SolvedByAI

Queue

628 cases were harvested and are backed by a real paper, but they fail the test the index publishes on. Each one is listed here with the reason, so the exclusion can be checked rather than trusted.

All 628 assisted 358 not a result 183 unsupported 87
Paper Year Cited Why it is held
Design of a large language model for improving customer service in telecom o... Electronics Letters 2024 24 assisted The LLM+LangChain system functions as a knowledge-recommendation component within human-operated customer support workflows (its outputs are accepted or rejected by human agents), rather than autonomously delivering the end-to-end service result. “The implementation of this LLM‐based framework within telecom customer service systems has significantly sharpened the precision of knowledge recommen...”
The integration of machine learning into automated test generation: A system... Software Testing Verification and Reliability 2023 24 not a result This is a systematic mapping study (a survey) characterizing 124 existing publications rather than producing a primary result, and its stated contribution is a roadmap for researchers. “We perform a systematic mapping study on a sample of 124 publications.”
Deep learning in fluorescence imaging and analysis Journal of intelligent medicine 2024 24 not a result The abstract explicitly describes a review article surveying deep learning applications in fluorescence imaging, with no primary result produced by an AI system. “This article reviews the latest advances in the application of DL to FI methodologies and their subsequent impact on biology and biomedicine.”
Application of machine learning in breast cancer survival prediction using a... Scientific Reports 2024 23 assisted The paper reports comparative accuracy of trained models (108 models, best 85.56%) on an existing clinical prediction task whose output is explicitly meant to inform human care planning, making the AI a predictive component in a human clinical workflow rather than the agent that produced the paper's primary result. “According to external validation, the DNN model trained with the Shiraz dataset, considering all features, exhibited the highest accuracy (85.56%).”
Clinical Large Language Model Evaluation by Expert Review (CLEVER): Framewor... JMIR AI 2025 23 not a result The paper's contribution is an evaluation framework/methodology in which human doctors judge LLM outputs, the AI systems are test subjects, not agents that solved a task. “we propose CLEVER (Clinical Large Language Model Evaluation–Expert Review), a methodology for blind, randomized, preference-based evaluation by practi...”
Examining Deep Learning Architectures for Crime Classification and Predictio... Forecasting 2021 20 assisted The paper is primarily a comparative benchmark study evaluating deep learning configurations against existing methods and providing recommendations for configuration, rather than presenting the AI's crime prediction as a novel solved result. “a comparative study of 10 state-of-the-art methods against 3 different deep learning configurations is conducted”
Deep reinforcement learning for real-world quadrupedal locomotion: a compreh... Intelligence & Robotics 2022 19 not a result This is a survey/review article that summarizes existing DRL literature on quadrupedal locomotion rather than presenting a primary result produced by an AI system. “In this review article, we systematically organize and summarize relevant important literature, covering DRL algorithms from problem setting to advanc...”
The future of cosmological likelihood-based inference: accelerated high-dime... The Open Journal of Astrophysics 2024 19 assisted The ML components (emulation, learned harmonic mean) serve as acceleration tools inside a human-designed Bayesian inference pipeline, demonstrated on simulated data to reproduce results already obtainable by traditional methods, rather than the AI itself producing a novel result. “Specifically, we combine (i) emulation, where a machine learning model is trained to mimic cosmological observables, e.g. CosmoPower-JAX; (ii) differe...”
Use of responsible artificial intelligence to predict health insurance claim... Exploration of Digital Health Technologies 2024 19 assisted This is a model-comparison study reporting prediction accuracy on an existing task, where the AI outputs are meant to inform human decisions on insurance policy formulation and pricing rather than the AI itself performing the end-to-end task. “The findings demonstrate that the XGBoost and RF models outperformed the other algorithms, displaying the highest R-squared values of 79% and 77% and...”
Embedding the weather prediction errors (WPE) into the photovoltaic (PV) for... Journal of Forecasting 2024 18 assisted The paper's stated novelty is human-engineered features and adjustment coefficients wrapped around standard ML models to improve accuracy metrics on an existing PV-forecasting task, making the AI a prediction component inside a human-designed and human-completed method rather than the agent that itself constitutes the result. “The novelty of the research we conduct consists in proposing several features and determining the coefficients to adjust the PVF based on WPE.”
A novel procedure for cable damage identification of cable-stayed bridge usi... Structural Health Monitoring 2024 18 assisted The paper's contribution is a human-engineered procedure (PSO-based model updating, FE simulation, hyperparameter tuning) in which CatBoost serves as one predictive component, validated by accuracy comparison against other ML methods on a benchmark bridge rather than the AI itself producing a novel damage-identification result. “a procedure is proposed to identify the cable damage in the cable-stayed bridges using the particle swarm optimization (PSO) and the categorical gradi...”
Dynamic microarchitectural adaptation using machine learning ACM Transactions on Architecture and Code Optimization 2013 16 unsupported no abstract available for this DOI
Machine Learning Guided Discovery of Non‐Linear Optical Materials Advanced Theory and Simulations 2024 14 assisted The ML models serve as a screening component that proposes candidates, which are then confirmed by human-run DFT calculations, making this a prediction-inside-a-pipeline rather than an AI-produced discovery. “These findings are validated using density functional theory(DFT) calculations.”
Using principal component analysis for neural network high-dimensional poten... The Journal of Chemical Physics 2020 14 unsupported no abstract available for this DOI
Improving Credit Risk Assessment through Deep Learning-based Consumer Loan D... International Journal of Finance & Banking Studies (2147-4486) 2023 14 assisted The deep learning model only produces default-likelihood predictions that inform banks' human-run credit review workflow, and the abstract merely reports test-set accuracy on an existing prediction task rather than the AI performing credit risk assessment end to end. “The model was evaluated on a test set of 250 records and achieved a test set accuracy of 95.2%, correctly predicting the default state of 238 out of 2...”
Phase Transformation Temperature Prediction in Steels via Machine Learning Materials 2024 13 assisted The LightGBM model is used as a tool within a human-driven study of influencing factors and mechanisms (feature engineering, SHAP/PDP interpretation, discussion of alloying effects), rather than the AI itself producing the paper's primary result end to end. “an improved version of the gradient-boosting method LightGBM has been utilized to study the influencing factors of the four phase transformation tempe...”
SymbolNet: neural symbolic regression with adaptive dynamic pruning for comp... Machine Learning Science and Technology 2025 13 assisted The paper's contribution is a human-designed method/framework (SymbolNet with adaptive sparsity regularization) whose evidence is effectiveness/accuracy demonstrations on existing benchmark tasks, rather than an AI-produced result that itself constitutes the contribution. “we demonstrate the effectiveness of our model on the LHC jet tagging task (16 inputs), MNIST (784 inputs), and SVHN (3072 inputs)”
Manifold Learning for Aerodynamic Shape Design Optimization Aerospace 2025 11 assisted The manifold-learning reduced-order model and autoencoder serve as fast surrogate components inside a human-developed genetic-algorithm optimization pipeline whose results are validated against full-order CFD, so the AI accelerates predictions within the workflow rather than itself performing the design optimization end to end. “the optimization outcomes aligned well with those obtained from the FOM-based framework, further validating the effectiveness and practical applicabil...”
Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenk... Applied Sciences 2023 11 assisted The paper is a human-driven methodological comparison and tuning study of ML classifiers on simulated IACT data, where the models are evaluated components for the gamma/hadron separation pipeline rather than the AI itself producing the scientific result. “we have assessed and compared a number of supervised Machine Learning methods, including the Random Forest method, Extra Trees method, and Extreme Gra...”
Deep learning–based approaches for weed detection in crops Frontiers in Plant Science 2026 11 not a result The abstract explicitly describes a review synthesizing existing deep learning weed-detection approaches, with no new primary result produced by an AI system. “This review provides a comprehensive synthesis of recent progress in deep learning-based weed detection”
Assessment of Support Vector Machine performance for default prediction and... Banks and Bank Systems 2022 11 assisted This is a human-run comparative assessment of an off-the-shelf technique's accuracy on an existing task (default prediction), where the SVM's predictions served to inform the authors' methodological comparison and tool design rather than constituting an AI-produced result that is itself the paper's contribution. “The results of this study showed that the model based on the Support Vector Machine approach with the Radial Basis Function kernel, performs better in...”
A bilingual benchmark for evaluating large language models PeerJ Computer Science 2024 11 not a result The paper's contribution is a new benchmark and evaluation dataset for assessing existing LLMs (ChatGPT, GPT-4, fastText) rather than an AI system producing a primary result; the models are the objects of evaluation, not agents solving a problem. “This work introduces a new benchmark for the bilingual evaluation of large language models (LLMs) in English and Arabic.”
Optimizing Crop Yield Prediction: Data-Driven Analysis and Machine Learning... Current Agriculture Research Journal 2024 10 assisted The paper's contribution is a human-run comparative evaluation of standard ML algorithms' accuracies on an existing crop-yield prediction task, not an AI system end-to-end solving the problem or producing the primary result itself. “We thoroughly assessed and contrasted the performance of a variety of machine learning algorithms, including Bagging Regressor, KNN, Decision Trees, G...”
Faculty Opinions recommendation of Deep learning enables rapid identificatio... Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature 2019 not a result the record is "faculty opinions recommendation", so it is a note about another work rather than a primary result: there is nothing here for an AI to have solved “We used GENTRL to discover potent inhibitors of discoidin domain receptor 1 (DDR1)... in 21 days. Four compounds were active in biochemical assays, an...”
Multimodal deep learning for biomedical data fusion: a review Briefings in Bioinformatics 2021 750 not a result This is a survey/review proposing a taxonomy of existing methods rather than reporting a primary result produced by an AI system. “we review the current state-of-the-art of such methods and propose a detailed taxonomy that facilitates more informed choices of fusion strategies for...”
Can Large Language Models Transform Computational Social Science? Computational Linguistics 2023 559 not a result The paper's contribution is a road map, prompting best practices, and a benchmark evaluation re-running 13 LLMs on 25 existing CSS benchmarks, not an AI system solving a primary research problem. “This work provides a road map for using LLMs as CSS tools. Towards this end, we contribute a set of prompting best practices and an extensive evaluati...”
Diagnosis of Pneumonia from Chest X-Ray Images Using Deep Learning venue not stated 2019 410 assisted The paper reports accuracy of two existing pre-trained CNNs on a chest X-ray dataset, explicitly framed as a computer-aided system to guide clinicians rather than an AI autonomously performing diagnosis. “computer-aided diagnosis systems are needed to guide the clinicians”
Identifying Exoplanets with Deep Learning: A Five-planet Resonant Chain arou... The Astronomical Journal 2018 368 assisted The neural network's role was to rank and classify candidate signals that the authors had already identified via their own search, and the authors themselves performed the statistical validation that confirmed the planets, making the AI a classification component in a human-run discovery pipeline rather than the end-to-end discoverer. “We statistically validate two new planets that are identified with high confidence by our model.”
Deep learning-based survival prediction of oral cancer patients Scientific Reports 2019 320 assisted The paper applies and validates an existing prediction algorithm's accuracy within a human-run retrospective clinical study, with the model's output intended to inform clinicians' treatment decisions rather than constituting an end-to-end AI-produced result. “deep learning-based survival prediction may improve prediction accuracy and guide clinicians both in choosing treatment options for better survival an...”
Multi-categorical deep learning neural network to classify retinal images: A... PLoS ONE 2017 282 assisted This is a pilot evaluation reporting a model's accuracy on an existing classification task (STARE fundus images), where performance was poor (30.5% accuracy for 10 classes) and the authors themselves conclude the techniques were ineffective, so the AI did not solve retinal disease detection but only produced evaluation results within a research workflow. “due to the small size of datasets, the deep learning techniques in this study were ineffective to be applied in clinics”
A Framework for Effective Application of Machine Learning to Microbiome-Base... mBio 2020 265 not a result The paper's contribution is a reusable pipeline and methodological recommendations for how researchers should apply ML to microbiome data, with standard off-the-shelf models benchmarked on an existing classification task as a demonstration rather than an AI-solved problem. “This work represents a step toward developing more-reproducible ML practices in applying ML to microbiome research. We implement a rigorous pipeline a...”
Automated robot‐assisted surgical skill evaluation: Predictive analytics app... International Journal of Medical Robotics and Computer Assisted Surgery 2017 188 unsupported no abstract available for this DOI
Object Detection and Image Segmentation with Deep Learning on Earth Observat... Remote Sensing 2020 177 not a result This is a literature review surveying 429 existing studies rather than presenting a primary research result produced by an AI system. “In this review, we provide a comprehensive overview of the impact of deep learning on EO applications by reviewing 429 studies on image segmentation a...”
AutoGrow4: an open-source genetic algorithm for de novo drug design and lead... Journal of Cheminformatics 2020 171 assisted AutoGrow4 is a software tool for semi-automated, computer-AIDED drug discovery whose demonstrated output is only predicted candidate ligands (docking-scored, unvalidated) that feed into a human-run drug-development pipeline rather than an end-to-end solved drug design, and the paper's primary contribution is the toolkit itself. “We here present AutoGrow4, an open-source program for semi-automated computer-aided drug discovery.”
Machine learning explainability in nasopharyngeal cancer survival using LIME... Scientific Reports 2023 160 assisted The ML model is a prognostic decision-support tool whose survival predictions, validated on retrospective registry data and performing comparably to an existing XGBoost baseline, are framed as informing human treatment planning and clinical decisions rather than the AI itself producing a novel solved result. “ML may aid in planning individualized therapy for this patient population.”
Classification of Paediatric Inflammatory Bowel Disease using Machine Learni... Scientific Reports 2017 157 assisted The ML models serve as a diagnostic aid within a clinical workflow that physicians still complete, with the paper explicitly framing the modelling as supporting rather than performing diagnosis, and positioning itself as a 'blueprint' for ML use with clinical data. “This study employs mathematical modelling of endoscopic and histological data to aid diagnostic accuracy.”
Comprehensive evaluation of deep learning architectures for prediction of DN... Bioinformatics 2019 153 not a result This is a benchmark/comparison study that re-evaluates existing deep learning architectures on an existing prediction task and produces guidelines and a toolkit, rather than an AI system solving a new problem. “its fully automatic model selection procedure allows us to perform a fair and unbiased comparison of deep learning architectures... Our work provides...”
Permutation invariant polynomial neural network approach to fitting potentia... The Journal of Chemical Physics 2014 151 unsupported no abstract available for this DOI
An evaluation of platforms for processing camera‐trap data using artificial... Methods in Ecology and Evolution 2022 133 not a result This is a review and comparative evaluation of existing AI platforms plus a guidance GitBook, not a paper whose primary result is an AI system solving a task. “By reviewing features of popular AI-powered platforms and sharing an open-source GitBook that illustrates how to manage AI output to evaluate model pe...”
Machine Learning of Designed Translational Control Allows Predictive Pathway... ACS Synthetic Biology 2018 118 assisted The ML models predict optimal RBS combinations from screened subsets, but the actual result, boosted production titers, was achieved through a human-run wet-lab pipeline of library screening, fermentation, and scale-up rescreening, making the ML a predictive component rather than the agent that produced the outcome. “our approach was able to boost production titers by over 60% when screening under 3% of a library”