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Congchao Wang

3 accepted papers

2026

Universal Model Routing for Efficient LLM Inference

ICLR 2026poster

Model routing is a simple technique for reducing the inference cost of large language models (LLMs), wherein one maintains a pool of candidate LLMs, and learns to route each prompt to the smallest feasible LLM. Existing works focus on learning a router for a fixed pool of LLMs. In this paper, we con…

Cited by 0SourceScholar
2025

Gatekeeper: Improving Model Cascades Through Confidence Tuning

NeurIPS 2025poster

Large-scale machine learning models deliver strong performance across a wide range of tasks but come with significant computational and resource constraints. To mitigate these challenges, local smaller models are often deployed alongside larger models, relying on routing and deferral mechanisms to o…

Cited by 0SourceScholar
2019

muSSP: Efficient Min-cost Flow Algorithm for Multi-object Tracking

NeurIPS 2019poster

Min-cost flow has been a widely used paradigm for solving data association problems in multi-object tracking (MOT). However, most existing methods of solving min-cost flow problems in MOT are either direct adoption or slight modifications of generic min-cost flow algorithms, yielding sub-optimal com…