MisEdu-RAG builds concept and instance hypergraphs for two-stage retrieval of pedagogical knowledge and student errors, improving feedback quality on the MisstepMath benchmark by 10.95% token-F1 and up to 15.3% on response dimensions.
Advances in neural information processing systems 33, 9459–9474 (2020)
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LiteSemRAG delivers leading MRR@10 on three benchmarks using only lightweight semantic graph methods and zero LLM tokens.
ArguMath is an AI-simulated classroom environment that enables pre-service math teachers to practice orchestrating mathematical argumentation through customizable scenarios, AI student interactions, and structured reflection, with preliminary user feedback indicating potential benefits for theory-
Trans-RAG uses multi-stage query transformations to retrieve from mathematically isolated per-organization vector spaces, achieving 89.90° angular separation, 99.81% isolation, and only 3.5% nDCG@10 drop versus homomorphic encryption baselines.
citing papers explorer
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MisEdu-RAG: A Misconception-Aware Dual-Hypergraph RAG for Novice Math Teachers
MisEdu-RAG builds concept and instance hypergraphs for two-stage retrieval of pedagogical knowledge and student errors, improving feedback quality on the MisstepMath benchmark by 10.95% token-F1 and up to 15.3% on response dimensions.
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LiteSemRAG: Lightweight LLM-Free Semantic-Aware Graph Retrieval for Robust RAG
LiteSemRAG delivers leading MRR@10 on three benchmarks using only lightweight semantic graph methods and zero LLM tokens.
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ArguMath: AI-Simulated Environment for Pre-Service Teacher Training in Orchestrating Classroom Mathematics Argumentation
ArguMath is an AI-simulated classroom environment that enables pre-service math teachers to practice orchestrating mathematical argumentation through customizable scenarios, AI student interactions, and structured reflection, with preliminary user feedback indicating potential benefits for theory-
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Trans-RAG: Query-Centric Vector Transformation for Secure Cross-Organizational Retrieval
Trans-RAG uses multi-stage query transformations to retrieve from mathematically isolated per-organization vector spaces, achieving 89.90° angular separation, 99.81% isolation, and only 3.5% nDCG@10 drop versus homomorphic encryption baselines.