co-processing
MassGen's collaboration of intelligences utilizes a multi-stage processing model: when a user submits a task, the system distributes the question to all configured AI models, each of which maintains an independent work state but monitors the processing progress of the others. For example, in a Q&A task, different models may generate their own versions of the answer.
Consensus-building mechanisms
The system achieves result optimization by:
- Initial results comparison: collecting the first round of outputs from each model for cross-validation
- Difference-point labeling: automatically identifying key differences between answers
- Iterative optimization: multiple rounds of discussion by intelligentsia for points of disagreement, which can be adjusted through the
--consensusParameter (default 0.5) controls consensus stringency - Final synthesis: weighted voting or logic fusion algorithms are used to generate harmonized results
Typical Application Examples
When dealing with complex problems such as "explaining quantum entanglement":
- Gemini may focus on mathematical representations
- GPT-4o is good at explaining things in layman's terms
- The system combines the best of both worlds to produce a final answer that is both theoretically deep and easy to understand.
This answer comes from the articleMassGen: A Multi-Intelligence Collaborative Task Processing SystemThe
May not be reproduced without permission:AI productivity tools " How does MassGen's multi-intelligent body collaboration mechanism work?
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