Lapras: Latent Reasoning for Time Series Language Models

Time Series Language Models (TSLMs) offer a promising path toward time series understanding by reasoning over temporal signals and producing natural language answers and explanations. A common approach is Chain-of-Thought (CoT), which generates step-by-step rationales describing relevant signal patterns and connecting them to final answers. Although these models learn from reference CoT traces during post-training, generating faithful descriptions of input time series at inference time remains challenging. The model is required to express high-dimensional and continuous temporal representations in discrete language tokens, which may lead to neglecting task-relevant patterns or describing them inaccurately. Because subsequent reasoning steps build on these descriptions, early errors can propagate through the reasoning chain, ultimately leading to incorrect answers accompanied by plausible explanations that are inconsistent with the input signal. To address this, we propose Lapras (Latent Post-trained Reasoning Across Series), a post-training framework that equips TSLMs with latent reasoning. A model trained with Lapras reasons through a sequence of continuous thoughts in the joint time series-language space, producing text only for the final answer. It learns this through teacher-student self-distillation, where a teacher trained on CoT reference traces reasons explicitly through text. The student is trained to align its hidden states with the teacher's at the answer stage, transferring the teacher's reasoning ability into the student's latent computation. We evaluate Lapras across four TSLM backbones on five diverse time series question answering benchmarks. Lapras improves average F1 by up to 10.79% over explicit CoT while generating 23.9 times fewer tokens. Importantly, the continuous thoughts generated by Lapras can be decoded into readable reasoning traces through standard language decoding, preserving the capability to provide textual explanations. Together, these results highlight Lapras as a promising post-training paradigm for TSLMs that enables efficient and effective reasoning at inference while supporting interpretability.