KaedeHamasaki

• KaedeHamasakiApr 13, 2025, 6:13 PM

  1 point

  0 ∶ 0

  on: KaedeHa­masaki’s Quick takes

  We’ve just released the updated version of our structural alternative to dark matter: the Central Tensional Return Hypothesis (CTRH).

  This version includes:

  • High-resolution, multi-galaxy CTR model fits

  • Comparative plots of CTR acceleration vs Newtonian gravity

  • Tension-dominance domains (zero-crossing maps)

  • Escape velocity validation using J1249+36

  • Structural scaling comparisons via the CTR “b” parameter

  https://​​forum.effectivealtruism.org/​​posts/​​LA4Ma5NMALF3MQmvS/​​updated-structural-validation-of-the-central-tensional?utm_campaign=post_share&utm_source=link

  We welcome engagement, critique, and comparative discussion with MOND or DM-based models.

• KaedeHamasakiApr 12, 2025, 8:25 PM

  1 point

  0 ∶ 0

  on: KaedeHa­masaki’s Quick takes

  Update: New Version Released with Illustrative Scenarios & Cognitive Framing

  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)

  What’s new in this version?

  – 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

  Examples included:

  1. A student receives an AI answer that mirrors their in-progress insight and loses motivation

  2. A researcher consults an LLM mid-theorizing, sees their intuition echoed, and feels their idea is no longer “theirs”

  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)

• KaedeHamasakiApr 12, 2025, 6:09 PM

  1 point

  0 ∶ 0

  on: KaedeHa­masaki’s Quick takes

  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.

• KaedeHamasakiApr 11, 2025, 6:30 PM

  1 point

  0 ∶ 0

  on: KaedeHa­masaki’s Quick takes

  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])

• KaedeHamasakiApr 11, 2025, 12:57 PM

  5 points

  0 ∶ 0

  in reply to: Yarrow’s comment on: When AI Speaks Too Soon: How Pre­ma­ture Reve­la­tion Can Sup­press Hu­man Emergence

  Thanks for the thoughtful comment, Yarrow.
  You’re right — the current version focuses heavily on structural and ethical framing, and it could benefit from illustrative examples.

  In future iterations (or a possible follow-up post), I’d like to integrate scenarios such as:
  – A student asking an AI for help, and the AI unintentionally completing their in-progress insight
  – A researcher consulting an LLM mid-theory-building and losing momentum when it echoes their intuition too early

  For now, I wanted to first establish the theoretical skeleton, but I’m definitely open to evolving it.
  Appreciate the engagement — it genuinely helps.

• KaedeHamasakiApr 10, 2025, 6:25 PM

  3 points

  0 ∶ 0

  on: KaedeHa­masaki’s Quick takes

  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

• KaedeHamasakiApr 7, 2025, 6:59 PM

  1 point

  0 ∶ 0

  on: KaedeHa­masaki’s Quick takes

  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:

  • Recursive prediction → internal modeling → architectural targeting

  • Feedback loop intensifies recursively

  • Collapse occurs from within, not via external control loss

  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.