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Results for "adaptive learning".

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7 results for "adaptive learning"

ARXIV CS.AI

Learning in Blocks: A Multi Agent Debate Assisted Personalized Adaptive Learning Framework for Language Learning

Most digital language learning curricula rely on discrete-item quizzes that test recall rather than applied conversational proficiency. When progression is driven by quiz performance, learners can adv…

· 4 views
ARXIV.ORG

From Coarse to Fine: Self-Adaptive Hierarchical Planning for LLM Agents

Large language model-based agents have recently emerged as powerful approaches for solving dynamic and multi-step tasks. Most existing agents employ planning mechanisms to guide long-term actions in d…

· 4 views
ARXIV.ORG

AdaMamba: Adaptive Frequency-Gated Mamba for Long-Term Time Series Forecasting

Accurate long-term time series forecasting (LTSF) requires the capture of complex long-range dependencies and dynamic periodic patterns. Recent advances in frequency-domain analysis offer a global per…

· 4 views
ARXIV CS.AI

Cloudless-Training: A Framework to Improve Efficiency of Geo-Distributed ML Training

Geo-distributed ML training can benefit many emerging ML scenarios (e.g., large model training, federated learning) with multi-regional cloud resources and wide area network. However, its efficiency i…

· 4 views
ARXIV CS.AI

BiTA: Bidirectional Gated Recurrent Unit-Transformer Aggregator in a Temporal Graph Network Framework for Alert Prediction in Computer Networks

Proactive alert prediction in computer networks is critical for mitigating evolving cyber threats and enabling timely defensive actions. Temporal Graph Neural Networks (TGNs) provide a principled fram…

· 4 views
ARXIV CS.AI

Representation Homogeneity and Systemic Instability in AI-Dominated Financial Markets: A Structural Approach

This paper investigates how similarity in the informational representation of market states among Artificial Intelligence (AI) trading agents can generate systemic instability in financial markets. We…

· 4 views
ARXIV.ORG

Tandem: Riding Together with Large and Small Language Models for Efficient Reasoning

Recent advancements in large language models (LLMs) have catalyzed the rise of reasoning-intensive inference paradigms, where models perform explicit step-by-step reasoning before generating final ans…

· 4 views