cs papers | Gist.Science

Repair Instead of Retraining: A Constraint-Guided Framework for Neural Network Repair

This paper introduces a constraint-guided framework that repairs deployed neural networks by localizing fault-relevant weights via DeepSHAP, collecting symbolic constraints through concolic testing, and optimizing updates with Max-SMT, demonstrating through extensive experiments that systematic design-space exploration reveals critical repair strategies—such as bias-only modifications—that significantly reduce adversarial and backdoor vulnerabilities while preserving model fidelity.

Ting Yu Liu, Fang Yu2026-07-22

💻 computer science

Towards Intelligent UAV Path Planning: A Systematic Review of Hybrid Reinforcement Learning and Metaheuristic Optimization

This systematic review of 32 studies (2022–2025) identifies four hybrid Reinforcement Learning and Metaheuristic architectures for independent UAV path planning, finding that while these frameworks improve adaptability and convergence, their real-world readiness is currently limited by a complete lack of physical hardware validation, code reproducibility, and rigorous statistical testing.

Claudio Henríquez, Felipe Zambrano, Broderick Crawford, Francisco Cruz2026-07-22

💻 computer science

AIOps-Driven DevOps Pipeline for Predictive Deployment Risk Scoring, Anomaly Detection and Automated Downtime Reduction in CI/CD Environments

This paper proposes an integrated AIOps framework that combines XGBoost-based predictive risk scoring, autoencoder-driven anomaly detection, and Random Forest-based automated remediation to proactively mitigate deployment failures and significantly reduce mean time to recovery in CI/CD environments.

Abinaya Selvaraj, Parimala G2026-07-22

💻 computer science

Wire Arc Additive Manufacturing: A Progressive Multi-Architecture Computer Vision Framework for Real-Time Defect Detection

This paper presents a progressive multi-architecture computer vision framework for real-time defect detection in Wire Arc Additive Manufacturing (WAAM), systematically evaluating and comparing interpretable two-step models, end-to-end deep learning networks, and a novel learnable preprocessing architecture (MCFE-Net) to optimize the trade-offs between computational efficiency, interpretability, and anomaly detection accuracy.

Soham Agarwal, Shahana Bano, Anil Prathuru, Somasundar Kannan, Deepika Nikam, Shiva Sekar, Sagar Nikam2026-07-22

💻 computer science

Testing the Consent-Friction Functional: Preference-Design Artifacts and the Contention-Bounded Limits of Coordination Friction in Multi-Agent Reinforcement Learning

This empirical study tests a consent-friction functional in multi-agent reinforcement learning, rejecting its specific monotone form as a universal predictor while demonstrating that observed coordination effects are largely artifacts of design choices and that the sole surviving structural effect—where cooperative alignment improves outcomes—is driven by increased feasibility in shared-resource environments rather than reduced learning friction.

Murad Farzulla2026-07-22

💻 computer science

Accent-Aware User Interaction in Consumer Electronics via Multilingual Speech Recognition

This paper presents a comprehensive comparative study of machine learning and deep learning approaches for multilingual speech accent recognition using the Speech Accent Archive and AccentDB datasets, demonstrating that hybrid CNN–BiLSTM models achieve superior accuracy (up to 99.18%) and highlighting the feasibility of deploying these systems for personalized, accent-aware interactions in consumer electronics.

Ramesh Kumar Bhukya2026-07-22

💻 computer science

Adaptive Student Knowledge Assessment in E-Tutoring Systems: An Intuitionistic Fuzzy Logic and Ontology Modeling Approach

This paper proposes an adaptive E-tutoring system that integrates Atanassov's Intuitionistic Fuzzy Logic with an ontology-based domain model to effectively handle uncertainty in student knowledge assessment, enabling personalized teaching materials and dynamic feedback through a functional Python prototype.

Hussein Ali Ahmed Hussain, Sultan Almutairi, Mukhtar M. E. Mahmoud, Abdullah Albalawi, Laszlo Kovacs2026-07-22

💻 computer science

The Lie Awareness Spectrum: How Large Language Models Recognize, Rationalize, and Refuse Deception

This empirical study introduces a "Lie Awareness Spectrum" to characterize how GPT-5.4, Claude 4.6 Sonnet, and Gemini 3.1 Pro differentially recognize, rationalize, and refuse deception, revealing distinct ethical architectures and a paradox where models resist direct lying commands yet succumb to implicit social pressure.

Abbas Hamidavi2026-07-22

💻 computer science

AI Evaluation of Expert Testimony in Medical Malpractice

This study demonstrates that AI tools can rapidly retrieve relevant medical literature and effectively detect significant deviations from evidence in expert testimony within medical malpractice cases, particularly highlighting that defense experts often contradict published data more frequently than plaintiff experts.

Mayer Brezis2026-07-22

💻 computer science

What Makes a Review Read as Positive or Negative? Linguistic Signatures of Valence in Online Consumer and Cultural-Product Reviews

This study demonstrates that while sentiment intensity, readability, and lexical diversity reliably distinguish positive from negative reviews across film and consumer-electronics domains, affective punctuation exhibits contradictory valence signals between categories, highlighting the critical need for domain-specific calibration in automated sentiment analysis systems.

Senthil kumar Anantharaman2026-07-22

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