ITAS, a multi-agent tutoring system with quantum-specialized LLM agents, cloud infrastructure, and analytics, was deployed in a real quantum computing course and provided evidence that agent specialization improves reliability while surfacing curriculum gaps.
Toward personalizing quan- tum computing education: An evolutionary LLM-powered ap- proach
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Introduces versioned late materialization to eliminate data redundancy in ultra-long sequence training for DLRMs by storing histories once and reconstructing via pointers at training time.
Priority PayGo keeps multi-agent tutoring responses under 4 seconds even at 50 concurrent users, while costs stay below textbook prices per student.
citing papers explorer
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From Prototype to Classroom: An Intelligent Tutoring System for Quantum Education
ITAS, a multi-agent tutoring system with quantum-specialized LLM agents, cloud infrastructure, and analytics, was deployed in a real quantum computing course and provided evidence that agent specialization improves reliability while surfacing curriculum gaps.
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Versioned Late Materialization for Ultra-Long Sequence Training in Recommendation Systems at Scale
Introduces versioned late materialization to eliminate data redundancy in ultra-long sequence training for DLRMs by storing histories once and reconstructing via pointers at training time.
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Latency and Cost of Multi-Agent Intelligent Tutoring at Scale
Priority PayGo keeps multi-agent tutoring responses under 4 seconds even at 50 concurrent users, while costs stay below textbook prices per student.