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Global Regulators Agree on Crypto Framework: A Systems Theoretic and Automated Summarization Approach with #REFERENCES
Getting everyone on the same page when it comes to crypto regulation isn't easy. It’s a messy, global puzzle that needs regulators to sync up and share information—fast. If you want to understand how this happens, you have to look at two big challenges: first, how do all these separate regulatory bodies actually line up their policies, and second, how do we make sense of the flood of news and updates that come out of this process? In this paper, we roll out a hybrid approach. We use scale-free linear observer-based protocols to model how regulators “sync up” their state—basically, how they reach agreement, even with limits on what each one can do. At the same time, we use NLP techniques, fine-tuned with evolutionary methods on large language models, to boil down all those news stories into clear, useful summaries. Control theory helps explain how these regulators (who aren't exactly introspective or transparent) work together, even when they're working with limited resources. We also dig into ways to pull out the essential info from the mountains of complex, jargon-filled news. The big idea? This framework gives us a way to analyze how global standards come together, and how we can actually communicate what’s happening to people who need to know.
Introduction
Lately, global regulators have managed to settle on a big, sweeping framework for cryptocurrencies. That’s a major moment for international financial rules. But if you try to analyze how they got there, you run into a couple of real headaches. For starters, regulators act like a big network—they have to coordinate, even though each one deals with its own local problems and limited capacity (Liu et al., 2020). And then, once a new framework appears, the sheer volume of news and commentary is overwhelming. We need smart, automated systems to sift out what matters and cut down on all the noise (Chen et al., 2024).
The problem is, most current models just don’t get the complexity of these distributed systems. A lot of them assume you know everything about how these regulators communicate, but in reality, international relationships are anything but transparent. On top of that, today’s information extraction tools often get tripped up by the complicated language and multiple meanings you find in regulatory news. That leads to summaries that are either vague or miss the point entirely (Chen et al., 2024). And if you only look at small pieces of the puzzle, you lose sight of the global picture, which makes it even harder to understand these agreements (Li et al., 2025).
Here’s what we’re bringing to the table:
We frame the way global regulators agree as a “global regulated state synchronization” problem. We use a scale-free linear protocol that doesn’t depend on knowing the exact communication network (Liu et al., 2022).
We introduce an information extraction pipeline. It uses an improved BERTSum-LSTM model, along with evolutionary fine-tuning, to turn all the crypto framework news into short, trustworthy summaries (Chen et al., 2024; Xiao & Chen, 2023).
We borrow ideas from biology and physics—like how growth gets limited by physical space (Couturier et al., 2009) or how cells regulate genome duplication (Herrick & Bensimon, 2007)—to build a solid way of thinking about the limits and stability of this global regulation effort.
Related Work
Multi-Agent Synchronization and Control Theory
If you want to know how regulators actually come together on a shared framework, you have to look at multi-agent systems. Lately, researchers have zeroed in on how networks of agents—whether they’re linear or nonlinear—manage to sync up (Liu et al., 2020). Liu and colleagues mapped out different types of agent models (like neutrally stable and double-integrator dynamics) and built a protocol where each agent only needs info from its closest neighbors. This matters for regulation, because it shows that you can get global agreement even if nobody has the full picture. There’s also the issue of “actuator saturation”—in plain English, no single regulator has unlimited power—which puts real limits on what any one group can enforce (Liu et al., 2022). Fan and co-authors pushed this further by looking at feedforward nonlinear systems with unknown growth rates. They came up with a logic-based switching gain approach to keep things stable, even when the system isn’t predictable (Fan et al., 2024).
Biological and Physical Analogies of Global Regulation
Global regulation isn’t just something people do—it shows up in biology and physics, too. Scientists have studied the global regulation of genome duplication...
References
Liu, Zhenwei, Nojavanzadeh, Donya, Saberi, Ali, & Stoorvogel, Anton A. (2020). Scale-free Linear Observer-based Protocol Design for Global Regulated State Synchronization of Homogeneous Multi-agent Systems with Non-introspective Agents Subject to Input Saturation. https://arxiv.org/pdf/2004.13479v1
Chen, Yiming, Chen, Haobin, Liu, Simin, Liu, Yunyun, Zhou, Fanhao, & Wei, Bing (2024). Research on Information Extraction of LCSTS Dataset Based on an Improved BERTSum-LSTM Model. https://arxiv.org/pdf/2406.18364v1
Li, Jiahao, Chen, Xinhong, Jiang, Zhengmin, Zhou, Qian, Li, Yung-Hui, & Wang, Jianping (2025). Global Regulation and Excitation via Attention Tuning for Stereo Matching. https://arxiv.org/pdf/2509.15891v1
Liu, Zhenwei, Saberi, Ali, & Stoorvogel, Anton A. (2022). Scale-free linear protocol design for global regulated state synchronization of discrete-time double-integrator multi-agent systems subject to actuator saturation. https://arxiv.org/pdf/2210.14453v1
Xiao, Le, & Chen, Xiaolin (2023). Enhancing LLM with Evolutionary Fine Tuning for News Summary Generation. https://arxiv.org/pdf/2307.02839v2
Couturier, Etienne, Pont, Sylvain Courrech du, & Douady, Stephane (2009). Steric Constraints as a Global Regulation of Growing Leaf Shape. PLoS ONE 4(11): e7968. https://doi.org/10.1371/journal.pone.0007968
Herrick, John, & Bensimon, Aaron (2007). Global regulation of genome duplication in eukaryotes: an overview from the epifluorescence microscope. https://arxiv.org/pdf/0707.3811v4
Fan, Debao, Zhang, Xianfu, Feng, Gang, & Li, Hanfeng (2024). Global Regulation of Feedforward Nonlinear Systems: A Logic-Based Switching Gain Approach. https://arxiv.org/pdf/2406.19718v1
Bratus', Alexander S., Posvyanskii, Vladimir P., & Novozhilov, Artem S. (2009). Existence and stability of stationary solutions to spatially extended autocatalytic and hypercyclic systems under global regulation and with nonlinear growth rates. https://arxiv.org/pdf/0901.3556v1
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