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AgentsNet

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100
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Dataset Card for AgentsNet

This dataset card describes AgentsNet, a benchmark for evaluating coordination and collaboration capabilities of large language models (LLMs) organized in multi-agent systems. The benchmark comprises structured problem-solving tasks rooted in distributed computing, such as leader election, consensus, and graph coloring, executed via decentralized message-passing protocols.

Dataset Details

AGENTSNET is designed to assess how well large language models can reason, communicate, and collaborate when instantiated as agents in a network. It includes five classic problems from distributed systems:

  • Leader Election
  • Consensus
  • Graph Coloring
  • Vertex Cover
  • Maximal Matching

Each task is performed over communication graphs sampled from three network distributions (small-world, scale-free, geometric), with message-passing transcripts and final agent decisions provided for evaluation. The dataset supports evaluations at various scales (from 4 to 100 agents).

Uses

AgentsNet is intended for:

  • Benchmarking the ability of LLM-based agents to collaborate via structured message-passing.
  • Evaluating the performance of multi-agent systems on distributed coordination tasks.
  • Scaling experiments with LLM networks from small (4 agents) to large (100 agents) graphs.

Dataset Structure

Each example contains:

  • graph_generator: Type of graph (e.g., "ba" for Barabasi-Albert, "ws" for Watts-Strogatz or "dt" for Delaunay Triangulation)
  • num_nodes: The size of the graph
  • index: The sample index
  • graph: json string containing the graph
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