Consensia: Exploring Large Language Models as Trusted Arbitrators in Multi-Expert Consensus Mechanisms

Consensia is a research project aimed at exploring whether large language models (LLMs) can serve as trusted arbitrators in complex decision-making scenarios. By orchestrating multiple software engineering expert roles to reach interpretable consensus, it provides a new paradigm for AI-assisted decision-making and code review. Its core idea is to use a single LLM to simulate diverse expert perspectives, output well-argued consensus conclusions through structured debates—avoiding the complexity of multi-model systems while leveraging the advantages of collective intelligence.

Research Background and Core Issues

With the rapid improvement of LLM capabilities, whether AI can become a fair arbitrator in complex decision-making scenarios has become a key question. In the field of software engineering, tasks such as code review and architecture evaluation require integrating opinions from multiple domain experts, but human experts face issues like opinion divergence, cognitive biases, and communication costs. The Consensia project addresses this challenge by exploring the feasibility of LLMs acting as meta-experts to coordinate multiple professional roles and reach consensus.

Core hypothesis: Through a well-designed role-play mechanism, a single LLM can simulate diverse expert perspectives, conduct structured debates, and output interpretable consensus conclusions. This single-model multi-agent architecture balances collective intelligence and system simplicity.

System Architecture and Design Philosophy

Consensia adopts a separated front-end and back-end architecture: the front-end is built using React, Vite, and Tailwind CSS, while the back-end uses FastAPI to provide high-performance API services.

The core innovation is the Persona Orchestration mechanism: the back-end defines multiple software engineering expert roles (e.g., security experts, performance experts, maintainability experts), each with specific professional backgrounds, focus areas, and evaluation criteria. After users submit content to be reviewed, each expert independently generates opinions, which are then comprehensively analyzed by the Judge module to identify consensus and disagreements, and output structured conclusions with detailed reasoning—ensuring decisions are interpretable and auditable.

Technical Implementation Details

• Multi-LLM backend support: Compatible with OpenAI GPT series and Google Gemini series, with flexible switching via environment variables (can be set to auto-selection mode) to facilitate comparison of different model performances.
• Simulation mode: When no API key is configured, the back-end returns preset responses to support independent front-end development and testing.
• Deployment convenience: Docker Compose starts the service stack with one click, eliminating environment configuration issues; provides environment template files to guide secure management of sensitive information such as API keys.

Application Scenarios and Value Proposition

Consensia is designed to address pain points in software engineering: traditional code review is subjective, inconsistent in standards, and high in cost; single LLM reviews lack multi-dimensional perspectives. Its value lies in balancing the scalability advantages of AI with the multi-angle review capabilities of human experts.

Specific scenarios:

1. Code quality assessment (multi-dimensional scoring for security, performance, readability, etc.)
2. Technical solution selection (comparing the pros and cons of architectures)
3. Pull Request automatic pre-review (providing structured feedback before manual review)
4. Knowledge inheritance (encoding the review experience of senior engineers into expert personas)

Research Significance and Potential Impact

Academic level: Touches on core issues of LLM evaluation—can models reliably perform meta-evaluation? Does role-play expand knowledge boundaries? How credible is the arbitrator's consensus? These questions have reference value for AI safety and alignment research.

Engineering level: Demonstrates a new application mode of LLMs in software engineering workflows (decision support rather than just code generation). If feasible, it can reduce the cognitive burden of code review and improve consistency and coverage.

Limitations and Future Directions

Current limitations:

1. Expert personas rely on manual design; automatic optimization remains to be explored.
2. The credibility of the arbitrator has not been verified on a large scale, and systematic biases may exist.
3. Only single-round debates are supported; multi-round iteration mechanisms are lacking.

Future plans:

• Introduce real API calls to replace simulated responses.
• Support uploading resumes/trait libraries to enrich expert definitions.
• Track debate history to implement persona memory.
• Expand the arbitrator's output to include structured reasons and confidence scores.

Conclusion

Consensia represents an attempt to explore LLMs as trusted arbitrators, aiming to build a new paradigm of human-AI collaboration—neither blindly trusting a single AI judgment nor fully relying on manual review. Regardless of whether the vision is ultimately realized, the pursuit of interpretable consensus promotes a deeper understanding of AI's capabilities and limitations.