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cs.CL2026

AdaSR: Adaptive Streaming Reasoning with Hierarchical Relative Policy Optimization

Junlong Tong, Wenqi Xu, Yingqi Fan +4

Large reasoning models typically follow a read-then-think paradigm: they observe the complete input, reason over a static context, and then produce the answer. Yet many real-world…

cs.CL2026

ProactiveLLM: Learning Active Interaction for Streaming Large Language Models

Junlong Tong, Yao Zhang, Anhao Zhao +3

Standard Large Language Models (LLMs) follow a read-then-generate paradigm, causing unnecessary latency and computation. Streaming LLMs alleviate this issue by generating while rec…

cs.CL2025

StreamingThinker: Large Language Models Can Think While Reading

Junlong Tong, Yingqi Fan, Anhao Zhao +2

Large language models (LLMs) have demonstrated remarkable capabilities in chain of thought (CoT) reasoning. However, the current LLM reasoning paradigm initiates thinking only afte…

cs.CL2025

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling

Anhao Zhao, Fanghua Ye, Yingqi Fan +4

Large language models (LLMs) achieve remarkable performance across tasks but incur substantial computational costs due to their deep, multi-layered architectures. Layer pruning has…

cs.CL2025

LLM as Effective Streaming Processor: Bridging Streaming-Batch Mismatches with Group Position Encoding

Junlong Tong, Jinlan Fu, Zixuan Lin +4

Large Language Models (LLMs) are primarily designed for batch processing. Existing methods for adapting LLMs to streaming rely either on expensive re-encoding or specialized archit…