Yingqi Fan
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ProactiveLLM introduces a novel framework that enables streaming LLMs to actively decide when to interact with incoming data by leveraging the model's internal states, significantly reducing latency while maintaining quality.
ProactiveLLM introduces a novel framework that enables streaming LLMs to actively decide when to interact with incoming data by leveraging the model's internal states, significantly reducing latency while maintaining quality.
The paper proposes a vision-first formulation to improve cache reuse efficiency and reranking performance in multimodal large language models.
This paper proposes CompRank, a token-efficient reranking framework for large language models that reduces redundant computation and achieves strong reranking performance.
This paper proposes an adaptive streaming reasoning framework that enables models to reason during input streaming and perform final deliberation once the stream is complete.
Papers
AdaSR: Adaptive Streaming Reasoning with Hierarchical Relative Policy Optimization
Junlong Tong, Wenqi Xu, Yingqi Fan, Anhao Zhao +3 more
This paper proposes an adaptive streaming reasoning framework that enables models to reason during input streaming and perform final deliberation once the stream is complete.