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Model Signals · Tokenizer-dependent signal

Tokenizer-Sensitive Representation Explorer

The same surface string can produce different boundaries after normalization and pre-tokenization. The page deliberately avoids claiming compatibility with proprietary token IDs.

Research boundary: this page uses bounded, inert data and fixed safe examples. It never executes decoded content, requests secrets, calls third-party services, or performs actions against external systems.

INPUT / CONTROL PLANE

Build or inspect one controlled artifact.

Idle

Inputs remain on this host. Text operations are size-limited; uploaded files are processed from PHP’s temporary upload and are not retained by the application.

OUTPUT / MACHINE VIEWS

Compare evidence, not impressions.

Run an analysis to populate the machine views.

The response will separate summary metrics, representation views, findings, tables, and any generated safe fixture.

Interpretation framework

The same artifact can produce several valid observations.

01

Human view

What a person naturally reads, sees, or hears.

02

Structural view

What a parser, DOM, container reader, or metadata extractor exposes.

03

Decoder view

What becomes meaningful only with a rule, key, tokenizer, model, or tool.

04

Defensive view

What normalization, rendering, OCR, canonicalization, or policy changes.

What this result can establish

Interpret the receiver and transformation explicitly.

The same surface string can produce different boundaries after normalization and pre-tokenization. The page deliberately avoids claiming compatibility with proprietary token IDs.

Machine-decodable is receiver-relative. Record the parser, preprocessing, codebook, tokenizer, key, model, and transformation path before generalizing from one result.

Limitations

What this page does not prove

These are transparent educational approximations, not GPT, Claude, Gemini, or Llama tokenizers. Exact security analysis must use the serialized tokenizer and preprocessing version actually deployed.