# The 2025 Foundation Model Transparency Index arXiv: https://arxiv.org/abs/2512.10169 ยท HTML: https://mlsanity.com/paper/2512.10169/ ## Key facts - Cited by 11 papers in the map (data through 14 Oct 2026). - Builds on 48 of its 98 references that are in the map. - First posted on arXiv 11 Dec 2025. ## Builds on (most cited first, 30 of 48) - [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020) - [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022) - [Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!](https://mlsanity.com/paper/2310.03693/) (2023) - [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018) - [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021) - [Green AI](https://mlsanity.com/paper/1907.10597/) (2019) - [OLMo: Accelerating the Science of Language Models](https://mlsanity.com/paper/2402.00838/) (2024) - [Carbon Emissions and Large Neural Network Training](https://mlsanity.com/paper/2104.10350/) (2021) - [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019) - [Release Strategies and the Social Impacts of Language Models](https://mlsanity.com/paper/1908.09203/) (2019) - [Scaling Data-Constrained Language Models](https://mlsanity.com/paper/2305.16264/) (2023) - [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://mlsanity.com/paper/2402.07827/) (2024) - [How is ChatGPT's behavior changing over time?](https://mlsanity.com/paper/2307.09009/) (2023) - [What Does it Mean for a Language Model to Preserve Privacy?](https://mlsanity.com/paper/2202.05520/) (2022) - [A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity](https://mlsanity.com/paper/2305.13169/) (2023) - [Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims](https://mlsanity.com/paper/2004.07213/) (2020) - [Sociotechnical Safety Evaluation of Generative AI Systems](https://mlsanity.com/paper/2310.11986/) (2023) - [Troubling Trends in Machine Learning Scholarship](https://mlsanity.com/paper/1807.03341/) (2018) - [A survey of agent interoperability protocols: Model Context Protocol (MCP), Agent Communication Protocol (ACP), Agent-to-Agent Protocol (A2A), and Agent Network Protocol (ANP)](https://mlsanity.com/paper/2505.02279/) (2025) - [Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining](https://mlsanity.com/paper/2212.06470/) (2022) - [Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey](https://mlsanity.com/paper/2210.07700/) (2022) - [The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI](https://mlsanity.com/paper/2310.16787/) (2023) - [The Foundation Model Transparency Index](https://mlsanity.com/paper/2310.12941/) (2023) - [The Gradient of Generative AI Release: Methods and Considerations](https://mlsanity.com/paper/2302.04844/) (2023) - [Interactive Model Cards: A Human-Centered Approach to Model Documentation](https://mlsanity.com/paper/2205.02894/) (2022) - [A Safe Harbor for AI Evaluation and Red Teaming](https://mlsanity.com/paper/2403.04893/) (2024) - [Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning](https://mlsanity.com/paper/2302.08476/) (2023) - [Structured access: an emerging paradigm for safe AI deployment](https://mlsanity.com/paper/2201.05159/) (2022) - [Understanding accountability in algorithmic supply chains](https://mlsanity.com/paper/2304.14749/) (2023) - [Ecosystem Graphs: The Social Footprint of Foundation Models](https://mlsanity.com/paper/2303.15772/) (2023) ## Built on by (most cited first, 11 of 11) - [The 2025 AI Agent Index: Documenting Technical and Safety Features of Deployed Agentic AI Systems](https://mlsanity.com/paper/2602.17753/) (2026) - [Autonomous Research Agents: A Survey of AI Scientists and the Verification Gap](https://mlsanity.com/paper/2608.05179/) (2026) - [The ATOM Report: Measuring the Open Language Model Ecosystem](https://mlsanity.com/paper/2604.07190/) (2026) - [M"OVE: A Holistic LLM Benchmark for the German Public Sector](https://mlsanity.com/paper/2606.13111/) (2026) - [Selecting Language Models for Social Science: Start Small, Start Open, and Validate](https://mlsanity.com/paper/2601.10926/) (2026) - [NeurIPS Should Require Reproducibility Standards for Frontier AI Safety Claims](https://mlsanity.com/paper/2605.08192/) (2026) - [Short-Term Gain, Long-Term Fragility: AI Labor Substitution and the Erosion of Sustainable Capability](https://mlsanity.com/paper/2605.27399/) (2026) - [Neuro-Symbolic Software Verification: Hyper-charging Local Language Models with Symbolic Reasoning at Scale](https://mlsanity.com/paper/2606.16886/) (2026) - [EvalSafetyGap: A Hybrid Survey and Conceptual Framework for LLM Evaluation-Safety Failures](https://mlsanity.com/paper/2606.30219/) (2026) - [Silent Revision: Measuring Undisclosed Change in the Safety Frameworks of Frontier AI Developers](https://mlsanity.com/paper/2609.08789/) (2026) - [Jailbreaks for Black-Box Uncertainty Quantification in Large Reasoning Models](https://mlsanity.com/paper/2609.35350/) (2026) ## Abstract (arXiv) Foundation model developers are among the world's most important companies. As these companies become increasingly consequential, how do their transparency practices evolve? The 2025 Foundation Model Transparency Index is the third edition of an annual effort to characterize and quantify the transparency of foundation model developers. The 2025 FMTI introduces new indicators related to data acquisition, usage data, and monitoring and evaluates companies like Alibaba, DeepSeek, and xAI for the first time. The 2024 FMTI reported that transparency was improving, but the 2025 FMTI finds this progress has deteriorated: the average score out of 100 fell from 58 in 2024 to 40 in 2025. Companies are most opaque about their training data and training compute as well as the post-deployment usage and impact of their flagship models. In spite of this general trend, IBM stands out as a positive outlier, scoring 95, in contrast to the lowest scorers, xAI and Midjourney, at just 14. The five members of the Frontier Model Forum we score end up in the middle of the Index: we posit that these companies avoid reputational harms from low scores but lack incentives to be transparency leaders. As policymakers around the world increasingly mandate certain types of transparency, this work reveals the current state of transparency for foundation model developers, how it may change given newly enacted policy, and where more aggressive policy interventions are necessary to address critical information deficits.