Multi-Agent Dialogue Generation

• Multi-agent dialogue generation is a computational process that synthesizes conversations among autonomous agents with cooperative and competitive objectives.
• It utilizes methods including deep reinforcement learning, actor role specialization, self-play, and game-theoretic coordination to boost coherence, diversity, and controllability.
• It finds applications in healthcare, education, negotiation, and synthetic dialogue data generation, driving scalable, task-oriented conversational AI systems.

Multi-agent dialogue generation is the computational process of synthesizing dialogues involving multiple autonomous conversational agents, which can interact either cooperatively or competitively in pursuit of individual and/or system-level objectives. Systems in this domain range from open-domain multi-turn conversations and synthetic group chat for data augmentation, to complex, goal-driven or role-specialized task-oriented interaction frameworks, and increasingly deploy LLMs either as agents, orchestrators, or environment participants. Modern multi-agent dialogue research spans a spectrum of methodologies including deep reinforcement learning, preference-based agent training, game-theoretic coordination, and automated synthetic data construction. Key contributions target improvements in long-term coherence, diversity, controllability, and modularity of interaction, with applications extending across healthcare, education, negotiation, consensus-building, and tool-augmented conversational AI.

1. Formalizations, Agent Roles, and Task Decomposition

Multi-agent dialogue generation frameworks are commonly premised on Markov games or stochastic collaborative games, where each agent observes some context (utterances, semantic frames, dialogue states) and selects actions (tokens, dialogue acts, tool calls) with an evolving policy (Li et al., 2016, Papangelis et al., 2019, Bolleddu, 20 Nov 2025, Zeng et al., 18 Aug 2025). Roles may be symmetric (identical policies simulating self-play), dyadic with fixed roles (seeker/provider, doctor/patient), or heterogeneous and hierarchical (domain-specific agents under a dialog manager, group chat managers, or optimization agents for prompt evolution). Table 1 summarizes canonical agent architectures from papers:

Framework	Core Agents	Coordination Mechanism
RL Dialogue (Li et al., 2016)	2 LSTM encoder-decoders	Self-play, turn alternation, REINFORCE
DARD (Gupta et al., 2024)	DM agent + domain agents	Dialog manager routes to per-domain specialist agents
DIMF (Feng et al., 20 May 2025)	Intent, Slot, Response	Linear pipeline, ReAct schema
LinguaGame (Ye et al., 8 Jan 2026)	Sender, Receiver	Signaling game, game-theoretic selection
MADS (Li et al., 30 Sep 2025)	User, Dialog, Optimizer	User sim, assistant, prompt-evolution in self-play
Diplomats (Bolleddu, 20 Nov 2025)	N policy agents	HCN: GNN + attention; PNP protocol; global consensus

Agents interact at various semantic levels: direct utterance generation, dialogue acts, structured frames, or grounded tool operations (Papangelis et al., 2019, Zeng et al., 18 Aug 2025, Cho et al., 13 Nov 2025). Task decomposition is a major design axis: the Domain-Independent Multi-Agent Framework (DIMF) decomposes task-oriented dialogue into intent classification, slot filling, and response generation (Feng et al., 20 May 2025), substantially reducing per-agent learning complexity.

2. Training Objectives and Learning Algorithms

Multi-agent dialogue generation employs a diversity of learning objectives, frequently blending supervised pre-training, reinforcement learning, preference optimization, and zero-shot coordination.

Reinforcement learning approaches frame dialogues as sequential games, with agents maximizing expected cumulative rewards through self-play. The classic deep RL approach (Li et al., 2016) uses policy gradients with composite rewards for informativity, coherence, and ease-of-answering. Collaborative RL techniques such as WoLF-PHC extend Q-learning and policy hill-climbing to non-stationary multi-agent environments, achieving 66.3% success in DSTC2 (Papangelis et al., 2019). Advanced MARL architectures—e.g., Dialogue Diplomats' Hierarchical Consensus Network—integrate GNNs, multilevel attention, and PPO training to scale robustly to 50 agents with 94.2% consensus (Bolleddu, 20 Nov 2025).

Preference-based fine-tuning such as Direct Preference Optimization (DPO) is leveraged in DIMF to align agent outputs with human preferences; Data Distribution Adaptation ensures robust coverage across domains (Feng et al., 20 May 2025). This modular optimization supports strong zero-shot transfer and agent interchangeability.

Game-theoretic paradigms such as LinguaGame sidestep parameter fine-tuning, crafting an inference-time signaling game to maximize mutual understanding over intent and strategy spaces at each turn. LinguaGame's equilibrium-based utterance selection significantly outperforms classic LLM re-ranking and intent-conditioned baselines on communication efficiency (Ye et al., 8 Jan 2026).

Synthetic self-play and prompt optimization enable large-scale, low-cost dialogue simulation in the absence of labeled data. For example, the MADS framework iteratively refines prompt-based policies for persona-driven persuasion, incorporating chain-of-attitude entropy as a diversity metric (Li et al., 30 Sep 2025). ToolACE-MT abandons explicit RL in favor of staged, non-autoregressive LLM-based dialogue simulation with iterative refinement and offline verification, substantially reducing data generation cost while improving data realism and agentic coordination (Zeng et al., 18 Aug 2025).

3. System Orchestration, Modularization, and Specialization

A key trend is the orchestration of multiple agent services—often with distinct capabilities—via a central manager, pipeline, or protocol:

• Pipeline architectures: Educational-Psychological multi-agent systems (Ni et al., 2024) pipeline security detection, intent identification, and domain-specialized LLMs for robust dual-domain counseling.
• Domain-specific delegation: DARD (Gupta et al., 2024) and DIMF (Feng et al., 20 May 2025) route context to domain or function-specific agents, leveraging mix-and-match modularity (Flan-T5, Mistral-7B, Claude).
• Human-in-the-loop curation: In mental-health or expert-in-the-loop deployments (e.g., dual dialogue for therapists (Kampman et al., 2024)), the orchestrator is a human, who selects and supervises agent outputs—preserving final control and mitigating automation bias.
• Game-theoretic and negotiation protocols: Dialogue Diplomats (Bolleddu, 20 Nov 2025) enforces negotiation structure and adaptive concession through a progressive protocol, while LinguaGame operates via explicit multi-stage signaling games at each utterance.

Specialization can occur at the level of sub-tasks (intent, slot, NLG), user simulation (persona-driven proxies), or even policy optimizer (Optimization Agent in MADS (Li et al., 30 Sep 2025)). In private domains, documentation-first behavioral modeling can entirely replace supervised/fine-tuned training, with orchestration logic and system prompts aligning agents to domain APIs and rules (Cho et al., 13 Nov 2025).

4. Synthetic Dialogue Generation and Data Augmentation

Synthetic multi-agent dialogue generation frameworks such as ConvoGen (Gody et al., 21 Mar 2025), ToolACE-MT (Zeng et al., 18 Aug 2025), and MADS (Li et al., 30 Sep 2025) produce training corpora for conversational AI in data-sparse or privacy-sensitive scenarios. Methods include:

• Persona-driven group chat managers: ConvoGen dynamically composes group dialogues among LLM-driven persona agents, using a dynamically sampled few-shot hub to increase lexical and contextual diversity (e.g., achieving MTLD ≈ 80–100, exceeding most human corpora).
• Tool-augmented, multi-turn program synthesis: ToolACE-MT offloads multi-turn agentic dialogue construction to a staged, non-autoregressive generation process (coarse skeleton → iterative refinement → model+rule verification), outpacing multi-agent simulation in sample efficiency and accuracy (Multi-Turn accuracy: 40.25% vs. 31.38% for MAS baseline on BFCL-v3) (Zeng et al., 18 Aug 2025).
• Self-play with optimization agents: MADS executes iterative optimization of dialog-agent prompts, synthesize multi-turn, persona-rich dialogues, and directly uplifts downstream conversion metrics (22.4% increase in real-world traffic conversion) (Li et al., 30 Sep 2025).

Synthetic frameworks enable scalable domain adaptation, support fine-tuning for downstream LLM agents (including code, tool, or information-seeking agents), and act as reproducible test beds for evaluation (Gody et al., 21 Mar 2025, Cho et al., 13 Nov 2025).

5. Evaluation Methodologies and Benchmarks

Evaluation strategies in multi-agent dialogue generation comprise both automatic and human-centered criteria, as well as specialized, domain-driven metrics:

• Automatic metrics: Success rate, inform rate, BLEU score, dialogue length, diversity indices (distinct-n, MTLD), task-completion efficiency, and hallucination rates feature across most frameworks (Li et al., 2016, Gupta et al., 2024, Zeng et al., 18 Aug 2025).
• Human/Large Model Judgments: Grounding, relevance, empathy (Therapist Empathy Scale (Kampman et al., 2024)), expert Likert ratings, and LLM-as-judge overall dialog evaluation are used to capture qualitative dimensions often missed by automated metrics (Gody et al., 21 Mar 2025, Li et al., 30 Sep 2025, Ye et al., 8 Jan 2026).
• Specialized metrics: Chain-of-attitude entropy (MADS (Li et al., 30 Sep 2025)), consensus/fairness indices (Gini, Pareto, Dialogue Diplomats (Bolleddu, 20 Nov 2025)), and compliance/factual control in clinical dialogue (Feng et al., 26 May 2025).
• Benchmarks: MultiWOZ (Gupta et al., 2024, Feng et al., 20 May 2025), E-EVAL for educational QA (Ni et al., 2024), BFCL-v3, ACEBench, τ-Bench for agentic function calling (Zeng et al., 18 Aug 2025), and MTMedDialog for medical interactions (Feng et al., 26 May 2025).

Methodological innovations include balanced preference-pair selection for DPO/DDA (Feng et al., 20 May 2025), and human-in-the-loop evaluation to ensure domain accuracy, professional tone, and user safety in sensitive settings (Ni et al., 2024, Kampman et al., 2024).

6. Challenges, Limitations, and Future Directions

Multi-agent dialogue generation encounters scalability, generalization, and evaluation challenges:

• Non-stationarity and stability: As agents co-learn, non-stationarity of environments complicates RL optimization; approaches such as WoLF-PHC and hierarchical attention networks provide partial solutions (Papangelis et al., 2019, Bolleddu, 20 Nov 2025).
• Scalability: Tabular RL and heavy autoregressive simulation become prohibitive as agent and domain counts rise. Non-autoregressive iterative generation substantially reduces data cost while maintaining performance (Zeng et al., 18 Aug 2025).
• Evaluation lacking standardization: Many empirical effects, particularly in counseling or psychological domains, remain anecdotal or based on subjective human judgment (Ni et al., 2024, Kampman et al., 2024).
• Dynamic domain adaptation: Static retrieval corpora and schema (as in Baidu Encyclopedia-based systems) limit extensibility and require reindexing for knowledge updates (Ni et al., 2024).
• Coordination and arbitration: In most current systems, modular agent outputs are selected or merged deterministically, leaving complex arbitration (conflicting recommendations, multi-agent fusion) as an open question (Kampman et al., 2024).
• Robustness to domain drift: Systems using documentation- or prompt-first behavioral modeling exhibit transfer robustness, but efficacy depends on the completeness and accuracy of documentation (SpecDoc) and adherence to propositional safety constraints (Cho et al., 13 Nov 2025).

Open avenues include integration of deep MARL methods (COMA, MADDPG), scaling modular multi-agent frameworks to more heterogeneous domains, exploration of direct inference-time coordination (as in LinguaGame (Ye et al., 8 Jan 2026)), and development of large-scale, automatic evaluation benchmarks for subjective outcomes in counseling and education.

7. Impact Across Domains and Applications

Multi-agent dialogue generation is enabling substantive advances in open-domain and task-oriented conversational systems:

• Clinical decision support: DoctorAgent-RL aligns multi-turn questioning strategies with real-world medical protocol, outperforming standard supervised and RL models both in reasoning quality and interaction efficiency (53.9% diagnostic accuracy, 8.6 turns per consult) (Feng et al., 26 May 2025).
• Negotiation and consensus-building: Dialogue Diplomats achieves unprecedented multi-party consensus and fairness using attention+GNN hierarchies and protocol-driven negotiation (Bolleddu, 20 Nov 2025).
• Education and psychological counseling: Multi-agent modular systems combine retrieval, intent classification, and LLM specialization to achieve domain-leading accuracy and professional tone (Ni et al., 2024).
• Synthetic conversational AI data: ConvoGen and ToolACE-MT enable scalable production of high-diversity, contextually grounded dialogue data across open and tool-augmented domains, supporting the development of next-generation conversational agents (Gody et al., 21 Mar 2025, Zeng et al., 18 Aug 2025).
• Private-domain orchestration: Training-free, documentation-driven behavior modeling allows rapid adaptation and agile compliance in privacy-sensitive deployments (Cho et al., 13 Nov 2025).

In summary, multi-agent dialogue generation constitutes a rapidly evolving field driving both empirical advances in conversational AI performance and foundational progress in coordination, modularity, and evaluability of complex agent interactions (Li et al., 2016, Ni et al., 2024, Gupta et al., 2024, Papangelis et al., 2019, Ye et al., 8 Jan 2026, Cho et al., 13 Nov 2025, Gody et al., 21 Mar 2025, Feng et al., 20 May 2025, Li et al., 30 Sep 2025, Bolleddu, 20 Nov 2025, Feng et al., 26 May 2025, Zeng et al., 18 Aug 2025).