Md. Robiul Islam Niloy | Search arXiv

arXiv2026

Black-Box Optimization of Mixed Binary-Continuous Variables: Challenges and Opportunities in Evolutionary Model Merging

Model merging has emerged as a cost-effective paradigm for combining capabilities of multiple large language models (LLMs) without additional training. Among existing approaches, Data Flow Space (DFS) merging operates by selectively activating parameter groups across model layers, offering greater flexibility than naive parameter averaging. However, the optimization challenges underlying DFS merging remain poorly understood. In this paper, we formally characterize the DFS merging problem as a mixed binary-continuous black-box optimization problem, identifying three fundamental challenges: (1) mixed binary-continuous variable types, (2) high-dimensional search spaces with up to 291 parameter groups, and (3) conditional variable dependencies corresponding to Type-I interaction as characterized by Akimoto et al. [1]. We empirically evaluate seven merging methods on Mistral-7B models across official GSM8K (1,319 questions) and MMLU (14,042 questions, 57 subjects) benchmarks. Our structured approach achieves a 49.5% reduction in effective search space. Results reveal that all tested merging methods fail to surpass the stronger source model on official benchmarks, with TIES (13.72%) and DELLA (4.12%) performing catastrophically on GSM8K. Notably, DELLA scores below even the base model (6.67%), demonstrating that standard parameter space methods fail when merging base and instruction-tuned models. These findings directly validate the difficulty of the Type-I interaction structure we identify, and motivate the application of algorithms specifically designed for this challenge such as the concurrent IGBD work of Ong et al. [2].

cs.NE↗

arXiv2026

Autonomous OSS Threat Detection via Taxonomy-Aligned LLMs

Open-source software (OSS) ecosystems face escalating supply chain threats, including typosquatting, dependency confusion, Trojan Source obfuscation, malicious build injection, and CI/CD pipeline poisoning. Sonatype documented over 1.2 million cumulative malicious packages across major registries, while signature-based and rule-based detection approaches lack the semantic understanding needed to generalize across evolving attacks. This paper proposes a taxonomy-aligned large language model framework for automated detection and multi-class classification of OSS supply chain threats. We introduce an AV-xxx taxonomy covering five semantically distinct attack categories and curate 999 verified real-world incidents from GitHub Security Advisories, CISA alerts, government security databases, and commercial threat intelligence reports, spanning 2018 to 2026 across eight package ecosystems. Using taxonomy-aligned prompting with GPT-4, the framework achieves 97% accuracy and 97% macro F1 across all five categories. Evaluation against eight methods five traditional machine learning baselines, one zero-shot open-source LLM, and two fine-tuned neural models reveals that fine-tuned Llama 3.1 8B (70.5%) and SecRoBERTa (77.5%) underperform a simple TF-IDF classifier (82.3%), while Mistral 7B without taxonomy alignment achieves 65.7%. These findings highlight taxonomy-aligned prompting over model scale, domain pretraining, or fine-tuning as the critical performance factor. Cross-ecosystem analysis shows varying classification difficulty, with npm achieving 65.5% baseline accuracy versus 100% in smaller ecosystems. GitHub Actions exhibits a substantial accuracy F1 divergence (82.4% accuracy, 0.367 macro F1), indicating systematic class imbalance and highlighting ecosystems where LLM-based contextual reasoning offers the greatest benefit. Our dataset, scripts, and code are publicly available in Github.

cs.CR↗