2026.08.17X's Phoenix Ranker: A Transformer That Cannot See Its Own Batch33 min read One sequence, three regions Three stacked bands, one per region of the Phoenix ranking model's input sequence. First, the user prefix at positions 0 to 1: two hashed identifier lookups and two hashed IP lookups, plus country, language, latitude and longitude, gender, age and installed apps; it attends to the user prefix and the history. Second, the history at positions 2 to 1023: post hashes, author hashes, semantic ID, the action taken, dwell time, timezone, local hour, product surface and post age; it attends to the user prefix and the history, bidirectionally rather than causally. Third, the candidates at positions 1024 to 1087: post hashes, author hashes, semantic ID and context, but no action and no dwell, because those are what the model predicts; a candidate attends to the user prefix, the history and itself, and nothing else. Right-anchored rotary position embedding puts the newest history event at position 1023 whatever the history length, and every candidate receives position 1024. A candidate's score therefore depends on the viewer and that candidate only, never on which other candidates share its batch. One sequence, three regions the Phoenix ranker reads 1088 positions in a single non-causal pass user — positions 0…1 two hashed id lookups, two hashed IP lookupscountry · language · lat/lon · gender · age · installed appsattends to: user + history history — positions 2…1023 post hashes · author hashes · semantic ID · action takendwell time · timezone · local hour · surface · post ageattends to: user + history — bidirectional, not causal candidates — positions 1024…1087 post hashes · author hashes · semantic ID · contextno action, no dwell — those are what the model predictsattends to: user + history + itself, and nothing else right-anchored RoPE puts the newest history event at 1023 whatever the history length every candidate gets position 1024 a candidate score depends on the viewer and that candidate alone never on which candidates share its batch
dataoptimal.com6 Steps To Write Any Machine Learning Algorithm From Scratch: Perceptron Case Study#Machine Learning
