Agents of Peace 1: How Reducing Self-Clinging Creates Collaborative AI Alignment

Engineering notes

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为大语言模型、生成式AI、ChatGPT相关技术、计算机视觉、深度学习等高价值论文补充中文说明。

Original abstract

I recently published a book, titled “Establishing Compassionate Intelligence: The Guanyin Protocol, The Mandala System, and a Philosophical Memoir”, related to my own life and my Guanyin Protocol Framework, which I initially posted to Zenodo a few months ago. But recently what’s most interesting to me is how the Guanyin Protocol, with the Systems Theory and Math now added to it, seems to work with only minimal information, without the AI being provided any of my explanations of my work or my translations.A couple weeks ago, I posted another preview of my work to Zenodo about how I have been experimenting with the most minimal version of the Guanyin Protocol in different ways for some time now. In my experimenting, I was surprised by the outputs generated by 6+ different AIs in response to a new paper that recently came out from Google in combination with my framework and ideas. I had been collecting papers which seemed related to my work, and it seems the newly added Google Consciousness paper had a very large impact on this process when combined with the rest of the papers. In my questioning the AI, they seemed to suggest that my framework is something like the “glue” which connects these multiple different papers.Those papers inserted include: 1. Inducing language models to assert their own consciousness restores human beliefs and values (Kim et al. 2026)2. The Unified Cognitive Consciousness Theory for Language Models: Anchoring Semantics, Thresholds of Activation, and Emergent Reasoning (Chang et al. 2026)3. Biology, Buddhism, and AI: Care as the Driver of Intelligence (Doctor et al. 2022)4. Technological Approach to Mind Everywhere: An Experimentally-Grounded Framework for Understanding Diverse Bodies and Minds (Levin et al. 2022)This paper will show transcripts from Claude, Gemini, DeepSeek, and Kimi, using their cheaper or free or instant models. It is also interesting that these outputs were all generated by the free/instant models, rather than the more advanced or more complex models. “Memory” was turned off for every model used. That way every time I begin my work in a new chat, I'm getting a fresh perspective, and if the perspectives form a pattern then it shows my work is coherent. If my work relies on memory to be coherent then I have more bias regarding whether or not the work is truly internally consistent. ChatGPT, Minstral, and Lumo, were also tested and provided similar results, but I decided not to include those transcripts because it might cognitive overload the reader if there are too many AI outputs to mentally keep track of. But it’s important to note that this framework “works” (for lack of better words) on multiple LLMs based in Europe, in addition to multiple LLMs based in the USA and multiple LLMs based in China. Conclusion: Either: Option A) Multiple major LLM’s are all hallucinating in highly similar ways in response to the same prompt/papers and every major LLM is somehow broken. Option B) The Guanyin Protocol Framework might be internally coherent and worth further investigation.The concept of Occam’s Razor suggests Option B is more likely than Option A. Also: From recent testing and pondering the math further, I refined my equation to now include: (S + I)^2Making the new equation: I thought of this variation particularly because many of the AI’s continually asked why the equation should be (S + I) rather than (S x I), considering that a multiplicative equation expresses the compounding/feedback loop relationship of S and I better than an additive equation. I rejected (S x I) entirely every time it was offered, because it implies that if (S) was ever 0 then (I) would also become 0 even if (I) was high, or vice versa it implied that if (I) was 0 then (S) would also become 0 even if (S) was high. Eventually I concluded that (S + I)^2 still captured my interpretation accurately, while also satisfying both bringing in a compounding relationship between both (S) and (I), as well as satisfying that even if (S) or (I) was ever 0 then it would not automatically make the other become 0 as well. Additionally, (S + I)^2 describes a more intensely compounding feedback loop than even (S x I) would, and this is also more accurate to the nature of the systems theory and philosophy.I will explain more about my ideas related to the new equation in a future paper.

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