RegulaTutor: A Theory-Informed Framework for Long-Term Personalized Learning
cs.AI
Submitted: 2026-10-09
Updated: 2026-10-09
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- HarnessForge: Joint Harness and Policy Evolution for Adaptive Agent Systems
- Co-Harness: Co-Evolving Harnesses and Model Weights for LLM Agents
- Are Agents Ready to Teach? A Multi-Stage Benchmark for Real-World Teaching Workflows
- Tailoring Scaffolding to Diagnostic Strategies: Theory-Informed LLM-Based Agents
- SIA: Self Improving AI with Harness & Weight Updates
- Meta-Harness: End-to-End Optimization of Model Harnesses
- Harness Updating Is Not Harness Benefit: Disentangling Evolution Capabilities in Self-Evolving LLM Agents
- TeachLM: Post-Training LLMs for Education Using Authentic Learning Data
- Teaching According to Students' Aptitude: Personalized Mathematics Tutoring via Persona-, Memory-, and Forgetting-Aware LLMs
- DeepTutor: Towards Agentic Personalized Tutoring
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