Theory gaps in Computer Science

Representative open questions

Showing 30 of 128 — one per source paper, highest-quality first.

• Causal K-means clustering (2026) · doi

  The analysis assumes bounded support (∥μ∥∞ ≤ B < ∞ a.s.) for the high-probability bounds in Theorem 3.1, but real causal data with heavy-tailed covariate distributions may violate this; the paper does not extend results to unbounded or heavy-tailed settings.

• AN ITERATIVE GLMM–XGBOOST ALGORITHM WITH GROUP-AWARE CONDITIONAL PERMUTATION IMPORTANCE FOR EXPLAINING MULTILEVEL ITEM RESPONSE DATA (2026) · doi

  The paper introduces group-aware conditional permutation importance for explaining GLMM–XGBoost predictions but does not provide guidance on interpreting interaction effects identified through this approach when predictors operate at different hierarchical levels (person-, cluster-, item-level), particularly in cross-level interactions.

• Securing IoT Devices with PUFs: Mitigating Aging and Tampering through Cryptography and Machine Learning (2026) · doi

  The serial test frequency statistics νi1…im are noted as non-independent, yet the paper does not provide guidance on correcting bias or variance inflation that may occur when applying this test to PUF output sequences in aging or tampered IoT device scenarios.

• Blockchain-integrated machine learning framework for transparent smart contract vulnerability detection (2026) · doi

  The SHAP-based optimization framework improved CatBoost's minority class detection (arithmetic and denial_of_service vulnerabilities) but the mechanism by which SHAP restructures feature importances to reduce off-diagonal errors in imbalanced smart contract datasets remains theoretically unexplained. Future work should investigate whether this improvement generalizes to other vulnerability types or datasets with different imbalance ratios.

• Inferring High-Dimensional Dynamic Networks Changing with Multiple Covariates (2026) · doi

  The Hamming distance analysis reveals that radiation doses of 0 Gy and 0.05 Gy produce nearly identical network structures with only marginal differences attributed to random variation, but no threshold or statistical test is established to distinguish true dose-dependent network changes from stochastic fluctuations in CVN estimation.

• Predicting reading outcomes in school starters using a computer assisted dynamic assessment of decoding (2026) · doi

  The study demonstrates that dynamic assessment components identify whether poorer letter-sound learning is due to learning difficulty versus limited learning opportunity in CALD children, but the mechanisms underlying this differentiation in bilingual populations specifically require further investigation with larger EAL samples.

• Large language model based machine translation for universal multilingual understanding and translation quality enhancement (2026) · doi

  The comparative analysis shows GPT-4 outperforms other models consistently across language pairs (En-De, En-Cs, En-Zh, En-Ru, De-En, Cs-En, Zh-En, Ru-En), but does not investigate the specific linguistic phenomena or grammatical structures where GPT-4 advantages emerge, particularly for morphologically complex languages like Russian and Czech.

• Quantum Information Framework for Neural Network Generalization: A Comprehensive Experimental Analysis (2026) · doi

  The framework evaluates generalization on test sets but does not investigate the relationship between quantum information metrics (von Neumann entropy, effective rank) and overfitting detection, or whether these metrics can predict generalization gaps before evaluation on held-out test data.

• Comparative analysis of deep learning algorithms for rolling element bearing fault classification under variable loads and speeds (2026) · doi

  Vision Transformer (ViT) demonstrated unpredictable and non-monotonic performance degradation across noise levels (0.830 baseline, 0.690 at 5 dB, 0.744 at 3 dB, 0.733 at 1 dB SNR) compared to CNN-based architectures, with significant accuracy fluctuation at intermediate SNR conditions. Research is needed to identify why transformer-based architectures exhibit this erratic behavior in rolling element bearing fault detection under noisy conditions and how to stabilize their performance.

• LLM-Powered Silent Bug Fuzzing in Deep Learning Libraries via Versatile and Controlled Bug Transfer (2026) · doi

  The failure case analysis reveals that TransFuzz cannot distinguish compiler-level optimization artifacts from genuine functional bugs in gradient computation, as demonstrated by the torch.compile flex_attention case where numerical differences from compiler optimizations were misclassified. Developing domain-specific oracles that incorporate expert-level reasoning about compiler internals and floating-point precision behavior is needed for silent bug fuzzing in compiled deep learning operations.

• Quantum-SpinalNet: a hybrid deep learning approach for mammographic breast cancer detection (2026) · doi

  The paper demonstrates that integrating biologically-inspired SpinalNet with quantum-inspired DQNN achieved synergistic performance improvements (93.8% accuracy, Dice = 0.89), but does not experimentally isolate whether these gains derive from reduced overfitting via layer-wise modular processing or from improved feature abstraction through quantum probabilistic reasoning.

• Ocean: Object-aware Anchor-free Tracking with Matching-relation Learning (2026) · doi

  Online update in anchor-free tracking introduces noise in certain scenes, particularly on datasets without restart capability (GOT-10K and LaSOT). The mechanisms by which online appearance modeling increases noise and whether this is inherent to the matching-relation learning approach or can be mitigated through alternative update strategies remain uninvestigated.

• Automated design of heuristics for resource-constrained project scheduling problem via regression algorithms (2026) · doi

  The regression-based heuristics demonstrated superior generalization from small to large project instances, but the mechanisms enabling this cross-scale knowledge transfer in resource-constrained project scheduling remain unexplored. Future work should investigate which regression algorithm features (feature engineering, model architecture, training data characteristics) are responsible for the generalization advantage over genetic programming approaches across Multi-projects, Large-projects/P, and Large-projects/SP datasets.

• From unstructured text to structured reasoning: a hybrid knowledge graph for Indonesian sentencing analysis (2026) · doi

  While the paper demonstrates that objective entities (F1 > 90%) correlate with standardized formats and interpretive entities (F1 < 80%) with legal reasoning variation, it does not propose or validate methods to explicitly model this epistemological distinction in the hybrid knowledge graph structure for improved entity disambiguation.

• On the interface between linguistics, computer science and psychiatry: analyzing textual key-factors affecting BERT-based classification of schizophrenia in social media texts (2026) · doi

  Topicality effects on semantic coherence salience in schizophrenia detection remain theoretical; the paper cannot yet determine whether BERT captures deeper grammatical or coherence disruption patterns related to topic versus simply capturing differences in linguistic information density and discourse register across genres. Cross-topic coherence analysis with controlled semantic and syntactic complexity is required.

• Predicting Employee Attrition: A Machine Learning Approach in Human Resource Analytics (2026) · doi

  The feature importance analysis reveals divergent rankings between Gradient Boosting and Random Forest models for secondary attrition predictors, with Gradient Boosting prioritizing workload variables (Overtime, Stock Option Level) while Random Forest emphasizes demographic factors (Distance from Home, Years at Company). The paper does not investigate whether these differences stem from model architecture bias or represent genuine contextual variations in attrition mechanisms across employee subgroups.

• Hepatitis C Diagnosis using Supervised Machine Learning Algorithms and Ensemble Learning Techniques (2026) · doi

  The study demonstrates that SVM achieves perfect recall (100%) but 0% specificity and ROC-AUC of 0.50 in hepatitis C diagnosis, indicating poor differentiation of healthy cases. The underlying mechanisms causing this severe specificity collapse in SVM compared to ensemble methods like AdaBoost and Random Forest require investigation through kernel selection analysis and class boundary examination.

• Understanding the Dynamics of Trust and Engagement in E-Commerce Recommender Systems: Trends and Influences (2026) · doi

  Current theoretical frameworks for trust and engagement in e-commerce recommender systems are predominantly based on research from China and Western markets, with African informal and community-driven recommendation networks critically underrepresented. Future work must empirically investigate how trust conceptualizations differ across African, South Asian, and Latin American e-commerce contexts to validate whether existing engagement models capture culturally diverse user interactions with recommender systems.

• Deep Learning Based Fish Species and Freshness Detection Using Convolutional Neural Networks (2026) · doi

  The three freshness categories (Fresh, Medium, Spoiled) used for CNN classification lack correlation with objective freshness metrics such as bacterial load, pH levels, or volatile organic compound concentrations; validation against biochemical freshness indicators is absent.

• Unified URL and QR Based Phishing Detection Framework (2026) · doi

  While ethical considerations address data protection and transparency, the paper does not evaluate adversarial robustness: how the model performs against evasion attacks (e.g., homograph attacks, obfuscated URLs, dynamically generated QR codes with steganographic encoding).

• A Comprehensive Review of Machine Learning Techniques for Student Academic Performance Prediction (2026) · doi

  Causal inference methods for determining which interventions are most effective for specific student subgroups in academic performance prediction have not been systematically applied or compared. Research is needed to establish causal models that move beyond correlation-based predictive approaches.

• ON DOMINATION IN COARSE DEG-CENTRIC GRAPHS (2026) · doi

  The domination number formulas for coarse deg-centric graphs are established only for specific graph families (double wheel, gear, web, flower, sunlet, blossom, and sunflower graphs) with n ≥ 3. The behavior of domination in coarse deg-centric graphs for other graph classes such as Petersen-type graphs, generalized Kneser graphs, or arbitrary regular graphs remains uncharacterized.

• An Approximate Decomposition of a Multivariate Polynomial and Its Application (2026) · doi

  Algorithm 2 computes approximate decompositions for indecomposable multivariate polynomials using Hamming distance, but the authors explicitly state that 'when it is indecomposable, we cannot determine the extent of the difference between the true solution and the output.' This requires developing theoretical bounds or optimality analysis to quantify how far the approximate decomposition is from an optimal solution.

• AI-Powered Resume Screening and Ranking System (2026) · doi

  The paper does not address how the AI-Powered Resume Screening System handles implicit or non-obvious qualifications, transferable skills, or alternative certifications that may not match exact keywords in the job description. The keyword matching technique may systematically disadvantage candidates with unconventional career trajectories or those who use domain-specific terminology different from standard job requirement vocabulary.

• A Systematic Review of Machine Learning Approaches For AI-Driven Fraud Detection in Loyalty Programs (2026) · doi

  No quantitative framework exists for choosing between interpretable tabular models and graph-based approaches based on program size and fraud taxonomy breadth, preventing loyalty operations with intermediate-scale datasets from objectively deciding when graph learning complexity is justified versus when simpler tabular models suffice.

• Optimizing avian flight dynamics with a synergetic bio-inspired and machine learning approach (2026) · doi

  The one-dimensional scalar motion analysis in Equations 15-20 assumes thrust force as the primary contributor to acceleration while treating wing motion as unidirectional. The framework does not account for unsteady inflow conditions or three-dimensional aerodynamic interactions that occur during natural avian flight, limiting the biofidelity of the bio-inspired model.

• A deep learning approach to multi-marginal optimal transport via Hilbert space embeddings of probability measures (2026) · doi

  Theorem 1 guarantees asymptotic satisfaction of marginal constraints via MMD penalty and weak convergence of transport maps, but the convergence of the transport cost value itself is not addressed and remains an open direction for further analysis in the multi-marginal optimal transport framework.

• LISArD: learning image similarity to defend against gray-box adversarial attacks (2026) · doi

  While the paper identifies that LISArD struggles with objects blended into backgrounds (Figure 7), the specific visual features or embedding space properties that cause these misclassifications on CIFAR-10 and CIFAR-100 are not analyzed. A detailed investigation of the learned image similarity embeddings for these failure cases could reveal whether the issue stems from insufficient discriminative power in the similarity learning component.

• Utilizing Cascade Deep Metric Learning for the Kellgren-Lawrence Grading of Knee Osteoarthritis Classification from X-Ray Images (2026) · doi

  The paper demonstrates that CDML with VGG19 outperforms deeper architectures (ResNet50, EfficientNetB0), but does not investigate whether this advantage generalizes to other shallow/medium-depth backbones or explain the mechanistic reasons why the cascade ordinal-aware learning design interacts differently with network depth in knee OA classification.

• An Acoustic Feature-Based Ensemble Learning Approach for Chicken Health Detection (2026) · doi

  The paper demonstrates that RF with MFCC features minimizes false negatives while maintaining low false positives, but the mechanism by which different acoustic feature representations (MFCC vs. alternative spectral or temporal features) interact with specific classifier architectures (SVM, LR, RF) to produce these distinct error patterns is not theoretically analyzed or explained.