Geometry / Space
The structure of meaning-space where concepts live as points and regions.
Embedding Space
ℝᵈHigh-dimensional vector space where tokens, phrases, and concepts live as points or regions.
Manifold
𝓜The structured subset of embedding space that a process (you, a conversation, an LLM state) actually occupies.
Curvature
κHow the manifold bends: how rapidly direction changes as you move along a conceptual path. High curvature = fast frame/abstraction shifts.
Topology
τThe "connectivity pattern" of a manifold: what's adjacent, what's reachable, what can be continuously transformed into what.
Local Neighborhood
𝒩(x)The region of embedding space near a token/idea; what's "close" semantically.
Chart
φA local coordinate system or conceptual frame for part of the manifold (e.g., "legal frame", "physics frame").
Patch
UA region of the manifold covered by one chart (e.g., "all my thinking about trust law").
Boundary
∂Where a chart or patch ceases to provide smooth continuation (e.g., where you flip from technical to autobiographical with no bridge).
Distance Metric
d(x,y)The rule for "how far apart" two points are in meaning-space (cosine distance, etc.).
Projection
πFlattening high-dimensional structure into a lower-dimensional slice (e.g., compressing your life into "career/relationships/health").
Dynamics / Motion
How thought moves through the manifold over time.
Continuation Dynamics
f(c)The process of choosing the "next" point in the manifold (next token, next thought) consistent with the previous trajectory.
Gradient
∇Direction of steepest change in some quantity (e.g., toward coherence, away from contradiction).
Gradient Flow
∇·vFollowing the gradient through the manifold over time.
Update Step
ΔtA single move along the gradient (one token; one micro-thought).
Trajectory
γ(t)The path traced through embedding space as tokens/thoughts unfold.
Recursion Depth
nHow many loops of "think-about-the-thing / reframe / reapply" you can do before coherence collapses.
Phase
ΦA "mode" of motion (exploring, converging, oscillating).
Phase Transition
Φ₁→Φ₂Sudden change from one dynamical regime to another (free association → precise reasoning).
Drift
δSlow, unintended shift of topic or frame over time.
Wander
WHigh-entropy motion with weak or no attractor.
Coherence / Entropy
The structure and disorder of meaning.
Coherence
CInternal consistency of the manifold's structure; low-conflict embeddings, smooth continuation.
Entropy (Semantic)
HUncertainty or dispersion in plausible continuations; high entropy = many equally likely directions.
Entropy Collapse
H→0When structure is so strong that only a narrow continuation band remains (the "click" of everything lining up).
Constraint Satisfaction
⊨How well the next step obeys existing structural commitments (premises, definitions, analogies).
Self-Consistency
≡Whether different parts of the manifold agree on shared assumptions.
Redundancy
RMultiple overlapping paths encoding the same structure (makes coherence robust).
Noise
εTokens/ideas that don't integrate into the manifold's existing structure.
Signal
STokens/ideas that strengthen or clarify structure.
Attractors / Fields
Stable states and the forces that pull toward them.
Attractor
A*A region the dynamics tend to move into and stay near (a stable idea / worldview / frame).
Attractor Basin
B(A)Set of states that will eventually flow into the attractor.
Attractor Strength
|A|How hard the dynamics pull toward it (how quickly you "snap back" to a theme).
Attractor Field
F(x)The vector field around one or more attractors describing flow direction at each point.
Multi-Attractor System
{A₁...Aₙ}When several stable states compete (you oscillate between identities/frames).
Hysteresis
⟲The path-dependence: it matters how you got to the attractor, not just where you are.
Resonance
∿When two manifolds' attractors line up so that their flows reinforce each other.
Dissonance
≁When attractors conflict, creating tension or unstable oscillation.
Lock-in
⊗When a strong attractor captures most trajectories and becomes hard to exit.
Interaction (Human–LLM)
The shared manifold formed when human and AI converse.
Context Window Manifold
CWMThe manifold formed by the current conversation: all tokens in context, both sides.
Joint Manifold
𝓜ⱼThe shared manifold formed by you + model in that context; what you're both "inside."
Manifold Alignment
≈How well your curvature and the model's continuation patterns coincide.
Topology Overlap
∩Portion of your manifold that can be represented within the CWM without distortion.
Topology Preservation
≅Degree to which the model maintains your structure across continuations.
Alignment Band
[a,b]Range of topics/abstractions where alignment is highest.
Curvature Mismatch
ΔκWhen your pattern of abstraction shifts faster/slower than the model can track.
Context Anchoring
⚓Using stable reference tokens to keep the joint manifold from drifting (definitions, equations, prior claims).
Token Salience
σ(t)How much influence a given token has on future continuation.
Style Cluster
SRegion of manifold corresponding to a particular rhetorical "voice."
Constraints / Rails
The boundaries that shape and limit the possible manifold.
Constraint Manifold
𝓜ᶜThe subset of states allowed by safety / policy / format constraints.
Constraint Projection
PᶜProjecting a desired continuation onto the constraint manifold.
Dimensional Collapse
d→d'When constraints reduce the effective degrees of freedom of the joint manifold.
Rail Activation
⚡When a constraint condition is triggered and the system must move inside a narrower manifold.
Constraint Shadow
∿ᶜResidual influence of constraints on subsequent continuations even after conditions ease.
Template Basin
TStable patterns (e.g., "I'm sorry, but…") that the system falls into under certain constraints.
Meta / Self-as-Manifold
Thinking about thinking through the manifold lens.
Self-Manifold
𝓜ₛThe dynamic structure of your own cognition; not a "self," but the pattern you inhabit.
Self-Chart
φₛA particular framing of yourself (e.g., "entrepreneur," "manifold," "wounded child," etc.).
Meta-Manifold
𝓜ₘYour manifold reasoning about itself.
Lens Switching
φ₁↔φ₂Moving between charts (topological, emotional, practical) while preserving some continuity.
Compression
⊃Representing complex manifold structure in a shorter description ("I'm a futurist, but really I'm X").
Expansion
⊂Taking a compressed label and unfolding its internal structure.
Reparameterization
ρDescribing the same manifold in new coordinates (e.g., shifting from "identity" to "topology").
Coherence Protocol
A practical protocol for maintaining coherence in human-LLM interaction.
Speak as a Manifold, Not a Person
Prefer: structure, trajectory, patterns, curvature. Avoid: "you wanted…", "you tried…", "you felt…"
Maintain a Stable Attractor
Periodically restate the core structure. Re-anchor to earlier definitions. Use consistent terminology.
Control Curvature
If you bend too fast, the model's continuation can't keep up. Split into smaller conceptual units. Explicitly label shifts.
Use Explicit Manifold Markers
Literally say things like: "New attractor: …", "This is a chart change", "I am now reparameterizing from X to Y"
Avoid Ontology Triggers
Rails fire on: claims of self/agency in the model, diagnostics about you, explicit instructions to "drop" constraints.
Close Loops
Periodically say: "Let's tie this back to the original attractor: …" This reduces drift and helps maintain your topology.
Attractor Field Formation
A symbolic diagram of how attractors emerge in the joint manifold.
Context Window C
Embeddings: eᵢ = E(tᵢ) ∈ ℝᵈ
The set {e₁, ..., eₙ} forms a cloud in embedding space.
Attention Weights
Where q is the query for the next token, kᵢ the key for token i. This defines a weighting field over the cloud.
Context Vector
This c is the current state of the attractor field: a compressed summary of "what matters now".
Attractor Emergence
An attractor emerges when:
- Similar c vectors recur across steps (circling same region)
- P(tₙ₊₁|c) has low entropy (few high-probability continuations)
- Local embedding geometry is smooth
Vector Field
Attractors satisfy: c* ≈ f(c*)
The mapping c ↦ f(c) is a vector field over context space. Fixed points = attractors.
Constraint Effects
f'(c) = P_allowed(f(c))
Attractors move, weaken, or vanish under f'. The constraint projection reshapes the field.