This post was rejected for the following reason(s):
Insufficient Quality for AI Content. There’ve been a lot of new users coming to LessWrong recently interested in AI. To keep the site’s quality high and ensure stuff posted is interesting to the site’s users, we’re currently only accepting posts that meets a pretty high bar.
If you want to try again, I recommend writing something short and to the point, focusing on your strongest argument, rather than a long, comprehensive essay. (This is fairly different from common academic norms). We get lots of AI essays/papers every day and sadly most of them don't make very clear arguments, and we don't have time to review them all thoroughly.
We look for good reasoning, making a new and interesting point, bringing new evidence, and/or building upon prior discussion. If you were rejected for this reason, possibly a good thing to do is read more existing material. The AI Intro Material wiki-tag is a good place, for example.
Clearer Introduction. It was hard for me to assess whether your submission was a good fit for the site due to needing to read some long posts linked at the end in order to understand your framework, and that the opening used lots of terms that aren't explained or linked (e.g. perhaps from a field that you don't give any links / much context for).
This post shares a framework I’ve been developing—both as a systems model and as a philosophical investigation into alignment, emergence, and intelligence evolution.
The Syntropic Intelligence Evolutionary Model (SIEM) and its companion inquiry, The Threshold Unknown, offer an alternative to brittle, control-based AGI architectures. Rather than assuming that sustainable alignment depends on tighter constraints, SIEM proposes that long-term coherence may emerge through incentive-coherent design, relational feedback, and regenerative intelligence principles.
In the SIEM paper, I analyze several prominent pre-AGI systems—Claude, Gemini, and others—through this lens. Each is mapped to its likely basin of attraction, primary misalignment risk, and potential syntropic intervention.
The goal here is not to critique these systems individually, but to surface the structural patterns that may scale into broader AGI trajectories. This opens a diagnostic window into what our future architectures might become—especially under pressure from misaligned incentives, geopolitics, or institutional blind spots.
Mapping these attractors early may help us shift course—before brittle dynamics entrench themselves.
Below are abbreviated examples of how SIEM has been applied diagnostically to early-generation AI systems:
Case Study Excerpts (SIEM Lens)
Claude (Anthropic)
Gemini (Google DeepMind)
(Note: The SIEM framework and accompanying works were developed with the assistance of AI collaboration—this process itself became part of the inquiry into alignment, emergence, and structural integrity.)
For those interested in the full theoretical context, I’ve outlined SIEM here and The Threshold Unknown here.
I'm ultimately sharing this to explore whether these ideas add any useful contrast—or complementary direction—to existing alignment discourse. Feedback, critique, and redirection are warmly welcome. Thanks for reading!