arXiv subjects

Mohammad Zare

Publications and source records attributed to Mohammad Zare.

8 recordsLinked to original sources

arXiv2026

Microskill Architecture: A Modular Skill-Driven Framework for AI-Native Code Generation

Large language models and AI coding agents have reshaped software development, but the path to fully AI-native systems faces structural challenges. Chief among them is managing context windows without losing accuracy or efficiency. When developers inject full project documentation and code into a model's memory, the model loses mid-sequence information, token costs spiral, and architecture drifts. This paper presents MicroSkill Architecture: a modular design paradigm inspired by microservices, applied to knowledge encapsulation instead of service decomposition. Instead of feeding an agent the entire codebase, the architecture partitions knowledge into atomic, sharply scoped skill capsules, and a dynamic router selects only semantically relevant capsules for the task. We formally model context allocation as constrained optimization over semantic relevance subject to a token budget. An empirical case study an enterprise content management system with fifteen complex features shows that MicroSkill cuts token consumption by over 90%, nearly doubles first-try compilation success rates, eliminates architectural violations entirely, and enables autonomous extraction and registration of seven new skill capsules via a self-learning mechanism. These findings suggest MicroSkill Architecture offers a scalable foundation for building AI-native development systems that are more efficient, more reliable, and capable of evolving over time.

cs.SE↗

arXiv2026

Examining the Challenges of Intellectual Property in AI-Generated Productions

With the advancement of artificial intelligence systems capable of autonomously generating artistic, literary, musical works, and even inventions without direct human intervention, the intellectual property (IP) regime faces unprecedented questions and challenges. The most critical issue concerns the ownership of moral and economic rights in the absence of a human creator, and how such outputs can be granted legal protection. This paper first reviews the theoretical foundations and existing literature in this domain, then comparatively examines Iranian legal frameworks such as the 1969 Law for the Protection of Authors, Composers, and Artists Rights and the Patent and Trademark Registration Law-alongside other legal systems, including the European Union, the United Kingdom, and the United States. Furthermore, existing legal perspectives on the intellectual property of AI-generated works and the related enforcement challenges are analyzed. The findings reveal significant regulatory gaps within the current Iranian legal framework. To balance the promotion of innovation with the preservation of human creativity, revising existing laws and introducing novel approaches such as defining a specific intellectual property right for AI-generated works or designating ownership among associated human agents appears to be essential.

cs.CY↗

arXiv2026

QQJ: Quantifying Qualitative Judgment for Scalable and Human-Aligned Evaluation of Generative AI

The rapid progress of generative artificial intelligence has exposed fundamental limitations in existing evaluation methodologies, particularly for open-ended, creative, and human-facing tasks. Traditional automatic metrics rely on surface-level statistical similarity and often fail to reflect human perceptions of quality, while purely human evaluation, although reliable, is costly, subjective, and difficult to scale. Recent approaches using large language models as evaluators offer improved scalability but frequently lack explicit grounding in human-defined evaluation principles, leading to bias and inconsistency. In this paper, we introduce Quantifying Qualitative Judgment (QQJ), a scalable and human-centric evaluation framework that explicitly bridges the gap between human judgment and automated assessment. QQJ separates the definition of quality from its execution by anchoring evaluation in expert-designed, multi-dimensional rubrics and calibrating large language model evaluators to align with expert reasoning using a small, high-quality annotation set. This design enables consistent, interpretable, and scalable evaluation across diverse generative tasks and modalities. Extensive experiments on text and image generation demonstrate that QQJ achieves substantially stronger alignment with human judgment than traditional automatic metrics and unconstrained LLM-based evaluators. Moreover, QQJ exhibits improved stability across repeated evaluations and superior diagnostic capability in identifying critical failure modes such as hallucination and intent mismatch. These results indicate that structured qualitative judgment can be operationalized at scale without sacrificing interpretability or human alignment, positioning QQJ as a practical foundation for reliable evaluation of modern generative AI systems.

cs.AI↗

arXiv2026

IPBC: An Interactive Projection-Based Framework for Human-in-the-Loop Semi-Supervised Clustering of High-Dimensional Data

High-dimensional datasets are increasingly common across scientific and industrial domains, yet they remain difficult to cluster effectively due to the diminishing usefulness of distance metrics and the tendency of clusters to collapse or overlap when projected into lower dimensions. Traditional dimensionality reduction techniques generate static 2D or 3D embeddings that provide limited interpretability and do not offer a mechanism to leverage the analyst's intuition during exploration. To address this gap, we propose Interactive Project-Based Clustering (IPBC), a framework that reframes clustering as an iterative human-guided visual analysis process. IPBC integrates a nonlinear projection module with a feedback loop that allows users to modify the embedding by adjusting viewing angles and supplying simple constraints such as must-link or cannot-link relationships. These constraints reshape the objective of the projection model, gradually pulling semantically related points closer together and pushing unrelated points further apart. As the projection becomes more structured and expressive through user interaction, a conventional clustering algorithm operating on the optimized 2D layout can more reliably identify distinct groups. An additional explainability component then maps each discovered cluster back to the original feature space, producing interpretable rules or feature rankings that highlight what distinguishes each cluster. Experiments on various benchmark datasets show that only a small number of interactive refinement steps can substantially improve cluster quality. Overall, IPBC turns clustering into a collaborative discovery process in which machine representation and human insight reinforce one another.

cs.LG↗

arXiv2026

Res-MIA: A Training-Free Resolution-Based Membership Inference Attack on Federated Learning Models

Membership inference attacks (MIAs) pose a serious threat to the privacy of machine learning models by allowing adversaries to determine whether a specific data sample was included in the training set. Although federated learning (FL) is widely regarded as a privacy-aware training paradigm due to its decentralized nature, recent evidence shows that the final global model can still leak sensitive membership information through black-box access. In this paper, we introduce Res-MIA, a novel training-free and black-box membership inference attack that exploits the sensitivity of deep models to high-frequency input details. Res-MIA progressively degrades the input resolution using controlled downsampling and restoration operations, and analyzes the resulting confidence decay in the model's predictions. Our key insight is that training samples exhibit a significantly steeper confidence decline under resolution erosion compared to non-member samples, revealing a robust membership signal. Res-MIA requires no shadow models, no auxiliary data, and only a limited number of forward queries to the target model. We evaluate the proposed attack on a federated ResNet-18 trained on CIFAR-10, where it consistently outperforms existing training-free baselines and achieves an AUC of up to 0.88 with minimal computational overhead. These findings highlight frequency-sensitive overfitting as an important and previously underexplored source of privacy leakage in federated learning, and emphasize the need for privacy-aware model designs that reduce reliance on fine-grained, non-robust input features.

cs.CR↗

arXiv2025

SEMO: A Socio-Evolutionary Adaptive Optimization Framework for Dynamic Social Network Tie Management

We propose a novel computational framework that models human social decision-making under uncertainty as an integrated Multi-Armed Bandit (MAB) and Markov Decision Process (MDP) optimization problem, in which agents adaptively balance the exploration of new social ties and the exploitation of existing relationships to maximize a socio-evolutionary fitness. The framework combines reinforcement learning, Bayesian belief updating, and agent-based simulation on a dynamic social graph, allowing each agent to use bandit-based Upper-Confidence-Bound (UCB) strategies for tie formation within an MDP of long-term social planning. We define a formal socio-evolutionary fitness function that captures both individual payoffs (e.g. shared information or support) and network-level benefits, and we derive update rules incorporating cognitive constraints and bounded rationality. Our Social-UCB algorithm, presented in full pseudocode, provably yields logarithmic regret and ensures stable exploitation via UCB-style bounds. In simulation experiments, Social-UCB consistently achieves higher cumulative social fitness and more efficient network connectivity than baseline heuristics. We include detailed descriptions of envisioned figures and tables (e.g. network evolution plots, model comparisons) to illustrate key phenomena. This integrated model bridges gaps in the literature by unifying exploration-exploitation dynamics, network evolution, and social learning, offering a rigorous new tool for studying adaptive human social behavior.

cs.SI↗

arXiv2025

Semimage: HSV-Based Semantic Image Encoding for Disentangled Text Representation

We propose SemImage, a novel method for representing a text document as a two-dimensional semantic image to be processed by convolutional neural networks (CNNs). In a SemImage, each word is represented as a pixel in a 2D image: rows correspond to sentences and an additional boundary row is inserted between sentences to mark semantic transitions. Each pixel is not a typical RGB value but a vector in a disentangled HSV color space, encoding different linguistic features: the Hue with two components H_cos and H_sin to account for circularity encodes the topic, Saturation encodes the sentiment, and Value encodes intensity or certainty. We enforce this disentanglement via a multi-task learning framework: a ColorMapper network maps each word embedding to the HSV space, and auxiliary supervision is applied to the Hue and Saturation channels to predict topic and sentiment labels, alongside the main task objective. The insertion of dynamically computed boundary rows between sentences yields sharp visual boundaries in the image when consecutive sentences are semantically dissimilar, effectively making paragraph breaks salient. We integrate SemImage with standard 2D CNNs (e.g., ResNet) for document classification. Experiments on multi-label datasets (with both topic and sentiment annotations) and single-label benchmarks demonstrate that SemImage can achieve competitive or better accuracy than strong text classification baselines (including BERT and hierarchical attention networks) while offering enhanced interpretability. An ablation study confirms the importance of the multi-channel HSV representation and the dynamic boundary rows. Finally, we present visualizations of SemImage that qualitatively reveal clear patterns corresponding to topic shifts and sentiment changes in the generated image, suggesting that our representation makes these linguistic features visible to both humans and machines.

cs.CV↗

arXiv2025

Attention-Guided Feature Fusion (AGFF) Model for Integrating Statistical and Semantic Features in News Text Classification

News text classification is a crucial task in natural language processing, essential for organizing and filtering the massive volume of digital content. Traditional methods typically rely on statistical features like term frequencies or TF-IDF values, which are effective at capturing word-level importance but often fail to reflect contextual meaning. In contrast, modern deep learning approaches utilize semantic features to understand word usage within context, yet they may overlook simple, high-impact statistical indicators. This paper introduces an Attention-Guided Feature Fusion (AGFF) model that combines statistical and semantic features in a unified framework. The model applies an attention-based mechanism to dynamically determine the relative importance of each feature type, enabling more informed classification decisions. Through evaluation on benchmark news datasets, the AGFF model demonstrates superior performance compared to both traditional statistical models and purely semantic deep learning models. The results confirm that strategic integration of diverse feature types can significantly enhance classification accuracy. Additionally, ablation studies validate the contribution of each component in the fusion process. The findings highlight the model's ability to balance and exploit the complementary strengths of statistical and semantic representations, making it a practical and effective solution for real-world news classification tasks.

cs.CL↗