RouteProfile: Elucidating the Design Space of LLM Profiles for Routing
| Authors | Jingjun Xu et al. |
| Year | 2026 |
| HF Upvotes | 28 |
| arXiv | 2605.00180 |
| Download | |
| Code | https://github.com/ulab-uiuc/RouteProfile |
Abstract
As the large language model (LLM) ecosystem expands, individual models exhibit varying capabilities across queries, benchmarks, and domains, motivating the development of LLM routing. While prior work has largely focused on router mechanism design, LLM profiles, which capture model capabilities, remain underexplored. In this work, we ask: How does LLM profile design affect routing performance across different routers? Addressing this question helps clarify the role of profiles in routing, disentangle profile design from router design, and enable fairer comparison and more principled development of routing systems. To this end, we view LLM profiling as a structured information integration problem over heterogeneous interaction histories. We develop a general design space of LLM profiles, named RouteProfile, along four key dimensions: organizational form, representation type, aggregation depth, and learning configuration. Through systematic evaluation across three representative routers under both standard and new-LLM generalization settings, we show that: (1) structured profiles consistently outperform flat ones; (2) query-level signals are more reliable than coarse domain-level signals; and (3) generalization to newly introduced models benefits most from structured profiles under trainable configurations. Overall, our work highlights LLM profile design as an important direction for future routing research.
Engineering Breakdown
Plain English
This paper investigates how to design LLM profiles—structured representations of model capabilities—to improve routing systems that dynamically select which LLM to use for a given query. The authors frame LLM profiling as an information integration problem over heterogeneous interaction histories and develop a general design space that decouples profile design from router mechanism design. By systematically exploring different profile configurations across multiple routers, they show that profile design significantly impacts routing performance, enabling fairer comparisons and more principled development of LLM routing systems. The work clarifies that profile quality matters as much as router quality, a finding that has been largely overlooked in prior routing research.
Core Technical Contribution
The core novelty is treating LLM profiling as a structured information integration problem and developing the first systematic design space for profiles independent of router mechanisms. Prior work focused almost entirely on router architecture (how to select models) while treating profiles as a afterthought. This paper decouples these concerns, showing that different profile construction methods—varying in what information they capture, how they aggregate historical performance data, and how they represent model capabilities—produce measurably different routing outcomes. The authors provide a principled framework for exploring profile design dimensions such as granularity (query-level vs. domain-level vs. global), feature engineering (raw metrics vs. learned representations), and aggregation strategies (averaging, percentile-based, weighted).
How It Works
The system pipeline begins with collecting heterogeneous interaction histories—logs of which models succeeded or failed on which queries, performance metrics, domain information, and query characteristics. These raw histories are then processed through a profiling layer that extracts structured capability representations; this layer is parameterized along multiple design dimensions including temporal weighting, domain stratification, and statistical aggregation. The resulting profiles are vectors or structured objects that characterize each model's strengths and weaknesses across different query types and domains. Multiple routers (ranging from simple classifiers to learned routing networks) then consume these profiles as input signals to make routing decisions. Critically, the authors keep the router mechanism fixed while varying only the profile construction, allowing them to isolate profile impact from router design effects. The evaluation measures routing accuracy, latency, and cost across diverse query distributions and benchmarks.
Production Impact
For teams deploying multi-model LLM systems, this work directly impacts how to instrument model selection logic. Instead of building a sophisticated router and hoping good profiles emerge naturally, engineers can now systematically evaluate different profiling strategies upfront—deciding whether to track per-domain performance, query-difficulty percentiles, temporal trends, or combinations thereof. This reduces the feedback cycle: you can prototype several profile configurations offline using historical logs before running expensive A/B tests in production. The practical implication is that adding a new model to your routing pool no longer requires retraining routers; you just re-profile existing models and recompute routing decisions. Trade-offs include the storage overhead for richer profiles (storing performance history across query types and domains rather than just aggregate metrics) and the engineering complexity of maintaining profile freshness as model performance drifts over time. For high-volume systems handling thousands of queries per second, choosing the right profile granularity affects both decision latency and memory consumption.
Limitations and When Not to Use This
The paper focuses on routing among existing models and does not address how profiles should adapt as new models arrive or as model capabilities change over time (cold-start and drift problems remain open). The work assumes access to rich historical interaction data to build profiles; in production scenarios where you're adding a brand-new model with no history, this approach provides limited guidance on initial profiling. The paper also does not deeply explore the interaction between profile design and the underlying router algorithm—it's possible that profile effectiveness is router-specific, meaning optimal profiles for a learned router differ from optimal profiles for a rule-based router, which would limit the generalizability of findings. Finally, the evaluation focuses on standard benchmarks and query distributions; real-world routing scenarios involve distribution shifts, adversarial inputs, and cost heterogeneity (different models have different monetary costs) that may require adaptive profiling mechanisms not covered in this work.
Research Context
This work builds on the growing body of LLM routing research, which emerged from the observation that no single model dominates across all queries and that ensemble-style approaches can improve accuracy and efficiency. It complements recent work on router mechanisms (e.g., learned routers, confidence-based routing) by examining the neglected input side—profile quality. The paper also connects to broader work on model selection in machine learning (e.g., metalearning, algorithm selection) and applies those insights to the LLM setting. This research opens a new direction: instead of optimizing only router architectures, the community should invest equally in profile design, potentially using techniques from feature engineering, transfer learning, and multi-task learning to construct better capability representations. The findings suggest that future work should focus on dynamic profiling strategies that adapt to query distribution shifts and on methods for efficient profile construction when historical data is scarce.
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