Disagreement-guided routing decides when a small model needs stronger help
The method uses model-to-model disagreement instead of labelled correctness signals, with an explicit assumption about the stronger model.
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The method uses model-to-model disagreement instead of labelled correctness signals, with an explicit assumption about the stronger model.
The study focuses on Mandarin-speaking students in computing courses, evaluating answers, translations and glossary generation separately.
A hierarchical task network tracks dependencies while local orchestrators dispatch workers, allowing decomposition to change as evidence arrives.
The system separates execution from analysis and adds synthesis, while keeping the model, trainer and budget fixed within comparisons.
A conflict-graph model adds overlooked spacing constraints to DSA-assisted multiple patterning, with a neural network guiding local refinement.
Three clock settings separate serving latency, token-related delay and an idealized pause, exposing the cost of late decisions.
The framework adds a telecom reasoning module and a domain-specific evaluation suite, with results from simulation and an O-RAN testbed.
The paper separates predictable labels from actual decision sensitivity and finds effects that do not transfer uniformly across models.
The framework supplies annotation conventions through in-context demonstrations, with dynamic retrieval performing best in the reported comparisons.
The research alternates trajectory rewriting with policy updates, separating partial progress from strict task completion.
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