Multi-agent Framework for Time-Sensitive Complementary Collaboration in Minecraft
- URL: http://arxiv.org/abs/2606.15684v1
- Date: Sun, 14 Jun 2026 09:02:33 GMT
- Title: Multi-agent Framework for Time-Sensitive Complementary Collaboration in Minecraft
- Abstract summary: TickingCollabBench is a Minecraft-based multi-agent benchmark for a novel class of time-sensitive complementary collaboration tasks.<n>We develop the TickingCollab framework, which supports the generation of diverse dynamic environments.<n>We design a feasibility-aware automated benchmark generation pipeline, where an LLM drafts structurally diverse task configurations.
- Score: 23.22494669795205
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- DuMateBench: Evaluating Autonomous Agents in Complex Real-World Workflows [69.12339622053588]
We introduce DuMateBench, a real-session benchmark reconstructed from anonymized and privacy-screened user sessions.<n>The resulting benchmark comprises 200 tasks spanning 8 broad scenarios and 17 fine-grained capability categories.<n>We execute these tasks in isolated Docker containers injected with three forms of real-world environmental complexity: Insufficient, Unstable, and Noisy.
arXiv Detail & Related papers (2026-08-27T02:36:23Z) - Scaling Agentic Capabilities via Grounded Interaction Synthesis [12.796216573698677]
We introduce Grounded Agentic Interaction Synthesis (GAIS), a framework that automates the scalable construction of diverse environments and complex tasks.<n>Specifically, we construct protocol-anchored environments derived from real-world Model Context Protocol (MCP) servers to ensure functional diversity and difficulty.<n>Experiments on BFCL, $2$-Bench, and ACEBench demonstrate that GAIS-synthesized data significantly outperforms state-of-the-art baselines.
arXiv Detail & Related papers (2026-06-01T09:57:52Z) - LiveClawBench: Benchmarking LLM Agents on Complex, Real-World Assistant Tasks [58.3639630490749]
We introduce LiveClawBench, a benchmark to evaluate LLM agents on real-world assistant tasks.<n>Based on an analysis of various realClaw usage cases, we derive a Triple-Axis Complexity Framework.<n>We construct a pilot benchmark with explicit complexity-factor annotations, covering real-world assistant tasks.
arXiv Detail & Related papers (2026-03-20T16:08:21Z) - TALM: Dynamic Tree-Structured Multi-Agent Framework with Long-Term Memory for Scalable Code Generation [0.0]
Agentic code generation requires large language models capable of complex context management and multi-step reasoning.<n>We propose TALM, a dynamic framework that integrates structured task decomposition, localized re-reasoning, and long-term memory mechanisms.<n> Experimental results on HumanEval, BigCodeBench, and ClassEval benchmarks demonstrate that TALM consistently delivers strong reasoning performance and high token efficiency.
arXiv Detail & Related papers (2025-10-27T05:07:36Z) - Dynamic Generation of Multi-LLM Agents Communication Topologies with Graph Diffusion Models [99.85131798240808]
We introduce a novel generative framework called textitGuided Topology Diffusion (GTD)<n>Inspired by conditional discrete graph diffusion models, GTD formulates topology synthesis as an iterative construction process.<n>At each step, the generation is steered by a lightweight proxy model that predicts multi-objective rewards.<n>Experiments show that GTD can generate highly task-adaptive, sparse, and efficient communication topologies.
arXiv Detail & Related papers (2025-10-09T05:28:28Z) - EIFBENCH: Extremely Complex Instruction Following Benchmark for Large Language Models [64.70546873396624]
We present the Extremely Complex Instruction Following Benchmark (EIFBENCH) for evaluating large language models (LLMs)<n>EIFBENCH includes multi-task scenarios that enable comprehensive assessment across diverse task types concurrently.<n>We also propose the Segment Policy Optimization (SegPO) algorithm to enhance the LLM's ability to accurately fulfill multi-task workflow.
arXiv Detail & Related papers (2025-06-10T02:39:55Z) - CoT-RVS: Zero-Shot Chain-of-Thought Reasoning Segmentation for Videos [59.391265901911005]
We propose CoT-RVS, a novel framework employing the zero-shot Chain-of-Thought (CoT) capability of MLLM to address complex challenges by temporal-semantic reasoning.<n>CoT-RVS analyzes the visible objects within a given frame that possibly match the language query (semantic), and chooses a corresponding for each object that can be observed effortlessly among all frames (temporal)<n>Our framework's training-free feature further allows its extension to process online video streams, where the CoT is used at test time to update the object of interest when a better target starts to emerge
arXiv Detail & Related papers (2025-05-24T07:01:31Z) - MLE-Dojo: Interactive Environments for Empowering LLM Agents in Machine Learning Engineering [57.156093929365255]
Gym-style framework for systematically reinforcement learning, evaluating, and improving autonomous large language model (LLM) agents.<n>MLE-Dojo covers diverse, open-ended MLE tasks carefully curated to reflect realistic engineering scenarios.<n>Its fully executable environment supports comprehensive agent training via both supervised fine-tuning and reinforcement learning.
arXiv Detail & Related papers (2025-05-12T17:35:43Z) - TDAG: A Multi-Agent Framework based on Dynamic Task Decomposition and Agent Generation [41.21899915378596]
We propose a multi-agent framework based on dynamic Task Decomposition and Agent Generation (TDAG)<n>This framework dynamically decomposes complex tasks into smaller subtasks and assigns each to a specifically generated subagent.<n>ItineraryBench is designed to assess agents' abilities in memory, planning, and tool usage across tasks of varying complexity.
arXiv Detail & Related papers (2024-02-15T18:27:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.