The Structure, Transport, and Nature of Context in the TranSymbol Protocol

Version: v1.0
Author: Arnie Widdowson
System: TranSymbolics / Gyrator Plug-in Framework
Date: [Autogenerated at save]

Overview

This document defines the folder structure used to save and restore full transformer context snapshots, including symbolic extensions such as obfuscation, encryption, and plug-in metadata. The structure supports both human auditing and automated traversal.

All saved contexts reside under a root directory:

/media/krusty/gm/gm194/context/

Each save creates a timestamp-named subdirectory using this pattern:

m<M>d<D>x<h>x<m>x<s>

Where:

• M = month
• D = day
• h = hour
• m = minute
• s = second

Example: m30d2x15x23x48x10

This ensures natural sort order and time uniqueness.

Directory Layout

m30d2x15x23x48x10/├── input_ids.npy├── attention_mask.npy├── position_ids.npy├── past_key_values/│   ├── 0_key.npy│   ├── 0_value.npy│   ├── 1_key.npy│   ├── 1_value.npy│   └── ...├── metadata/│   ├── config.txt│   ├── symbolic_map.npy│   ├── obfchain.txt│   └── keylog.txt

Expanded File Descriptions

input_ids.npy

What it is: A file that stores the actual text as numbers. These numbers are token IDs, which are how the model reads and understands text.

Format: A 2D table of numbers (a NumPy array). Each row is one piece of text (like a sentence). Each number in the row is one token.

[  [101, 2074, 1037, 2307, 2154, 102],  [101, 2129, 2024, 2017, 102,    0]]

Example:
101 might mean “[START]”,
102 might mean “[END]”,
“just”, “a”, “good”, “day”, etc.,
0 at the end is padding

Why it matters: This is the actual input that was given to the model. Without it, the saved memory (the “context”) makes no sense.

Symbolic use: You might change the IDs using a secret map, so the text is hidden unless the map is known. That’s symbolic obfuscation.

attention_mask.npy

What it is: A matching table that tells the model which tokens are real and which are just padding.

Format: Same size as input_ids.npy, just 1s and 0s.

[  [1, 1, 1, 1, 1, 1],  [1, 1, 1, 1, 1, 0]]

What it means: 1 = real token, 0 = ignore this (padding)

Why it matters: It controls which tokens affect the model’s attention. If this is wrong, the model could focus on meaningless input.

Symbolic use: Usually not altered symbolically. But it can be encrypted for storage if needed.

position_ids.npy

What it is: Tells the model where each token appears in the full conversation.

Format: A 2D array of integers indicating position indexes.

[  [0, 1, 2, 3, 4, 5],  [6, 7, 8, 9,10,11]]

Why it matters: These must align with the cached positions. Mismatches can cause attention collapse or drift.

Symbolic use: Should remain exact. Minor symbolic drift only under controlled transformation.

past_key_values/

What it is: The model's memory—key/value tensors for each transformer layer.

1. Mental Image

Imagine each transformer layer holding index cards. Each card has a key (what it remembers) and a value (what it retrieved). These are filed in a stack—one for each layer.

2. File Layout

past_key_values/├── 0_key.npy├── 0_value.npy├── 1_key.npy├── 1_value.npy...├── N_key.npy├── N_value.npy

3. Shape Logic

Each key/value tensor is shaped like:

(batch, heads, tokens, head_dim)

Example: (1, 8, 128, 64) means:

• 1 conversation
• 8 attention heads
• 128 tokens remembered
• 64 values per token per head

4. Save Format

These are saved as uncompressed .npy files after converting from CuPy:

np.save(path, cp.asnumpy(tensor))

5. Why Save These?

They hold the actual memory of the transformer. Saving them allows the model to skip replaying the full prompt.

6. Symbolic Plug-in Potential

You can symbolically transform them with:

• Rotate attention heads
• Scramble token dimension
• XOR with symbolic mask
• Add structured offset per head

7. Error Risk

If any tensor is corrupted, missing, or mismatched, the model may hallucinate, freeze, or collapse.

8. Symbolic Insight

Each key/value tensor encodes: “What did this token mean at that layer, through that head?” This is symbolic state, not just math.

metadata/config.txt

What it is: Human-readable text metadata. Used to match model configuration and cache format.

model: Gemma-2Bdtype: float16cache_impl: standard

metadata/symbolic_map.npy

What it is: NumPy array mapping obfuscated token IDs back to their true values.

Used to reverse symbolic masking during contextload().

metadata/obfchain.txt

What it is: Describes the sequence of symbolic and/or encryption steps applied during contextsave().

rotate_heads layer=0 order=[3,0,2,1]xor_mask value seed=982734remap_ids map=symbolic_map.npy

metadata/keylog.txt

What it is: Records key material, agent names, prompts, or symbolic signatures related to this save.

seed: 42918user: agent_fkeyhash: ab7721a3...source: symbolic prompt #12

Resolution Chain

During contextload(), the recovery sequence follows:

1. Read config.txt to verify model compatibility
2. Load and reverse each op from obfchain.txt (in reverse order)
3. Apply symbolic map to input_ids.npy if obfuscated
4. Load key/value tensors and match against position
5. Reconstruct usable past state

Each op in gyrobf() must leave an entry in obfchain.txt.

Big 8 + 4 Table

#	Gyrator Element	File or Source	Description
1	Token IDs	input_ids.npy	Raw token sequence
2	Attention Mask	attention_mask.npy	Binary mask
3	Position IDs	position_ids.npy	Absolute positions
4	Past Keys	past_key_values/*_key.npy	KV memory: keys
5	Past Values	past_key_values/*_value.npy	KV memory: values
6	Model Config	config.txt	Architecture and dtype
7	Cache Format	config.txt (cache_impl)	Cache structure (standard, etc.)
8	Cache Position	position_ids + KV size	Offset for continuation
9	Symbolic Map	symbolic_map.npy	Token remapping table
10	Obf Chain	obfchain.txt	Transformation log
11	Key Log	keylog.txt	Seed, user, session
12	Context Metadata	config.txt, protocol.txt	Format and versioning