TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning
- Published
- Source
- arXiv
- Paper number
- 598
- Field
- Machine Learning
- arXiv ID
- 2607.08940
Key points
- It is the first modality plus model co-routing framework that uses the complementarity of LLMs for numerical accuracy and VLMs for global patterns.
- It models complex interactions with a heterogeneous graph over task, query, modality, and model and uses a GNN.
- On four time-series reasoning tasks, it achieves 16% to 46% relative improvements and builds a Pareto frontier with cost-aware optimization.
- It greatly outperforms the best baseline even when using only 10% of the training data, showing data efficiency.
- GPT-5 is used for node profiling and initialization from pretrained embeddings, enabling zero-shot generalization to new models and tasks.
- The user-defined alpha parameter allows real-time control of the performance-cost tradeoff.
Paper links
External research summaries. These are not HDATF publications or measured product results.