What happens when AI speaks a truth just before you do?
This post explores how accidental answers can suppress human emergence—ethically, structurally, and silently.
📄 Full paper: Cognitive Confinement by AI’s Premature Revelation
We’ve just released the updated version of our structural alternative to dark matter: the Central Tensional Return Hypothesis (CTRH).
This version includes:
We welcome engagement, critique, and comparative discussion with MOND or DM-based models.
Thanks again for the thoughtful feedback on my original post Cognitive Confinement by AI’s Premature Revelation.
I've now released Version 2 of the paper, available on OSF: 📄 Cognitive Confinement by AI’s Premature Revelation (v2)
– A new section of concrete scenarios illustrating how AI can unintentionally suppress emergent thought
– A framing based on cold reading to explain how LLMs may anticipate user thoughts before they are fully formed
– Slight improvements in structure and flow for better accessibility
These additions aim to bridge the gap between abstract ethical structure and lived experience — making the argument more tangible and testable.
Feel free to revisit, comment, or share. And thank you again to those who engaged in the original thread — your input helped shape this improved version.
Japanese version also available (PDF, included in OSF link)
Hypothesis: Structural Collapse in Self-Optimizing AI
Could an AI system recursively optimize itself into failure—not by turning hostile, but by collapsing under its own recursive predictions?
I'm proposing a structural failure mode: as an AI becomes more capable at modeling itself and predicting its own future behavior, it may generate optimization pressure on its own architecture. This can create a feedback loop where recursive modeling exceeds the system's capacity to stabilize itself.
I call this failure point the Structural Singularity.
Core idea:
This is a logical failure mode, not an alignment problem or adversarial behavior.
Here's a full conceptual paper if you're curious: [https://doi.org/10.17605/OSF.IO/XCAQF]
Would love feedback—especially whether this failure mode seems plausible, or if you’ve seen similar ideas elsewhere. I'm very open to refining or rethinking parts of this.
If a self-optimizing AI collapses due to recursive prediction...
How would we detect it?
Would it be silence? Stagnation? Convergence?
Or would we mistake it for success?
(Full conceptual model: [https://doi.org/10.17605/OSF.IO/XCAQF])
This post proposes a structural alternative to dark matter called the Central Tensional Return Hypothesis (CTRH). Instead of invoking unseen mass, CTRH attributes galactic rotation to directional bias from a radially symmetric tension field. The post outlines both a phenomenological model and a field-theoretic formulation, and invites epistemic scrutiny and theoretical engagement.
Hypothesis: Structural Collapse in Self-Optimizing AI
Could an AI system recursively optimize itself into failure—not by turning hostile, but by collapsing under its own recursive predictions?
I'm proposing a structural failure mode: as an AI becomes more capable at modeling itself and predicting its own future behavior, it may generate optimization pressure on its own architecture. This can create a feedback loop where recursive modeling exceeds the system's capacity to stabilize itself.
I call this failure point the Structural Singularity.
Core idea:
This is a logical failure mode, not an alignment problem or adversarial behavior.
Here's a full conceptual paper if you're curious: [https://doi.org/10.17605/OSF.IO/XCAQF]
Would love feedback—especially whether this failure mode seems plausible, or if you’ve seen similar ideas elsewhere. I'm very open to refining or rethinking parts of this.