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Introduces Coincidex, an open-source continual learning framework that uses dynamic task-similarity routing to avoid replay buffers, with benchmark results and identified failure modes.
Introduces TSRouter, a graph-based dynamic routing framework that selects the optimal modality (LLM or VLM) and model for time-series reasoning tasks, achieving 16-46% relative improvement over baselines and demonstrating zero-shot generalization.
This paper proposes a reward-density heuristic for dynamic multi-vehicle routing problems that matches the solution quality of metaheuristics like ALNS, GA, and SA while requiring two to three orders of magnitude less planning time, demonstrated on drone task allocation and urban taxi dispatch scenarios.
This paper proposes the Agent-as-a-Router framework, which transforms model routing into a dynamic, iterative process. Based on task type and real-time execution feedback, it selects the most suitable LLM to improve coding performance and cost efficiency.
A new method called Zero-Expert Self-Distillation Adaptation (ZEDA) allows MoE models like Qwen3 and GLM to skip half their expert computations on easy tokens with minimal accuracy loss, achieving ~20% inference speedup by adding dummy experts that output nothing.
HyDRA is a hybrid dynamic routing architecture for heterogeneous LLM pools that predicts fine-grained capability requirements per query and selects the cheapest capable model via shortfall matching, achieving up to 72.5% cost savings with quality maintained. It is deployed in GitHub Copilot's VS Code Chat auto-mode and decouples routing from model catalog, requiring no retraining when models change.
BEAM introduces binary expert activation masking for dynamic routing in Mixture-of-Experts LLMs, achieving up to 85% FLOPs reduction with minimal performance loss and up to 2.5× faster decoding.