CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society

Role-playing multi-agent systems

Foundational work on LLM-based agents and Role-playing multi-agent systems.

Addresses how language models can extend beyond single-turn generation through Role-playing multi-agent systems.

The paper operationalizes Role-playing multi-agent systems through a structured agent mechanism.

The system connects an LLM with state, control, tools, memory, environments, interfaces, or other agents.

Uses task-specific datasets, environments, benchmarks, or interaction traces appropriate to the research question.

Uses the training or inference adaptation documented by the primary source; undisclosed details are not inferred.

Evaluates the proposed mechanism against relevant baselines in the target setting.

Compares against the underlying model, simpler control strategies, or prior agent mechanisms.

Reports evidence for the proposed contribution within its stated benchmark or environment.

Contributes a reusable pattern for building or evaluating LLM-based agents.

Primary source: https://arxiv.org/abs/2303.17760