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Shaoqiang Lu

2 accepted papers

2026

Mixture-of-Trees: Learning to Select and Weigh Reasoning Paths for Efficient LLM Inference

AAAI 2026technical

We introduce Mixture-of-Trees (MoT), a novel framework that integrates sparse expert activation with structured tree-based reasoning for efficient LLM inference. MoT employs a learned gating mechanism to selectively activate only the most relevant expert reasoning trees for each problem, where exper

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2026

Topological Active Inference for Task Disambiguation

ICML 2026poster

In open-ended domains, natural language instructions are often *underspecified*, mapping to multiple valid yet functionally distinct latent intents. While Large Language Models (LLMs) excel at generation, their ability to resolve such *task ambiguity* through interaction is currently hampered by *se…

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