# Changing the grain of a conceptual token

ID: adaptive-token-granularity
Canonical: https://rezonansapp.com/nodes/adaptive-token-granularity
Version: 1
SHA256: 1018a0bdb1694df973af5c7544c6f94d173aaf8088860b5d17e763593a61d2fd
Epistemic kind: reconstruction

## Question

Can a sequence or paragraph be represented as one working unit and later refined without losing the relations needed by another model?

## Formulation

Token merging is proposed beyond ordinary segmentation: local units could carry coarse learning and be decomposed when more resources become available.

## Tension

Compression, semantic adequacy, and exact reversibility are separate objectives.

## Challenge

Which downstream tasks tolerate coarse units, and which require distinctions erased during merging?