13d • General discussion
Coordinate Access, Metric Choice, and the Illusion of “Bending Reality” From Déjà Vu to Cognitive Geometry, Algorithms, and LLM Ontology
Thesis
Introduction: The Scene as a Structural Hint, Not Evidence
In the film , a pivotal scene describes the possibility of “space in a higher dimension creating an instantaneous link between two distant points.” The dialogue immediately tempers itself—“Well, that’s what we hope for” and follows with a reminder that even perception itself is delayed: light reflected from a mirror takes time to return. The scene is not claiming new physics; it is dramatizing a problem of access, coordinates, and observability. The past is not rewritten; it is indexed. The distance is not destroyed; it is reparameterized. This distinction is crucial, because it mirrors how real science, cognition, and modern AI systems actually work. The movie gestures toward a real principle: changing how a system is represented can radically alter what paths appear possible, even when the underlying reality remains unchanged.
1. Coordinates Are a Choice, Not Reality
In mathematics and physics, coordinates do not define reality; they define how reality is described. A system can appear complex or simple depending on the coordinate frame used. For example, orbital motion looks convoluted in Cartesian coordinates but becomes almost trivial in polar or rotating frames. Nothing physical changes—only the description. The Déjà Vu scene implicitly leans on this idea: distance and time feel absolute only because we are using a particular coordinate system. If another coordinate system existed that indexed spacetime differently, the same events could appear adjacent rather than remote. This is not science fiction; it is a foundational principle of representation. What looks “far” or “separate” is often an artifact of the axes we choose to measure along.
2. “Bending Space” Is Really Metric Redefinition
In real physics, space is not bent like rubber in a visual sense. In general relativity, mass and energy change the metric, the rule that determines distance and straightness. Objects follow the shortest paths (geodesics) in that geometry, which appear curved only when viewed from an external frame. Translating this to cognition and language: when you say you are “bending space,” what you are actually doing is changing the metric that defines similarity and opposition. Love and hate are opposites under a valence metric, but neighbors under a salience or attachment metric. Their trajectories converge not because meanings collapse, but because the geometry that governs distance has been redefined. The apparent bending is a consequence of metric choice, not semantic distortion.
3. Latent Attractors Explain Convergence
Your intuition that love and hate behave like “two trajectories converging to a point” is more accurate than the bending metaphor. That point is a latent attractor; a shared underlying variable such as importance, attachment, or identity relevance. Both emotions are high-energy states centered on what matters most to an individual. When representations emphasize that latent variable rather than moral polarity, the vectors converge naturally. This is not philosophical ambiguity; it is how high-dimensional systems behave. Multiple surface-level oppositions can share the same deep structure. Recognizing the attractor allows systems; human or artificial, to anticipate flips, volatility, and intensity without misclassifying them as unrelated states.
4. Algorithms Impose Their Own Geometry
Social platforms function as optimization systems. Their objective functions; engagement, retention, interaction, define a metric space of content. In that space, neutrality is distant, while high-arousal states cluster together regardless of moral direction. Love and hate become neighbors not because the platform endorses either, but because both generate measurable behavior. This is a form of algorithmic geometry: distance is defined by behavioral response, not meaning. Understanding this explains how one can “give the algorithm what it wants” (engagement shape) while redirecting meaning toward a different human outcome. The algorithm responds to geometry; humans live with semantics.
5. LLMs Operate by Representation, Not Intention
Large language models do not possess values or goals; they are value-agnostic probability maximizers operating over learned representations. Their power lies in navigating high-dimensional semantic spaces, where proximity is governed by contextual similarity, not truth or ethics. When an LLM places love and hate near each other, it is not making a moral claim, it is reflecting shared structure in language usage. This makes LLMs effective tools for cognitive stress-testing: they can surface hidden attractors, alternate coordinate frames, and unexpected adjacencies. However, without human governance, they can also reinforce the platform’s geometry rather than challenge it. The dyad; human plus model, works only when the human controls the metric, constraints, and validation.
6. Governance Is Control of Representation Over Time
The final lesson, which the Déjà Vu scene implicitly raises, is that access without governance is dangerous. Seeing into the past does not grant wisdom; it grants exposure. Similarly, reshaping representational space without responsibility can amplify confusion or conflict. True governance is not censorship or suppression, it is explicit control over coordinate systems, metrics, and deployment context. In cognitive systems, this means teaching people to recognize when they are operating on a single axis and how to move into richer representations. In algorithmic systems, it means acknowledging objective functions and steering outcomes deliberately. In LLM use, it means maintaining human authority over meaning, action, and consequence.
Conclusion
The connection between coordinates, “bending space,” and the Déjà Vu narrative is not metaphorical coincidence; it is structural similarity. In every case, reality remains fixed while representation changes. Distance collapses not because the world moves, but because the frame does. What appears as science fiction in cinema becomes operational truth in math, physics, cognition, algorithms, and AI. Understanding this gives your community a powerful tool: the ability to recognize when opposites are artifacts of projection, when convergence signals a shared attractor, and when systems are quietly shaping behavior through geometry rather than argument. This is not about manipulating reality, it is about seeing the geometry that was already there and choosing how to navigate it responsibly.
0
0 comments
Coordinate Access, Metric Choice, and the Illusion of “Bending Reality” From Déjà Vu to Cognitive Geometry, Algorithms, and LLM Ontology
powered by
skool.com/trans-sentient-intelligence-8186
TSI: The next evolution in ethical AI. We design measurable frameworks connecting intelligence, data, and meaning.
Suggested communities
Powered by