arXiv subjects

Woohyeuk Lee

Publications and source records attributed to Woohyeuk Lee.

3 recordsLinked to original sources

arXiv2026

Open at the Edge, Captured at the Center: llama.cpp and the Political Economy of Local AI Inference

Critical scholarship on open AI has focused on model releases and cloud ecosystems, leaving the local inference infrastructure that makes open-weight models runnable on user-owned devices largely unexamined. We address this gap through a mixed-methods analysis of llama$.$cpp, combining 7,681 merged pull requests from March 2023 through March 2026 with repository discussions, corporate statements, and contributor blogs. We show that local inference broadens participation at execution while relocating capture into the infrastructure that makes execution possible. Through hardware backends, model integration labor, and Hugging Face's February 2026 absorption of the project, we document how control shifts to hardware vendors, model distributors, and core maintainers while model owners and individual contributors bear the cost of making models runnable. These dynamics suggest that preserving openness outside the cloud requires attention to the infrastructure that makes models runnable, not just to the models themselves. This calls for policy mechanisms--analysis of format dependencies and vendor influence, model compatibility requirements, and sustained public funding for inference tooling--that extend beyond model release conditions to the infrastructure layer.

cs.CY↗

arXiv2026

Open AI in the Wild: Adoption and Adaptation of Open Models on r/LocalLLaMA

Existing work on AI openness has focused on defining what technical components or release practices qualify a system as "open". However, less is known about how openness is understood and put into practice by people who adopt and adapt these models under real-world constraints. In this paper, we present an empirical study of r/LocalLLaMA, a large online community centered on running and customizing open foundation models locally. Through thematic analysis of community discussions, we find that members conceptualize openness pragmatically - in relation to reliability, local control, privacy, and the ability to adapt models under constraints such as compute resources, licensing, and usability. We identify key motivations for adopting open models, including autonomy, experimentation, and resistance to platform instability, as well as deterrents such as steep learning curves and performance gaps compared to closed systems. We further describe how shared resources and projects, including datasets, evaluation frameworks, and inference tools, sustain interdependent development in the broader open AI ecosystem beyond individual model releases. We then discuss the implications of a utility-oriented view of openness, and how producer support for downstream usability and infrastructure could better enable sustained innovation in open model ecosystems.

cs.HC↗

arXiv2026

Reimagining Open Source and Openness in AI: Co-Creating Responsible Technological Futures

Debates over open source and openness in artificial intelligence have intensified as policymakers, researchers, and practitioners grapple with how foundation models should be developed and governed to balance innovation, accountability, and public interest. However, there has been limited empirical work examining how diverse stakeholders collectively understand and negotiate responsible openness in AI, particularly through participatory processes that extend beyond industry-led definitions and frameworks. This paper presents findings from a multi-sectoral workshop grounded in futures thinking and participatory design methods. The workshop generated co-created visions of desirable futures and the role of AI, alongside a set of action pathways and a research roadmap focused on responsible open source and openness in AI. This paper makes three key contributions. First, it empirically documents the co-created visions, actions, and research priorities. Second, it identifies four core tensions that emerged as participants translated high-level aspirations into concrete actions, revealing conflicting interpretations of openness regarding its purpose (as an end or a means), its scope (expansion versus meaningful access), and its operation (mandatory versus conditional, sufficient versus dependent on governance and use). These tensions illustrate that responsible openness is not a singular technical solution, but a negotiated sociotechnical project shaped by values, positionalities, and priorities. Third, the paper advances methodological approaches in AI governance by demonstrating how participatory futures methods can surface plural visions, actions, and research priorities that extend beyond dominant, largely corporate, narratives, offering empirical insight into how openness, power, and accountability are negotiated in practice.

cs.CY↗