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The subject line of the email was simple, almost arrogant:
In the final map of the series—Split, XG’s best map—it happened. Zen called for a B execute on a standard pistol round. The predictor said “two in heaven, one back site.” Raze swung.
Lethe was a feedback loop. Every time XG used the predictor, the model ingested that round’s real outcome and updated itself. It grew sharper. But it also left a quantum signature in the server logs—a mismatch between input latency and reaction time. A ghost in the machine. Riot’s anti-cheat couldn’t see the program, but it could see the statistical anomaly: a team whose average reaction time was 80ms faster than human peak, but only on rounds they won .
Kai’s hands trembled. This is why they’re undefeated. Zen wasn’t calling plays. He was reading the predictor’s output through a discreet earpiece. Raze wasn’t reacting; she was pre-firing the pixel where the enemy would be .
The program didn’t look like a cheat. It looked like a neural network overlay—a translucent web of nodes that mapped the server’s tick architecture. Within seconds, it had scraped the past 100 rounds of a random ranked match. Then it did something impossible: it simulated the next 100 rounds, predicting every peek, every utility line-up, every death, with 98.7% accuracy.
Kaelen “Kai” Voss, the head of analytics for Team Susquehanna, stared at the 2.4 GB attachment. The sender was an encrypted relay he didn’t recognize. The file name was a ghost rumor from the pro VALORANT scene—a supposed cheat so sophisticated it didn’t aim. It predicted .
Kai watched from his hotel room, the “XG VALORANT UNDEAD” zip still open on his laptop. He deleted it. Then he wrote a new subject line for Riot’s security team:
