Oct 27 – Nov 2, 2025

Shorter but not Worse: Frugal Reasoning via Easy Samples as Length Regularizers in Math RLVR

arXiv · Nov 2 · NLP LLM

A new method is proposed to reduce the verbosity of LLMs in step-by-step reasoning by retaining moderately easy problems during Reinforcement Learning with Verifiable Rewards (RLVR) training. This approach acts as an implicit length regularizer, preventing the model from excessively increasing output length on harder problems. Experiments using Qwen3-4B-Thinking-2507 show the model achieves baseline accuracy with nearly twice shorter solutions.

LLM-based Multi-class Attack Analysis and Mitigation Framework in IoT/IIoT Networks

arXiv · Oct 30 · Research NLP

This paper introduces a framework that combines machine learning for multi-class attack detection in IoT/IIoT networks with large language models (LLMs) for attack behavior analysis and mitigation suggestion. The framework uses role-play prompt engineering with RAG to guide LLMs like ChatGPT-o3 and DeepSeek-R1, and introduces new evaluation metrics for quantitative assessment. Experiments using Edge-IIoTset and CICIoT2023 datasets showed Random Forest as the best detection model and ChatGPT-o3 outperforming DeepSeek-R1 in attack analysis and mitigation.

Climate Adaptation-Aware Flood Prediction for Coastal Cities Using Deep Learning

arXiv · Oct 29 · Research CV

Researchers have developed a CNN-based deep learning model for predicting coastal flooding in cities under various sea-level rise scenarios. The model utilizes a vision-based, low-resource DL framework and is trained on datasets from Abu Dhabi and San Francisco. Results show a 20% reduction in mean absolute error compared to existing methods, demonstrating potential for scalable coastal flood management.

BRIQA: Balanced Reweighting in Image Quality Assessment of Pediatric Brain MRI

arXiv · Oct 30 · CV Healthcare