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PASC constructs a structure-only prior representation, estimates a target-edge structural prior score, and compares this score with a local signed-context cue to derive a conflict residual, which calibrates signed attention aggregation, target-edge gated fusion, and regime-adaptive optimization.
Agentic tutoring systems increasingly rely on large language models (LLMs) to generate real-time feedback, yet risky outputs, such as answer leakage, contradiction, and hallucination, can directly affect learning. Answer leakage is a central concern in this setting. It exposes problem solutions to students and can be v...
This work identifies a fundamental inconsistency across the structural, propagation, and semantic layers of existing models that leads to biased representations learned from sparse or noisy datasets, and proposes a unified framework named SSC-Loop that treats signed social recommendation as the maximization of structur...
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