scalable-prompt-routing-moe

name	scalable-prompt-routing-moe
title	Scalable Prompt Routing for Frontier LLMs
version	0.0.3
engine	skillxiv-v0.0.3-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2603.19415
keywords	["Prompt Routing","Mixture of Experts","Graph Clustering","Cost Reduction","Multi-Model Inference"]
description	Route queries across frontier models using two-stage system: graph-based task discovery identifies ~332 latent task types via semantic similarity + preference patterns; MoE with task-specific adapters estimates quality for candidate models. Achieves <50% inference cost of strongest single model while exceeding its performance; applies when managing pools of frontier models with narrow capability gaps.

Component ID

Two-stage LLM prompt routing system combining task discovery and quality estimation.

Motivation

Frontier model pools show narrow performance gaps where subtle capability differences determine task suitability. Routing queries to diverse specialized models can achieve better cost-performance than over-relying on a single strongest model. However, discovering latent task structure and estimating per-model quality requires principled methods beyond heuristics.

The Modification

Stage 1: Graph-Based Task Discovery

Discover latent task types by combining semantic similarity of task descriptions with model preference patterns using Rank Biased Overlap scoring.

# Task discovery via graph clustering on semantic space
def discover_task_types(task_descriptions, model_responses, n_tasks=332):
    """
    Identify latent task types by clustering tasks and models simultaneously.
    Uses semantic embeddings of task descriptions and preference agreement patterns.
    """
    # Embed task descriptions
    task_embeddings = encode_descriptions(task_descriptions)

    # Compute semantic similarity between tasks
    semantic_similarity = cosine_similarity(task_embeddings)

    # Compute preference agreement: which models agree on relative rankings
    model_preferences = []
    for model in available_models:
        rankings = rank_responses(model_responses[model])
        model_preferences.append(rankings)

    # Fuse semantic + preference signals via graph clustering
    combined_graph = semantic_similarity * agreement_matrix(model_preferences)
    task_clusters = spectral_clustering(combined_graph, n_clusters=n_tasks)

    return task_clusters

Stage 2: Task-Specific Quality Estimation

A mixture-of-experts architecture with task-specific prediction heads provides specialized quality estimates alongside general adapters.

# MoE quality estimation with task-specific routing
class TaskAwareQualityMoE:
    """
    Predicts per-model quality for a given task using task-specific adapters.
    Reduces model consideration to ~32% per task (from full 11 models).
    """
    def __init__(self, n_tasks=332, n_models=11):
        self.task_classifier = TaskClassifier(n_tasks)
        self.general_adapters = nn.ModuleList([
            Adapter() for _ in range(n_models)
        ])
        self.task_specific_heads = nn.ModuleList([
            nn.ModuleList([Adapter() for _ in range(n_models)])
            for _ in range(n_tasks)
        ])

    def estimate_quality(self, query_embedding):
        """
        Single forward pass: classify task, activate task-specific heads.
        Maintains constant effective model size via adaptive activation.
        """
        task_id = self.task_classifier(query_embedding)

        # General quality scores across all models
        general_scores = [adapter(query_embedding) for adapter in self.general_adapters]

        # Task-specific refinements
        task_specific_scores = [
            self.task_specific_heads[task_id][i](query_embedding)
            for i in range(len(self.general_adapters))
        ]

        # Combine: task-specific provides calibration for task
        final_scores = general_scores + task_specific_scores
        return final_scores

Ablation Results

Cost-Performance Tradeoff:

• FineRouter performance: Exceeds Claude-Sonnet-4.5 baseline on 10 benchmarks
• Cost: <50% of single strongest model inference cost
• Routing distribution (example): Claude-Sonnet-4.5 (28%), DeepSeek-R1 (27%), Llama-4-Maverick (23%), Qwen3-235B (13%)

Task Discovery:

• Discovers ~332 fine-grained task types from training data
• Reduces model consideration pool: ~32% of models per task (from full pool of 11)
• Graph clustering identifies meaningful task boundaries beyond semantic similarity alone

Efficiency:

• Single forward pass through task classifier; adaptive head activation
• Constant effective model size maintained across routing decisions
• No per-model batching overhead

Conditions

• Model pool: Works with frontier models showing narrow capability gaps (e.g., Claude-Sonnet, DeepSeek-R1, Llama-4, Qwen3)
• Task discovery requirements: Access to responses from all candidate models on training data for preference agreement estimation
• Task pool size: ~300–400 latent tasks typical; scales with training data diversity
• Training data: Balanced coverage of task types to avoid underrepresented clusters
• Inference constraints: Single forward pass required; compatible with streaming/batched deployment

Drop-In Checklist

• Collect responses from all candidate models on diverse training queries
• Run graph clustering on task embeddings + preference agreement matrix
• Build task classifier (encoder + classifier head)
• Create MoE with general adapters for each model + task-specific heads
• Validate discovered task types: inspect clusters for semantic coherence
• Benchmark cost vs. single-model baseline—target <50% relative cost
• Measure performance vs. single strongest model—target >= performance
• Profile latency: confirm single forward pass enables streaming
• Calibrate routing distribution across your model pool