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Junhong Qian

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

Cited by 0SourcePDFScholar
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…

Cited by 0SourceScholar