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)
7 Pith papers cite this work. Polarity classification is still indexing.
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Minos uses a two-tiered multi-agent architecture with retrieval-augmented reasoning and FSM-coordinated agents to reconstruct attack scenarios from provenance data, reporting 0.92 recall and 0.64 precision on 14 scenarios.
LiteSemRAG delivers leading MRR@10 on three benchmarks using only lightweight semantic graph methods and zero LLM tokens.
FinKG-News constructs news-centric financial knowledge graphs to support in-context learning for credit risk report generation across three dimensions, claiming 19-34% quality gains and fewer hallucinations than baselines.
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.
MedSynapse-V proposes a latent memory evolution framework with meta-query prior retrieval, causal counterfactual refinement via RL, and intrinsic memory transition to improve diagnostic accuracy over chain-of-thought baselines in medical VLMs.
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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Minos: A Multi-Agent Collaborative Framework for Provenance-Based Backward Tracking
Minos uses a two-tiered multi-agent architecture with retrieval-augmented reasoning and FSM-coordinated agents to reconstruct attack scenarios from provenance data, reporting 0.92 recall and 0.64 precision on 14 scenarios.
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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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Evidence-Supported Credit Risk Report Generation Using News-Centric Financial Knowledge Graphs
FinKG-News constructs news-centric financial knowledge graphs to support in-context learning for credit risk report generation across three dimensions, claiming 19-34% quality gains and fewer hallucinations than baselines.
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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.
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MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution
MedSynapse-V proposes a latent memory evolution framework with meta-query prior retrieval, causal counterfactual refinement via RL, and intrinsic memory transition to improve diagnostic accuracy over chain-of-thought baselines in medical VLMs.