analiza aiqbench
This research introduces TickingCollabBench, a rigorous Minecraft-based benchmark designed to evaluate multi-agent systems under strict real-time constraints, partial observability, and agent heterogeneity. The study highlights significant limitations in current LLMs when coordinating complex tasks without global knowledge or sufficient latency tolerance, offering critical insights for advancing robust collaborative AI architectures.
Multi-agent Framework for Time-Sensitive Complementary Collaboration in Minecraft
arXiv:2606.15684v1 Announce Type: new Abstract: We present TickingCollabBench, a Minecraft-based multi-agent benchmark for a novel class of time-sensitive complementary collaboration tasks. Our benchmark reflects four core characteristics of real-world collaboration: agent heterogeneity, mandatory collaboration, dynamic environments, and strict real-time constraints with failure risks. To enable this, we develop the TickingCollab framework, which supports the generation of diverse dynamic environments and abstracts Minecraft's primitive APIs to enable declarative YAML task specifications for composing these events. Building on this, we design a feasibility-aware automated benchmark generation pipeline, where an LLM drafts structurally diverse task configurations and feasibility verifier filters out invalid ones using approximate constraints. Evaluations demonstrate that lang latency and inherent difficulty of coordinating under partial observability and agent heterogeneity cause LLMs to frequently fail under dynamic environments and fall significantly short of a global-knowledge oracle.
Abstract:We present TickingCollabBench, a Minecraft-based multi-agent benchmark for a novel class of time-sensitive complementary collaboration tasks. Our benchmark reflects four core characteristics of real-world collaboration: agent heterogeneity, mandatory collaboration, dynamic environments, and strict real-time constraints with failure risks. To enable this, we develop the TickingCollab framework, which supports the generation of diverse dynamic environments and abstracts Minecraft's primitive APIs to enable declarative YAML task specifications for composing these events. Building on this, we design a feasibility-aware automated benchmark generation pipeline, where an LLM drafts structurally diverse task configurations and feasibility verifier filters out invalid ones using approximate constraints. Evaluations demonstrate that lang latency and inherent difficulty of coordinating under partial observability and agent heterogeneity cause LLMs to frequently fail under dynamic environments and fall significantly short of a global-knowledge oracle.
Artificial Intelligence (cs.AI)
arXiv:2606.15684 [cs.AI]
(or arXiv:2606.15684v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.15684 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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