Unverified50% confidenceFactExact time
The non-interactive paradigm allows multiple heterogeneous SLMs to independently generate candidate solutions, after which a router orchestrates these cached samples without requiring further interaction between models
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50%
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Long-term
Relevance
9/15/2026
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UnverifiedThe non-interactive paradigm circumvents the bottleneck dependencies on SLM context windows and computation by fully decoupling generation from selection73% similarUnverifiedFew-shot examples convey samples rather than rules, and the model's induction process is probabilistic with systematic biases, so examples and Schema should be used in conjunction rather than as substitutes for each other.72% similarUnverifiedThis method is model-agnostic and plug-and-play, can be directly applied during the inference stage without modifying model parameters, and can be applied as an external wrapper layer to different families of large language models.72% similarUnverifiedSub-agents specify the use of a smaller model via a model override parameter and receive self-contained prompts that do not depend on the main loop's context.72% similarUnverifiedJoint modeling allows the multimodal associations between sequences and structures to be captured end-to-end within the model, rather than being split across different training stages.71% similar
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