GMT Router

Overview

gmtrouter is an experimental personalized selector. It uses versioned model intelligence for cold start, then blends that base score with preferences learned from a user's prior model feedback.

What Problem Does It Solve?

Global quality rankings cannot reflect that different users prefer different models. GMT Router starts safely from static evidence, then learns a bounded per-user preference from feedback.

When to Use

Use GMT Router when requests carry a stable user identity, feedback is available, and personalization is more useful than a single global ranking. Prefer a stateless selector when those inputs are absent.

Selection behavior

Before a user reaches min_interactions_for_personalization, candidates are ranked by their available intelligence score. Afterward, the current selector uses:

score = 0.3 * intelligence + 0.7 * user preference

A candidate with no learned preference receives its intelligence score with a small penalty. Missing intelligence uses the selector's neutral cold-start fallback and remains distinct from a measured score of zero.

When a candidate declares reasoning_effort, GMT Router reads only that exact evidence bucket. It does not borrow measurements from another effort. Coverage is never multiplied into intelligence: higher coverage breaks a tie only when the final GMT scores and intelligence scores are equal. Selection diagnostics report the chosen score and coverage, or mark intelligence unavailable.

Configuration

algorithm:
  type: gmtrouter
  gmtrouter:
    enable_personalization: true
    min_interactions_for_personalization: 3
    max_interactions_per_user: 100
    history_sample_size: 5
    embedding_dimension: 768
    num_gnn_layers: 2
    attention_heads: 8
    storage_path: state/gmtrouter.json

Feedback updates bounded per-user interaction history and model preferences. When storage_path is configured, that state is persisted. Query, response, and model-description embeddings contribute only when an embedding function is available.

Limitations

• GMT Router is experimental; validate personalization on representative traffic.
• Requests without a user identity share the anonymous preference state.
• Learned preferences are model-level, not reasoning-effort-specific.
• Static coverage does not describe or weight learned feedback.