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cs.CL2026

From Recognition to Understanding: Unlocking Cognitive Time Series Reasoning with LLMs

Xin Qiu, Junlong Tong, Yao Zhang +3

Time series analysis has recently been coupled with Large Language Models (LLMs) to leverage their reasoning and world knowledge capabilities, yet gains remain limited. We attribut…

cs.CL2026

Behavioral and Representational Evidence of Binomial Ordering Preferences in Large Language Models

Zhiqing Yang, Yilun Liu, Yunpu Ma +2

Large language models (LLMs) can readily reproduce conventional expressions, yet their ability to model gradient frequency distributions remains underexplored. We investigate this…

cs.CL2026

Select to Think: Unlocking SLM Potential with Local Sufficiency

Wenxuan Ye, Yangyang Zhang, Xueli An +2

Small language models (SLMs) offer efficient deployment, yet they often lag behind their larger counterparts (LLMs) in reasoning. Existing remedies either invoke an LLM at points o…

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.CL2026

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.CL2026

Rethinking the Role of LLMs in Time Series Forecasting

Xin Qiu, Junlong Tong, Yirong Sun +3

Large language models (LLMs) have been introduced to time series forecasting (TSF) to incorporate contextual knowledge beyond numerical signals. However, existing studies question…