MineExplorer: Evaluating Open-World Exploration of MLLM Agents in Minecraft

Even the most capable multimodal large language model (MLLM) agents struggle when they are forced to think more than one step ahead. While these models excel at describing what they see, they frequently lose the thread when a goal requires a sequence of hidden prerequisites. For example, an agent might need to find a tool before it can mine a specific block.

In the field of embodied AI (the study of agents that interact with physical or simulated worlds), researchers are moving agents from passive observers to active explorers. Previously, most benchmarks focused on short-horizon tasks. These tasks used constrained scenes that did not require much long-term planning. This left a gap in our understanding. Can an agent actually sustain exploration over a long sequence of changing states? Or does it just wander aimlessly once the immediate goal is out of sight? The MINEEXPLORER paper addresses this by using Minecraft as a rigorous testbed for multi-hop reasoning.

The Problem

The current state of MLLM evaluation faces two main issues: short horizons and domain entanglement. Most existing benchmarks compress interaction into tiny windows. This makes it impossible to tell if an agent can sustain a strategy over time. Furthermore, many game-based benchmarks accidentally test an agent's ability to memorize specific game mechanics. If an agent succeeds because it "knows" a niche Minecraft recipe, you haven't measured its general reasoning.

As shown in, the authors argue for a way to separate general world knowledge from Minecraft-specific priors (pre-existing knowledge about game rules).

Without this decoupling, we are measuring trivia, not intelligence. Existing frameworks often fail to isolate whether an agent can connect its perception of the environment with decisions that unfold over multiple, non-obvious steps.

How It Works

The authors tackle this through a multi-agent synthesis workflow. This workflow is designed to build high-fidelity, verifiable benchmark instances. Rather than using a single LLM to generate a task, they employ a "group chat" of specialized agents. This ensures the resulting scenarios are logically sound.

The architecture follows these key stages: 1. Knowledge-Controlled Filtering: They start with a large pool of atomic tasks. They use an LLM judge to strip away anything that requires non-intuitive Minecraft mechanics. This ensures the task pool consists only of tasks that a person with general commonsense could solve. 2. Capability Mapping: Remaining tasks are categorized into a ReAct-style taxonomy (a framework combining reasoning and acting). This covers Perception (understanding the environment), Reasoning (making decisions), and Action (executing movements), as detailed in .

1. Multi-Hop Composition: The Task Selector Agent picks $k$ compatible tasks. It chains them into a Directed Acyclic Graph (DAG, a structure with no loops). Crucially, the final instruction only mentions the ultimate goal. The intermediate steps are "hidden," forcing the agent to infer prerequisites from the environment.
2. Multi-Agent Orchestration: To prevent errors, a Scene Designer renders the environment. A Milestone Agent creates rule-based checkpoints (e.g., checking if a specific item is in the inventory). Finally, a Minecraft Expert audits the setup for game-specific biases.

This workflow is validated by human evaluation. It produces significantly more reliable instances than a single-agent approach. Specifically, the multi-agent pipeline increases the valid rate by approximately 30% [Table 2].

Numbers

The results reveal a stark reality for current MLLM technology. Scaling up does not necessarily solve the exploration problem. The authors report that while top-tier models like Claude-Opus-4.6 and Gemini-3.1-Pro-Preview handle single-hop tasks relatively well, their performance collapses as the number of "hops" increases [Table 3].

Specifically, the paper finds that: * Complexity kills performance: Task Success Rate (TSR, the percentage of completed goals) drops sharply as difficulty increases. As shown in, completion rates decrease consistently as tasks move from low-difficulty to high-difficulty regimes.

• Perception $\neq$ Reasoning: Across nearly all models, perception scores are consistently higher than reasoning scores. Models are good at identifying what is in front of them. However, they struggle to decide what to do next.
• Scale is not a silver bullet: Larger models and even "thinking" modes do not consistently translate into better exploration. In some cases, larger Qwen variants did not outperform their smaller counterparts.

Furthermore, the authors note that managing visual memory is a delicate balance. In their ablation study [Table 5], they found that increasing the frame buffer (the number of historical images provided to the model) beyond 20 frames actually degrades performance. This is likely because stale observations introduce noise into the agent's decision loop.

What's Missing

While MINEEXPLORER is a significant step forward, it has notable gaps. First, the benchmark is still fundamentally tethered to the Minecraft engine. While the authors work to decouple "Minecraft knowledge" from "world knowledge," the underlying physics and movement constraints are still game-governed. These may not perfectly generalize to real-world robotic systems.

Second, the paper focuses heavily on empirical evaluation. It lacks a corresponding training methodology. It tells us where the models are failing—primarily in navigation and long-term planning —but it does not provide a roadmap for fixing these gaps. For a practitioner, knowing that an agent fails at "resource gathering" is useful. However, knowing how to architect a policy to fix it is the real requirement.

Lastly, the computational cost of the multi-agent synthesis workflow is not fully explored. Generating these high-quality, multi-hop instances involves heavy orchestration of multiple high-end LLM calls. While this works for creating a benchmark, it might be a bottleneck for dynamic environment generation.

Should You Prototype This

If you are building autonomous agents for complex, multi-step environments, the answer is yes, prototype the evaluation logic, but not necessarily the synthesis workflow.

The core value is the "hidden prerequisite" testing methodology. You should adopt their approach of composing multi-hop tasks with implicit dependencies. This will help stress-test your own agents. However, unless you are specifically building a benchmark, the full multi-agent synthesis pipeline is likely overkill. The code and dataset are available at https://github.com/Jometeorie/MineExplorer. This is a solid starting point for anyone needing to move beyond simple, single-turn instruction following.

Figures from the paper