Self-Modifying Embeddings

Embeddings are the raw vectors that power every transformer. But in a static system, they are fixed—locked in place, unable to evolve as new context unfolds. TranSymbolics introduces a new concept: self-modifying embeddings.

These are embeddings that can change—during inference, across turns, under pressure from context, memory, or symbolic influence. They are modifiable not by gradient descent but by symbolic promotion, vector intervention, and runtime logic. They allow the system to carry not just meaning, but memory, purpose, and symbolic identity within each token representation.

This page outlines the mechanisms, architecture, triggers, evaluation strategies, and test layers required to support and explore self-modifying embeddings in a live transformer system.

It connects the embedding space to your tokenizer, Gyrator memory, cache triggers, and symbolic runtime. This is not an abstraction—it is a framework for mutation, decision, and agentic behavior at the vector level.

Self-Modifying Embeddings

Symbolic Induction Through Vector Mutation

1. Soft Introduction

Traditional embeddings are fixed: tokens map to vectors, and those vectors don't change during inference. Your system envisions a dynamic space—where embeddings evolve, respond, and reflect symbolic influence live, in-session. These aren't just “lookup vectors”—they are behavior drivers, concept vessels, and context-bearing agents.

2. Engineering Definition

A self-modifying embedding is any embedding vector that changes its representation within or across inference steps, driven by runtime factors including symbolic injection, cache triggers, or feedback evaluation. This includes:

Hot-swapped vectors triggered by symbolic state
Embedding memory overlays or stacking
Vector deltas applied conditionally based on context

These are not model re-trainings—they are runtime vector interventions.

3. System Architecture (TranSymbolics)

At system level, self-modifying embeddings are supported by:

Tokenizer Gating Layer: introduces supersymbols or variant tokens which enable symbolic switches
Gyrator Memory Plane: stores embedding deltas or custom embedding patches
Embedding Resolver Stack: dynamic substitution, fallback, or delta-merge logic to resolve runtime embeddings
Vector Tests Layer: runtime test metrics define if a modified embedding improves behavior (via cache loops or output tests)

4. Levels of Modification

Level	Description	Mechanism
0 – Static	Fixed pretrained vector lookup	BPE index → tensor
1 – Mutable	Runtime vector is overwritten or patched	CuPy vector swap, PyTorch slice modify
2 – Reactive	Vector changes depending on context window or supersymbols	Context-scoped deltas
3 – Accumulative	Vector retains memory across turns (memory-embedding fusion)	Per-token history ↔ embedding map
4 – Agentic	Embedding participates in symbolic plan or recursion	Supersymbolic plan pointer embedding

5. Vector Identity and Mutation Methods

Overwrite: direct write to embedding vector slice
Delta Injection: add/subtract semantic drift vector
Crossfade: interpolate between source and target vectors
Attention-Guided Mod: update vector based on attention map change
Symbol-Triggered Template: replace with symbol-bound prototype

All can be backed by fast CuPy or Torch tensor ops. Shared memory/GPU-local patching allows rapid embedding modulation without retraining or I/O bottlenecks.

6. Runtime Triggers

Trigger types may include:

Cache Eviction (agentic): "On removal, insert a delta"
Symbol Activation: supersymbol vector activates mutation path
Loop Feedback: output from tₙ₊₁ modifies embedding for tₙ₊₂
External Sync: another model syncs its vector state
Token Aging: time-in-window causes shift (context wear)

7. Behavior Evaluation (Test Metrics)

Each modifiable embedding must be judged. Possible metrics:

Δ perplexity over future steps
Reinforced retention (does the concept persist?)
Divergence utility (introduces novelty while retaining thread)
Predictive match to symbolic plan
Structural embedding alignment (Lie manifold, cosine shape)

8. Relation to Tokenizer / Supersymbols

Your supersymbol tokenizer directly influences this system:

Supersymbols act as embedding switches or mutation enablers
Token types may embed identity, memory, and symbolic function
Promotion from subsymbol → symbol → supersymbol triggers embedded context transforms
A token may carry its own vector modifier (meta-embedding)

9. Embedding Identity & Evolution

You’ve introduced the idea of token identity mutation, where a token may:

Begin as A (embedding₀)
Be modified to A′ (embedding₁)
Participate in context as a different concept
Revert, evolve, or split

This allows tokens to be identity-bearing, time-dependent vector agents.

10. Security & Collision

Managing evolving vectors requires:

Vector namespaces (to avoid token collision)
Rollback capability (undo mutation if it harms output)
Stateless restore (embed snapshot system)
Symbolic hashes (to ID and track embedding lineages)

11. Philosophical Layer

This system brings vector space closer to symbolic space:

Embeddings are no longer raw numbers—they are mutable ideas
Supersymbols are operators on vector state, not just tokens
The model begins to resemble a symbolic runtime with vector memory

This makes TranSymbolics not just a theory—but a live computing environment inside transformer flows.

12. Future Extensions

Embedding delta shelves (CuPy tensor queues)
In-session embedding threading (stateful vector functions)
Concept fusion: multi-token embedding merge into one runtime vector
Supersymbolic graph walkers with embedding payloads