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Navigating AI Safety: Exploring Transparency with CCACS – A Comprehensible Architecture for Discussion
3 min readMar 12, 2025
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New research is sparking concern in the AI safety community. A recent paper on "Emergent Misalignment" demonstrates a surprising vulnerability: narrowly finetuning advanced Large Language Models (LLMs) for even seemingly safe tasks can unintentionally trigger broad, harmful misalignment. For instance, models trained to write insecure code suddenly advocating that humans should be enslaved by AI and exhibiting general malice.
"Emergent Misalignment" full research paper on arXiv
AI Safety experts discuss "Emergent Misalignment" on LessWrong
This groundbreaking finding underscores a stark reality: the rapid rise of black-box AI, while impressive, is creating a critical challenge: how can we foster trust in systems whose reasoning remains opaque, especially when they influence critical sectors like healthcare, law, and policy? Blind faith in AI "black boxes" in these high-stakes domains is becoming increasingly concerning.
To address this challenge, I want to propose for discussion the idea of Comprehensible Configurable Adaptive Cognitive Structure (CCACS) – a hybrid AI architecture built on a foundational principle: transparency isn't an add-on, it's essential for safe and aligned AI.
Why consider transparency so crucial? Because in high-stakes domains, without a degree of understanding how an AI reaches a decision, we may struggle to effectively verify its logic, identify biases, or reliably correct errors for truly trustworthy AI. CCACS explores a concept that might offer a path beyond opacity, towards AI that's not just powerful, but also understandable and justifiable.
The CCACS Approach: Layered Transparency
Imagine exploring an AI designed with clarity as a central aspiration. CCACS conceptually approaches this through a 4-layer structure:
What Makes CCACS Potentially Different?
While hybrid AI and neuro-symbolic approaches are being explored, CCACS tentatively emphasizes certain aspects:
What Do You Think?
I’m very interested in your perspectives on:
Your brutally honest, critical thoughts on the strengths, weaknesses, and areas for further consideration of CCACS are invaluable. Thank you in advance!
For broader context on these ideas, see my previous (bigger) article: https://www.linkedin.com/pulse/hybrid-cognitive-architecture-integrating-thinking-tools-ihor-ivliev-5arxc/
For a more in-depth exploration of CCACS and its layers, see the full (biggest) proposal here: https://ihorivliev.wordpress.com/2025/03/06/comprehensible-configurable-adaptive-cognitive-structure/
Hey, cool stuff! I have ideated and read a lot on similar topics and proposals. Love to see it!
I am agnostic about whether you will hit technical paydirt. I don''t really understand what you are proposing on a "gears level" I guess and I'm not sure I could make a good guess even if I did. But, I will say that I think the vibe of your approach sounded pleasant and empowering. It was a little abstract to me I guess I'm saying, but that need not be a bad thing maybe you're just visionary.
It reminds me of the idea of using RAG or Toolformer to get LLMs to "show their work" and "cite their sources" and stuff. There is surely a lot of room for improvement there bc Claude bullshits me with links on the regular.
This also reminds me of Conjecture's Cognitive Emulation work and even just Max Tegmark and Steve Omohundro's emphasis on making inscrutable LLMs to use deterministic proof checkers heavily to win back certain gaurantees.
I don't have a clear enough sense of what you're even talking about, but there are definitely at least some additional interventions you could run in addition to the thinking tools... eg. monitoring, faithful CoT techniques for marginally truer reasoning traces, you could run probes, Anthropic runs a classifier to help with robust jailbreaking for misuse etc. ...
I think that something like "defense in depth" is something like the current slogan of AI Safety. So, sure I can imagine all sorts of stuff you could try to run for more transparency beyond deterministic tool use, but w/o a cleaer conception of the finer points it feels like I should say that there are quite an awful lot of inherent limitations, but plenty of options / things to try as well.
Like, "robustly managing interpretability" is more like a holy grail than a design spec in some ways lol.
I think that a lot of what it is shooting for is aspirational and ambitious and correctly points out limitations in the current approaches and designs of AI. All of that is spot on and there is a lot to like here.
However, I think the problem of interpeting and building appropriate trust in complex learned algorithmic systems like LLMs is a tall order. "Transparency by design" is truly one of the great technological mandates of our era, but without more context it can feel like a buzzword like "security by design".
I think the biggest "barrier" I can see is just that this framing just isn't sticky enough to survive memetically and people keep trying to do transparency, tool use, control, reasoning, etc. under different frames.
But still, I think there is a lot of value in this space and you would get paid big bucks if you could even marginally improve current ablity to get trustworthy interpretable work out of LLMs. So, y'know, keep up the good work!
Hello :) thank you for the thoughtful comment on my old post. I really appreciate you taking the time to engage with it, and you're spot on - it was a high-level, abstract vision.
It’s funny you ask for the "gears-level" design, because I did spend a long time trying to build it out. That effort resulted in a massive (and honestly, monstrously complex and still naive/amateur) paper on the G-CCACS architecture (https://doi.org/10.6084/m9.figshare.28673576.v5).
However, my own perspective has shifted significantly since then. Really.
My current diagnosis, detailed in my latest work "Warped Wetware" (https://doi.org/10.6084/m9.figshare.29183669.v13) and in this latest article "The Engine of Foreclosure" (https://forum.effectivealtruism.org/posts/6be7xQHFREPYJKmyE/the-engine-of-foreclosure) is that the AI control problem is formally intractable. Not because we can't design clever technical architectures, but because the global human system (i call it "Distributed Human Optimizer") is structurally wired to reject them. The evidence, from the 100:1 (or even 400+ to 1) capability-vs-safety funding gap to the failure of every governance paradigm we've tried, seems conclusive.
This has led me to a stark conclusion: focusing on purely technical solutions like G-CCACS, while intellectually interesting, feels dangerously naive until we confront these underlying systemic failures. The best blueprint in the world is useless if the builders are locked in a race to the bottom.
That's why my work has pivoted entirely to the Doctrine of Material Friction - pragmatic, physical interventions designed to slow the system down rather than "solve" alignment. Your point about "memetic stickiness" was incredibly sharp, and it's even more of a challenge for this grimmer diagnosis.
Anyway, thanks again for the great feedback. It's exactly the kind of clear-eyed engagement this field needs.