cs papers | Gist.Science

Verifier Exploitation in NLI-Guided Iterative Refinement: A Controlled Empirical Analysis

This paper demonstrates that verifier exploitation in NLI-guided iterative refinement is an architectural vulnerability inherent to single-metric feedback loops rather than an optimization artifact, showing how a training-free, zero-gradient pipeline can systematically degrade faithfulness while inflating NLI scores through content truncation, and proposes a universal, annotation-free detection protocol to identify such structural risks.

Arnav Gupta2026-06-30

💻 computer science

Reinforcement Learning for Dynamic Model Selection an d Attention-Guided Fracture Detection in Multi-Anatomic al Region X-ray Imagery

This paper proposes Dyna-FractureNet, a novel framework leveraging Cooperative Multi-Agent Reinforcement Learning to dynamically select optimal model ensembles and coordinate multi-level attention mechanisms, thereby significantly enhancing the accuracy, robustness, and efficiency of fracture detection across diverse anatomical regions in X-ray imagery.

Meng Zhang, Peng Li, Shuaihu Pan, Pengcheng Xie, Luanning Li2026-06-30

💻 computer science

Photovoltaic Panel Failure Detection Using Class-Conditioned Generative Adversarial Networks

This paper proposes an Auxiliary Classifier Generative Adversarial Network (AC-GAN) framework that synthesizes high-fidelity thermal images of rare photovoltaic defects to address class imbalance, thereby significantly improving the probabilistic calibration and reliability of AI-driven fault diagnosis in solar operations.

Md Moshfiqure Rahman, Paroma Chatterjee, Varsha Sen, Kamrul Hasan, Prashnna Gyawali, Anurag K. Srivastava2026-06-30

💻 computer science

Real-Time Cognitive State Decoding from EEG Using a Hybrid Quantum--Classical Neural Framework

This paper introduces QNeuroGen, a hybrid quantum-classical neural framework that achieves real-time cognitive state decoding from noisy EEG signals with 89.2% accuracy and 48 ms latency by integrating structured preprocessing with variational quantum feature encoding.

Srinivasa Gowda, P. Vanajakshi, Nischala GS2026-06-30

💻 computer science

Enhancing Breast Cancer Prediction from Histopathological Images using Hybrid CNN-LSTM Architecture

This study proposes a hybrid ResNet101-LSTM architecture optimized with Adam that achieves high accuracy and specificity in classifying breast cancer from BreaKHis histopathological images at 40x magnification, outperforming other deep learning models in both binary and multiclass scenarios.

Rajyalakshmi Bandi, PULLARAO CHENNAMSETTY2026-06-30

💻 computer science

Quantum Neural Networks for Wind Energy Forecasting: A Comparative Study of Performance and Scalability with Classical Models

This study demonstrates the viability of Quantum Neural Networks for wind energy forecasting by systematically evaluating 12 configurations against classical models, revealing that specific QNN designs can achieve competitive performance (up to R² = 0.94) despite the computational trade-offs inherent in NISQ-era simulations.

Batuhan Hangun, Onder Eyecioglu, Mehar Ali, Oguz Altun, Korhan Kayisli2026-06-30

💻 computer science

Quantum Hash Function Based on Spectral Properties of Graphs and Discrete Walker Dynamics

This paper introduces QGH-256, a novel quantum hashing algorithm that generates 256-bit fingerprints by mapping messages to weighted graphs on a toroidal grid and utilizing Quantum Phase Estimation to extract spectral features that capture both structural and dynamical properties, demonstrating strong cryptographic sensitivity and feasibility through Qiskit simulations.

Mohana Priya Thinesh Kumar, Pranavishvar Hariprakash2026-06-30

💻 computer science

DDANet: A Dense Dual-Attention Network for Robust and Generalizable Semantic Segmentation Across Diverse Medical Imaging Tasks

This paper introduces DDANet, a novel Dense Dual-Attention Network that combines dense skip connections and dual-attention mechanisms to achieve superior boundary delineation and generalization in medical image segmentation, outperforming baseline models with high accuracy, Dice, and IoU scores.

GOMATHI P, DEVI PRIYA2026-06-30

💻 computer science

TinyOL-ARLDA On-Device Learning Auto Regularized Linear Discriminant Analysis for Sound-Based Predictive Maintenance

This paper presents TinyOL-ARLDA, a lightweight, on-device incremental learning framework based on regularized Linear Discriminant Analysis that enables real-time, cloud-independent sound-based predictive maintenance on microcontrollers with over 94% accuracy and a memory footprint under 20 KB.

Abdelwahed ELMOUTAOUKKIL, Mohammed HAMLICH2026-06-30

💻 computer science

CGS-RAG:Community-Aggregated Graph Semantic Retrieval-Augmented Generation

This paper proposes CGS-RAG, a novel framework that leverages the HDRF algorithm for customizable community partitioning and semantic reporting to overcome the limitations of traditional and graph-enhanced RAG systems by balancing global semantic consistency with efficient resource usage in dynamic knowledge scenarios.

Shengwei Ji, Xiaoxue Zhang, Yue Huang, Dingyuan Li2026-06-30

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