Paradigm
Summary
Paradigm turns any goal into a personalized, adaptive learning path.
Similar Articles
From Correctness to Preference: A Framework for Personalized Agentic Reinforcement Learning
This paper proposes a unified framework for personalized agentic reinforcement learning that decouples generic task rewards from personalized preference rewards, introducing PARPO and PSGM for preference-aligned policy optimization and skill retrieval.
Evaluating Adaptive Personalization of Educational Readings with Simulated Learners
Researchers from Arizona State University present a framework for evaluating adaptive personalization of educational reading materials using theory-grounded simulated learners, incorporating memory models, misconception revision, and Bayesian Knowledge Tracing. Experiments across three subjects show adaptive reading significantly improved outcomes in computer science but had mixed results in chemistry and biology.
From Feasibility to Desirability: Plan, Learn, Adapt (PLA) Framework for Personalized On-Device Itinerary Generation
The paper proposes the Plan, Learn, Adapt (PLA) framework for personalized on-device itinerary generation, combining feasibility-guaranteed combinatorial planning with human preference learning via a Bradley-Terry reward model. In deployment, it achieved a 91% increase in itinerary completion rates with low latency, outperforming frontier LLMs in feasibility.
PersonaTrail: Benchmarking Personalized Web Agents through Browsing Trails
PersonaTrail is a benchmark for personalized web agents that uses realistic browsing trajectories to evaluate agents' ability to infer user preferences and recall past information. The paper also proposes PACMem, a memory framework that outperforms existing baselines on both tasks.
PRAGMA: Evaluating Personalized Guidance with Memory Alignment in Lifelong Conversations
The paper presents PRAGMA, a benchmark for evaluating personalized guidance in lifelong conversations, revealing that current large language model systems struggle with effective memory retrieval and reasoning for user-specific guidance.