10xLearners: Multi-Agent Personalized Learning
A multi-agent tutoring system that builds concept graphs, adaptive study plans, and assessment loops from learner-owned documents.
Research
Papers, experiments, and system prototypes focused on AI reliability and learning.
A multi-agent tutoring system that builds concept graphs, adaptive study plans, and assessment loops from learner-owned documents.
A three-stage interpretability pipeline that improves LLM failure-pattern discovery by controlling for question difficulty, diffing SAE latents, and scoring the discovered pattern set with set-level metrics.
LLMs benefit from extra test-time computation, but fixed reasoning budgets waste compute on easy problems and still under-explore hard ones. We train a three-action policy (continue, refine, terminate) with GRPO, Adaptive Length Penalty (ALP), and DeGRPO-style control-token weighting on Qwen2.5-Math-7B-Instruct (LoRA), and evaluate on MATH-500 with accuracy, efficiency, and action-usage analysis.
Undergraduate research (2012) on automatic Indonesian news categorization using TF-IDF features and Support Vector Machines.