MineExplorer Benchmark: 813 Tasks Show Why Multimodal Agents Can See but Still Fail
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Last checked: 2026-07-28.
Key numbers first
MineExplorer includes 813 human-verified one-to-four-hop tasks. Each episode runs up to 1,800 environment steps at 0.1 seconds per step, or three minutes of continuous interaction, and covers 14 capabilities across perception, reasoning, and action. Meituan reports the leading Claude-Opus-4.6 at about 41 overall, falling from 77 on one-hop tasks to 12 on four-hop tasks; nearly 60% of its analyzed failures involve navigation.
The useful part of this benchmark is not another universal leaderboard. Longer tasks expose breaks in search, memory, planning, navigation, and tool use. Scores come from the publisher's environment and settings. They can justify an internal rerun, but they cannot replace one.
What is actually being measured
Read the result in four layers: how tasks or environments were constructed, which tools a model could call, how many steps each run received, and how the final score was computed. A single percentage hides tool calls, retries, latency, and external-search cost. Two systems with the same accuracy can have very different budgets and failure surfaces when one needs 60 calls and the other needs 20.
Why Minecraft instead of image QA
MineExplorer keeps the environment running. Each episode lasts up to 1,800 steps at 0.1 seconds per step. During those three minutes, the model repeatedly observes, updates its belief, and acts. Instructions provide the final goal but not the full dependency graph. Two-to-four-hop tasks require the agent to infer hidden prerequisites such as acquiring a tool or reaching the correct area before attempting the requested action.
The authors filter tasks that mainly require Minecraft-specific knowledge and emphasize more general spatial, temporal, entity, resource, commonsense, causal, movement, collection, and crafting skills. It remains a game environment rather than a robot deployment. Its advantage is reproducible state and milestone-based scoring.
813 tasks and 14 capabilities
Five specialist agents and an orchestrator generate candidate tasks before human verification. The multi-agent process reportedly improves valid-task yield by about 30 percentage points and quality by roughly 0.5 points, leaving 813 verified instances. Fourteen capabilities sit under perception, reasoning, and action, so a failed episode can be traced beyond a single binary result.
The leading Claude-Opus-4.6 reaches about 41 overall, but drops from 77 on one-hop tasks to 12 on four-hop tasks. Its perception score is 61.91 and reasoning 54.71, while navigation accounts for nearly 60% of analyzed failures. The result is not that the model cannot see. It cannot reliably maintain world state, dependencies, and a route over time.
Why more steps and memory do not fix it
Tasks that succeed tend to finish early; failures do not become successful merely because the budget reaches 1,800 steps. Adding more historical frames eventually reduces performance because stale observations interfere with the current state. The missing mechanism is not unlimited context but timestamped state updates, goal decomposition, action-result confirmation, and expiration of old observations.
Embodied-system logs should preserve observation, plan version, action, environment response, milestone, and safety event together. A final frame and aggregate success rate cannot reveal whether perception, planning, navigation, or execution failed.
Turn a public benchmark into an internal test
Select 20 to 60 tasks with known answers and divide them into single-step, multi-step, and conflicting-information groups. Lock model version, tools, maximum steps, and budget, then save full traces. Record invalid actions, wasted searches, working citations, recoveries, latency, and total cost alongside success rate. Public scores shortlist candidates; this task pack decides deployment.
Practical case
A warehouse-robot team prepares 24 picking routes, each with a hidden prerequisite such as clearing an obstacle, charging, or scanning a location. Eight routes are one-step, eight are two-step, and eight require three or four steps. Models receive the same camera frames and action API. Acceptance tracks completion, invalid actions, recoveries, and safe stops. If one-step success exceeds 85% but multi-step success remains below 60%, the system may assist operators but cannot execute autonomously. Any boundary violation, unconfirmed grasp, or continued use of a stale plan after the environment changes stops the trial.
Common reading mistakes
- Presenting a vendor result as independent reproduction.
- Assuming the same model name means identical inference settings.
- Reporting success without calls, failed traces, latency, or cost.
- Using static question-answering results to predict sustained action in a changing environment.
- Making a procurement decision when sources, versions, or run settings are missing.