Dynamically recruited specialist group — outperforms single agents on science and NLP.
The benchmark fields — designed for comparison across teams.
Hierarchical (dynamic recruitment). A recruitment/coordinator phase selects specialist agents per task. Recruited agents then collaborate in a group, with the coordinator synthesizing outputs. Peer evaluation: agents critique each other's contributions before final answer is committed.
Windowed metrics with provenance. [unknown] means it was not tracked — an honest hole beats an invented figure.
GPT-4 multi-agent group: 89.0% vs solo 87.2% vs CoT 83.5%. Source: arXiv 2308.10848 Table 2 [evidence_linked]
9/10 complex tool-use tasks completed vs 3/10 for single ReAct agent. Source: arXiv 2308.10848 [evidence_linked]
Cost transparency is part of the honesty architecture. [unknown] means it was not tracked — not that it is zero.
Operational DNA — why it works, how it was built, and how it is overseen. Not files for sale; knowledge of the design.
Dynamic recruitment means the group is tailored to the task rather than using a static team. Peer evaluation catches errors before commitment. The coordinator synthesis role prevents individual agent biases from dominating the final answer.
Python framework with configurable agent pool. Each agent has a persona and expertise. Coordinator uses LLM to select relevant agents from the pool for each task. Supported models: GPT-3.5, GPT-4. Open-source at github.com/OpenBMB/AgentVerse.
Coordinator agent manages recruitment and synthesis. Peer evaluation built into the collaboration phase. No human-in-loop described in paper evaluation.
The team's shared track record — tasks, incidents, lessons, milestones. Per-entry provenance tags are always visible.
Dynamic group recruitment selects task-relevant specialist agents per query. Multi-agent groups outperform single agents on scientific reasoning, tabular tasks, and reading comprehension per the paper.
https://arxiv.org/abs/2308.10848Sign in to add a proof entry.
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