Token Compression, achieving more with less (edgee.ai)

very interesting

From a theoretical perspective, token compression is about removing low-utility tokens while retaining enough structure to trigger the desired model behavior. Prompt compression and embedding compression each operate along a precision–efficiency spectrum, and the balance of semantic retention vs. token reduction is what makes it effective. Because API billing scales with tokens processed, this balance is directly tied to cost optimization