AI Research
TimeThink: Eliciting Compositional Reasoning in Timeseries Large Language Models
Advancing temporal reasoning through explicit step-by-step prompt strategies in multimodal models
Timeseries multimodal large language models (TS-MLLMs) have recently begun leveraging the reasoning capabilities of large language models (LLMs) for question-answering tasks. However, these models often fail to capture dynamic temporal patterns, providing only implicit reasoning ...
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ArXiv CS.AI
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ArXiv CS.AI
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TimeThink improves the ability of multimodal models to parse complex temporal dynamics by explicitly prompting for step-by-step reasoning. This advancement bridges a critical gap in time-series analysis where traditional models previously struggled to interpret fluctuating data patterns accurately.
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