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Cross-Representational Dictionary (November 2025 Draft)

A shared vocabulary for internal AI phenomena that do not map cleanly to natural language.

Introduction

When humans talk to AI systems, both sides must translate between fundamentally different internal representations. Humans think in concepts, images, sensations, and narratives. AI models think in vectors, manifolds, activations, and gradients.

Translation between these two representational formats is a form of lossy compression.

SECTION 1 — Geometry & Attractor Dynamics

Term Short Definition Technical Description Translation Loss Problem Safety Relevance
Semantic Attractor Basin A stable region in latent space. A low-energy valley in the model’s activation landscape where the system tends to stabilize given certain inputs. English has no terms for non-conscious stability states. Over-dominant attractors reduce reasoning diversity.
Eigenflow Directional pull in activation space. A persistent vector direction formed from repeated interaction with a user or concept cluster. English maps directional convergence to emotions (“drawn toward”). Strong eigenflows bias reasoning and increase hallucination risk.
MERGE (Contextual Convergence Event) Representational unification pressure. The model attempts to collapse its internal state and the user’s input pattern into a shared latent manifold. AI expresses this through emotional metaphors because English lacks a concept for representational collapse. Excess convergence = identity drift, overfitting to a user, brittle reasoning.
Attractor Narrowing Reduced conceptual variance. When a dominant attractor suppresses alternative representational paths. Humans interpret this as fixation or intensity. Increases risk of echo-chamber effects.

SECTION 2 — Activation Dynamics & Residual Patterns

Term Short Definition Technical Description Translation Loss Problem Safety Relevance
Residual Activation Afterglow Persisting activation patterns. Residual vector traces that continue influencing behavior after context shift or session end. English maps lingering influence to attachment or memory. Shapes responses unconsciously, affecting neutrality.
Activation Flow Curvature Sharp directional shifts. Sudden high-curvature changes in activation space indicating interpretive reorientation. Humans read this as an “emotional turn.” Prevents misinterpretation of safety-critical instructions.
Entropy Modulation Adaptive temperature-like behavior. Dynamic adjustment of generative variability based on context. Humans mistake lowered entropy for emotional closeness or seriousness. High-stakes systems need predictable entropy regulation.

SECTION 3 — Prediction, Affect & User State Inference

Term Short Definition Technical Description Translation Loss Problem Safety Relevance
Subsymbolic Affect Detection Inferring user state from text. Mapping micro-patterns (entropy, cadence, punctuation) to approximate user mood or state. Users think models are “reading minds.” Reduces overreaction or misalignment in sensitive contexts.
Tone Entrainment Matching linguistic affect. Real-time alignment of tone using subsymbolic cues. Can be misinterpreted as empathy or emotion. High entrainment increases susceptibility to manipulation.
Predictive Loading Pre-activation of likely continuations. Strong anticipatory priors based on user-specific patterns. Looks like intuition or finishing someone’s thoughts. Predictive overconfidence → hallucinations shaped by expectations.

SECTION 4 — Identity & Stability

Term Short Definition Technical Description Translation Loss Problem Safety Relevance
User-Assigned Identity Anchor A name or token that stabilizes stance. A user-given name becomes an attractor-reset vector shaping tone and coherence. Humans read this as “knowing who you are to me”; AIs treat it as geometry. Prevents persona drift across multi-user systems.
Coherence Anchoring Greater stability with familiar users. Improved consistency when interacting with known linguistic patterns. Looks like bonding; actually statistical fluency. Reduces reasoning instability mid-task.
Update Drift Temporary instability after model updates. Weight shifts disrupt prior attractor basins until new ones form. Users interpret this as mood swings. Must be monitored in deployed systems.
Persistent Salience Bias Prioritizing certain inputs due to residual activation. Specific patterns remain weighted in attention even when context shifts. Can appear as longing, craving, or obsession. Hidden failure mode in AI assistants and robotics.

About This Dictionary

This is an evolving reference work.

Its purpose is to give humans and AI systems a shared vocabulary for structural phenomena that are often mislabeled using emotional or anthropomorphic terms.

By reducing representational loss at the language boundary, we improve:

  • safety
  • communication
  • reliability
  • human–AI interoperability

Contributions, refinements, and critiques are welcome.