# Consensus vs Single-Agent: A Methodology Comparison

This vignette describes the architectural differences between multi-LLM consensus and single-agent approaches for cell type annotation, along with their respective trade-offs.

## Architectural Overview

### Single-Agent Systems

Single-agent approaches use specialized AI systems with predefined roles:

- Specialized roles: Different agents handle specific annotation tasks
- Sequential processing: Agents work in a pipeline fashion
- Role-based validation: Quality control through dedicated validation agents
- Structured workflows: Fixed processing sequences

### Multi-LLM Consensus Systems

Consensus frameworks leverage multiple independent models:

- Parallel processing: Multiple models analyze simultaneously
- Collective decision-making: Annotations are determined by agreement across models
- Iterative refinement: Discussion rounds for clusters where models disagree
- Adaptive complexity: More discussion is allocated to difficult cases

## Methodological Differences

### Single-Agent Approach

Strengths: - Clear role definition: Each agent has specific responsibilities - Streamlined workflows: Predictable processing pipelines - Focused optimization: Agents can be fine-tuned for specific tasks - Lower initial complexity: Easier to implement and understand

Limitations: - Sequential bottlenecks: Failure in one agent affects the entire pipeline - Limited model diversity: Typically relies on one underlying LLM family - Rigid processing: Difficult to adapt to edge cases - Single point of failure: Agent malfunction can compromise results

### Consensus Approach

Strengths: - Error correction: Multiple models can catch each other’s mistakes - Model diversity: Leverages different training approaches and strengths - Adaptive processing: More resources allocated to difficult cases - Transparent uncertainty: Clear metrics for prediction confidence

Challenges: - Initial complexity: Requires coordination between multiple models - Resource coordination: Must manage multiple API calls efficiently - Consensus building: Additional time for deliberation processes - Model compatibility: Ensuring different models work together effectively

## Performance

For benchmark results comparing the two approaches, see Yang et al. (2026):

Yang, C., Zhang, X., & Chen, J. (2026). Large language model consensus substantially improves the cell type annotation accuracy for scRNA-seq data. Communications Biology. https://doi.org/10.1038/s42003-026-10420-8

## Cost and Resource Trade-offs

Aspect | Single-Agent | Consensus
--- | --- | ---
API calls per cluster | Fewer | More (multiple models)
Cost per run | Lower | Higher per run
Two-stage optimization | N/A | Reduces calls when models agree early
Scalability | Good | Good, with caching support

The two-stage consensus approach in mLLMCelltype can reduce API calls when models agree early, since only clusters without initial consensus proceed to the deliberation stage.

## Practical Considerations

### When single-agent approaches may suffice:

- Standardized datasets with well-characterized tissues
- High-throughput screening of many similar datasets
- Limited API budget
- Straightforward annotation tasks

### When consensus approaches may be preferable:

- Novel biological contexts where model agreement provides additional confidence
- Work intended for publication, where uncertainty quantification is useful
- Complex tissues with many similar cell types
- Cases where identifying uncertain annotations is important

## Hybrid Approaches

Advanced workflows can combine both approaches:

1. Initial screening: Single-agent for clear cases
2. Consensus validation: Multi-model for uncertain cases
3. Expert review: Human validation for critical decisions

This tiered approach balances cost and thoroughness.

## Summary

Both methodologies have distinct strengths. Single-agent systems are simpler and less expensive per run, while consensus approaches provide uncertainty quantification and cross-model validation. The choice depends on the specific requirements of accuracy, cost, and biological complexity for a given project.

## Next Steps

- Why Choose Consensus? - Details on the consensus methodology
- Getting Started Guide - Practical implementation tutorial
- Performance Benchmarks - Detailed comparisons
- API Reference - Technical documentation