---
title: "Consensus vs Single-Agent: A Methodology Comparison"
description: "Comparison of multi-LLM consensus and single-agent approaches for cell type annotation, covering architecture, trade-offs, and practical considerations."
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Consensus vs Single-Agent: A Methodology Comparison}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

# 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?](https://cafferyang.com/mLLMCelltype/articles/why-consensus.html) - Details on the consensus methodology
- [Getting Started Guide](https://cafferyang.com/mLLMCelltype/articles/getting-started.html) - Practical implementation tutorial
- [Performance Benchmarks](https://cafferyang.com/mLLMCelltype/articles/advanced-features.html) - Detailed comparisons
- [API Reference](https://cafferyang.com/mLLMCelltype/reference/index.html) - Technical documentation
