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Deggendorf Institute of Technology

Academic institutioneurope · de
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Research library23linked papers
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Selected work

Representative Papers

Lightweight PID-Based Drift Mitigation for Cellular Traffic Forecasting

Aug 08, 2026

This work addresses the challenge of traffic prediction in 6G mobile networks, where existing models suffer significant performance degradation under data distribution shifts and rely on costly retraining. To overcome this, the authors propose a lightweight online error correction framework that, for the first time, integrates a proportional–integral–derivative (PID) controller into network traffic forecasting as a correction layer. This layer dynamically compensates for prediction biases from a hierarchical spatio-temporal model (HiSTM) without updating its parameters, enabling real-time adaptation to distribution drift. The resulting end-to-end online correction architecture achieves substantial improvements in accuracy and robustness, reducing mean absolute error (MAE) by 30.18% and root mean square error (RMSE) by 26.68% on average across diverse drift scenarios, while maintaining low computational overhead and high efficiency.

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Evaluating Chunking Strategies for Retrieval-Augmented Generation on Academic Texts

Jul 02, 2026

This study investigates optimal text chunking strategies for enhancing the response quality of Retrieval-Augmented Generation (RAG) systems when applied to structurally complex academic papers. We systematically compare semantic clustering, fixed-length, and recursive chunking approaches, evaluating output faithfulness and relevance using the RAGAs framework. To our knowledge, this is the first empirical comparison of multiple chunking strategies on long-form scholarly texts. Our findings indicate that semantic clustering does not significantly outperform simpler methods, and that question type—generic versus document-specific—substantially influences system performance. Furthermore, we identify limitations in the reliability of RAGAs’ faithfulness metric for such tasks, suggesting a need for more robust evaluation measures in academic RAG applications.

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Recent publications

Latest Papers

Lightweight PID-Based Drift Mitigation for Cellular Traffic Forecasting

Aug 08, 2026

This work addresses the challenge of traffic prediction in 6G mobile networks, where existing models suffer significant performance degradation under data distribution shifts and rely on costly retraining. To overcome this, the authors propose a lightweight online error correction framework that, for the first time, integrates a proportional–integral–derivative (PID) controller into network traffic forecasting as a correction layer. This layer dynamically compensates for prediction biases from a hierarchical spatio-temporal model (HiSTM) without updating its parameters, enabling real-time adaptation to distribution drift. The resulting end-to-end online correction architecture achieves substantial improvements in accuracy and robustness, reducing mean absolute error (MAE) by 30.18% and root mean square error (RMSE) by 26.68% on average across diverse drift scenarios, while maintaining low computational overhead and high efficiency.

0 citationsRead paper

Evaluating Chunking Strategies for Retrieval-Augmented Generation on Academic Texts

Jul 02, 2026

This study investigates optimal text chunking strategies for enhancing the response quality of Retrieval-Augmented Generation (RAG) systems when applied to structurally complex academic papers. We systematically compare semantic clustering, fixed-length, and recursive chunking approaches, evaluating output faithfulness and relevance using the RAGAs framework. To our knowledge, this is the first empirical comparison of multiple chunking strategies on long-form scholarly texts. Our findings indicate that semantic clustering does not significantly outperform simpler methods, and that question type—generic versus document-specific—substantially influences system performance. Furthermore, we identify limitations in the reliability of RAGAs’ faithfulness metric for such tasks, suggesting a need for more robust evaluation measures in academic RAG applications.

0 citationsRead paper