Seed Diffusion's diffusion modeling architecture is naturally suited to solving global planning problems:
- Structured a priori learning: Through the constrained order diffusion technique, the model automatically recognizes the dependencies between variable declarations and invocations
- Non-local editing capabilities: Modifying a function name will synchronize the update of all call points, avoiding syntax errors caused by local substitution in traditional models.
- Two-stage training mechanism: The editorial-based diffusion training phase specifically reinforces code reasonableness judgments
Suggestion for operation: keep the complete context when inputting the original code, and modify the instruction to specify the functional goal rather than the specific realization, for example, requesting that theMoving procedural code to an object-oriented paradigmInstead of directly specifying the variable name to modify.
This answer comes from the articleSeed Diffusion: Validating High-Speed Language Models for Next-Generation ArchitecturesThe
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