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This work evaluates 25 model and agent systems under a controlled tool-using protocol, then uses controlled studies to locate failures in evidence access, tool selection, memory, reasoning, interaction, and scientific execution.
This work proposes Ontology-Masked Reasoning Autoencoding (OMRA), a controlled intervention that masks and reconstructs over-explained spans while preserving stance, factual content, and coherence and reduces the measured gap between AI preference and real user engagement.
HexLogicAgent is proposed, a framework that first organizes the meaning of natural-language statements and then guides logical reasoning through structured verification, supported by a logical hexagon theory, which explains why a complete structure of opposing meanings is necessary for reliable reasoning.
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