(a_t\mid g, s_t, m_t)T(s_{t+1}\mid s_t, a_t)\right].$$

3. Metrics: Success Rate and Progress Rate

Beyond the canonical Success Rate

$$\mathrm{SuccessRate} = \frac{1}{N} \sum_{i=1}^N \mathbb{I}[s^{(i)}_T \text{ meets } g^{(i)}],$$

AgentBoard introduces a continuous per-turn metric, Progress Rate, sensitive to intermediate subgoal achievement. Let $f(s, g) \in [0,1]$ be a similarity/matching function between agent state $s$ and goal $g$ (or subgoals $$a_t \sim \pi_\theta(\cdot \mid e_{1:t-1},\, u_\mathrm{task}),$$0). At turn $$a_t \sim \pi_\theta(\cdot \mid e_{1:t-1},\, u_\mathrm{task}),$$1,

$$a_t \sim \pi_\theta(\cdot \mid e_{1:t-1},\, u_\mathrm{task}),$$2

and overall:

$$a_t \sim \pi_\theta(\cdot \mid e_{1:t-1},\, u_\mathrm{task}),$$3

This metric supports discrimination between agents that nearly complete tasks and those that fail to advance. Capabilities such as sub-skill profiling (Memory, Planning, Grounding, World Modeling, Self-Reflection, Spatial Navigation) are incorporated via custom capability scores:

$$a_t \sim \pi_\theta(\cdot \mid e_{1:t-1},\, u_\mathrm{task}),$$4

4. Evaluation Methodology and Framework

AgentBoard’s standardized runner uses “sliding window” memory for long-horizon interaction, unifies agent prompting, and automates detailed logging. Pseudocode for a typical agent-environment run: $$a_t \sim \pi_\theta(\cdot \mid e_{1:t-1},\, u_\mathrm{task}),$$5 The evaluation suite supports both local and remote (API) LLMs and provides detailed visualization facilities (stepwise progress, radar sub-skill charts, action/observation trajectories, error breakdowns).

5. Analytical Findings from AgentBoard Benchmarks

AgentBoard has revealed several important insights:

• Metric Sensitivity: Progress Rate is discriminative even when Success Rate is near zero. For example, Llama2-13B and Mistral-7B exhibit approximately 3% success on embodied tasks but differ in Progress Rate (18.9% vs. 24.6%), exposing substantive underlying behavioral differences (Ma et al., 2024).
• Proprietary vs. Open-Weight Models: GPT-4 achieves the highest reported Progress (70.0%) and Success (47.9%) rates, outperforming all open-weight models; however, code-specialized LLMs (e.g., DeepSeek, CodeLlama) help to close this gap.
• Subskill Weaknesses: Open-weights typically lag in Planning and Self-Reflection compared to proprietary LLMs.
• Long-Range Planning: Proprietary models continue accumulating progress across 30+ interaction steps, while open-weight agents tend to plateau after approximately 6 steps.
• Grounding Accuracy: The syntactic validity of agent actions (“grounding” in the environment’s action grammar) is not strongly predictive of downstream task completion but highlights weaknesses in LLM action formatting.

6. Applications: Comparative Studies on Agentic Backbones

Recent work has leveraged AgentBoard to illuminate the strengths and limitations of diverse agentic backbones, especially in the context of diffusion-based LLMs (dLLMs) versus conventional auto-regressive models.

A comprehensive evaluation of dLLMs such as LLaDA and Dream showed that, contrary to efficiency-based expectations, dLLMs achieve at best 0–16% Success and 4–22% Progress rates across AlfWorld, ScienceWorld, and BabyAI—far below the 32–76% Success and 45–86% Progress rates of Qwen3-8B and Mistral-8B (auto-regressive LLMs) in identical ReAct multi-turn workflows (Lu et al., 19 Jan 2026). Notably, dLLMs exhibited:

• Retry-loop Failures: Agents with dLLM backbones repeatedly issue the same action (≥3 times consecutively) 5–10× more often than auto-regressive agents, indicating impaired exploration and response to negative feedback.
• Non-Causal Planning Collapse: Parallel denoising in dLLMs results in “fuzzy” intermediate states, undermining chain-of-thought and preventing commitment to long-range plans.
• Efficiency/Performance Trade-off: Despite higher token throughput (>150 toks/s), dLLMs exhibit a catastrophic reduction in meaningful planning capability.

The “bitter lesson” of these findings is that current dLLMs are inadequate as primary agentic backbones in long-horizon, feedback-driven planning and should only be deployed in non-causal or auxiliary roles (e.g., memory summarization, trajectory classification, schema correction) (Lu et al., 19 Jan 2026).

7. Extensions: World Models and Agent-Integrated Memory

AgentBoard serves as an evaluation ground for more sophisticated agent/world model integrations. The WorldEvolver framework, for example, introduces Episodic Memory, Semantic Memory, and Selective Foresight modules, augmenting agent planning with dynamic, contextually-evolving world models (Zhang et al., 29 Jun 2026). In controlled experiments on ALFWorld and ScienceWorld, WorldEvolver closes a 3–4 pp gap over previous world model baselines and achieves the highest best-of-5 Success Rates, reaching up to 63.33% on ScienceWorld with the GPT-5.4-mini backbone.

Detailed mechanisms include:

• Episodic Memory: Retrieval of exact prior (observation, action, next-observation) triples relevant to the agent’s current intended action.
• Semantic Memory: Online induction of persistent rules to filter or correct implausible model predictions.
• Selective Foresight: Confidence-based gating of predicted next-observations, suppressing low-confidence and potentially misleading rollouts.

These modules directly address common planning failures such as erroneous world-model predictions, misapplied actions, and misinterpretations of environmental feedback (Zhang et al., 29 Jun 2026).

8. Implications and Future Directions

AgentBoard establishes a rigorous foundation for diagnosing and improving multi-turn, partially observable agentic LLMs. Key open directions surfaced by its analyses include:

• Extending evaluation to non-deterministic or stochastic settings.
• Developing automated subgoal discovery to remove the need for manual task decomposition.
• Incorporating uncertainty and partial credit in Progress Rate formulations.

A plausible implication is that advances in hybrid architectures—combining parallel high-throughput models for non-causal tasks with causal, auto-regressive components for planning and feedback—will be necessary to reconcile efficiency requirements with the demands of robust, multi-horizon agentic reasoning. AgentBoard’s modular, extensible infrastructure positions it as a central resource for the continued empirical development and benchmarking of interpretable, capable LLM-powered agents (Ma et al., 2024).