# HugSelect: An Explainable Multi-Criteria Decision-Support Framework for foundation-model selection

arXiv: https://arxiv.org/abs/2608.08069 · HTML: https://mlsanity.com/paper/2608.08069/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 51 references that are in the map.
- First posted on arXiv 8 Aug 2026.

## Builds on (most cited first, 9 of 9)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Large Language Models: A Survey](https://mlsanity.com/paper/2402.06196/) (2024)
- [Foundational Models in Medical Imaging: A Comprehensive Survey and Future Vision](https://mlsanity.com/paper/2310.18689/) (2023)
- [Automated categorization of pre-trained models for software engineering: A case study with a Hugging Face dataset](https://mlsanity.com/paper/2405.13185/) (2024)
- [Benchmarking Recommendation, Classification, and Tracing Based on Hugging Face Knowledge Graph](https://mlsanity.com/paper/2505.17507/) (2025)
- [Empirical Evaluation of AI-Assisted Software Package Selection: A Knowledge Graph Approach](https://mlsanity.com/paper/2508.05693/) (2025)
- [Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks](https://mlsanity.com/paper/2412.20682/) (2024)
- [Evidence-Driven Decision Support for AI Model Selection in Research Software Engineering](https://mlsanity.com/paper/2512.11984/) (2025)

## Built on by (most cited first, 1 of 1)

- [When Is Smaller Greener? Right-Sizing Language Models for Agentic AI](https://mlsanity.com/paper/2610.19345/) (2026)

## Abstract (arXiv)

Foundation models are increasingly reused as software components, making model selection a critical software-engineering decision. Current model hubs primarily support discovery through popularity metrics, often neglecting functional capabilities, operational constraints, and community-perceived quality. We argue that foundation-model selection should be treated as an explicit, auditable software-component selection task rather than as keyword search, popularity ranking, or opaque conversational advice. This paper proposes HugSelect, an explainable decision-support framework for foundation-model selection. HugSelect builds a knowledge base of 71,274 models by combining repository metadata, extracted functional capabilities, and perceived quality attributes derived from community discussions into a unified pipeline. It ranks candidate models using a weighted additive model that exposes criterion-level score decompositions. We evaluated HugSelect through pipeline validation, comparative case studies against four commercial LLM-based recommendation systems (44 scenarios), fine-grained ablation, and an exploratory user study (n = 10). Extraction pipelines achieved an F1 score of 0.801 for functional features and an accuracy of 0.84 for quality-attribute mapping. HugSelect achieved a model-level Coverage@10 of 0.61 and family-level Coverage@10 of 0.91, showing recommendation quality comparable to that of the evaluated commercial systems, with no significant overall differences in ranking quality, while providing stable, traceable, and inspectable reasoning. Ablation confirmed that functional features were the main driver of retrieval accuracy, and preliminary user feedback suggests that the framework is useful and intuitive.