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The rapid growth of scientific literature makes survey writing increasingly challenging, motivating automatic survey generation. Yet, existing systems must search an open space of papers and an open space of concepts with no trace of how the community itself has navigated them, leading to incomplete scope, weak taxonom...
QuAD, a framework for quantitative pre-analysis assessment of qualitative response datasets, is introduced and a content-aware multi-agent system with two-stage instruction generation and adaptive anchors to improve scoring consistency is proposed.
RegulAR models task instructions as a hierarchical dependency graph and combines this structure with a Multimodal Large Language Model (MLLM) to interpret egocentric observations during execution and enables RegulAR to track progress, identify deviations by error type, estimate their impact on later steps, and deliver...
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