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It is argued that behavioral evaluation understates what models encode, while probing alone overstates what they deploy, in a three-level evaluation framework measured on the same items under the same binary decision.
The architectural patterns validated here, including layer-as-budget compression, semantic-locality scheduling, and separation of generation and review, transfer to the broader class of knowledge-intensive, multi-agent LLM applications.
QLWF is a visual workflow platform that turns natural-language research descriptions into executable workflows through an AI assisted five-stage pipeline that supports incremental refinement, so saved workflows can be revised by changing only the parts that need to change rather than being rebuilt from scratch.
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