WISE: A Long-Horizon Agent in Minecraft with Why-Which Reasoning
arXiv AIArchived Jun 12, 2026✓ Full text saved
arXiv:2606.12852v1 Announce Type: new Abstract: Rapid advances have been made in developing general-purpose embodied agent in environments like Minecraft through the adoption of LLM-augmented hierarchical approaches. Despite their promise, low-level controllers often become performance bottlenecks due to repeated execution failures. We argue that a key limitation is not only the lack of episodic memory, but also the decoupling of \textit{what-where-when} memory from \textit{which-why} reasoning.
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Computer Science > Artificial Intelligence
[Submitted on 11 Jun 2026]
WISE: A Long-Horizon Agent in Minecraft with Why-Which Reasoning
Renmin Cheng, Changhao Chen (The Hong Kong University of Science and Technology (Guangzhou))
Rapid advances have been made in developing general-purpose embodied agent in environments like Minecraft through the adoption of LLM-augmented hierarchical approaches. Despite their promise, low-level controllers often become performance bottlenecks due to repeated execution failures. We argue that a key limitation is not only the lack of episodic memory, but also the decoupling of \textit{what-where-when} memory from \textit{which-why} reasoning. To address this, we propose \textbf{WISE} (Which-Why Informed Semantic Explorer), a long-horizon agent framework with an enhanced low-level controller equipped with a Causal Event Graph that augments episodic memory with explicit causal structure linking observations to task relevance. Unlike prior work such as MrSteve, which relies on feature similarity for retrieval, WISE enables robust recall under viewpoint changes and supports opportunistic task reordering through causal reasoning. Building on this memory, we propose an Opportunistic Task Scheduler that dynamically re-prioritizes subtasks when causally relevant opportunities are detected. We further equip WISE with a multi-scale progressive exploration strategy to provide spatially comprehensive observations for downstream reasoning. Experiments show that WISE largely improves task success and efficiency on long-horizon sparse tasks, particularly in settings requiring adaptive decision-making.
Subjects: Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.12852 [cs.AI]
(or arXiv:2606.12852v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.12852
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From: Renmin Cheng [view email]
[v1] Thu, 11 Jun 2026 03:35:02 UTC (12,260 KB)
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