Lapras: Latent Reasoning for Time Series Language Models
Quick Answer
Lapras introduces a novel post-training framework for Time Series Language Models (TSLMs), enhancing reasoning capabilities by 10.79% on five benchmarks while reducing token generation by 23.9x.
Quick Take
This approach utilizes teacher-student self-distillation to improve the accuracy of temporal reasoning without compromising interpretability.
Key Points
- Lapras improves average F1 score by up to 10.79% over explicit Chain-of-Thought.
- The model generates 23.9x fewer tokens while maintaining reasoning quality.
- It employs teacher-student self-distillation for enhanced latent reasoning.
- Evaluated across four TSLM backbones on five time series question answering benchmarks.
- Continuous thoughts can be decoded into readable reasoning traces.
DeepSignal Analysis
What happened
Lapras is a new post-training framework designed for Time Series Language Models (TSLMs). It enhances reasoning capabilities by 10.79% on five benchmarks while significantly reducing token generation by 23.9 times. The framework employs teacher-student self-distillation to improve temporal reasoning accuracy.
Key evidence
- Lapras improves average F1 scores by up to 10.79% over explicit Chain-of-Thought methods across five time series question answering benchmarks.
- The framework reduces token generation by 23.9 times, indicating a more efficient processing method for TSLMs.
- Lapras utilizes teacher-student self-distillation, where a teacher model trained on reference traces guides a student model in reasoning.
Why it matters
The advancements made by Lapras could lead to more efficient and interpretable TSLMs, which are crucial for applications requiring temporal reasoning. By reducing the number of tokens generated, it may also lower computational costs and improve response times. This framework could set a new standard for how TSLMs are trained and utilized in various domains.
What to watch
Paper Resources
📖 Reader Mode
~2 min readAuthors:Yuliang Chen, Yu Yvonne Wu, Patrick Langer, Arvind Pillai, Sudarshan Regmi, Martin Maritsch, Juncheng Liu, Robert Jakob, Thomas Kaar, Tess Z. Griffin, Lisa Marsch, Michael V. Heinz, Nicholas C. Jacobson, Andrew Campbell
Abstract: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 linking relevant signal patterns to final answers. Although these models learn from reference CoT traces during post-training, generating faithful descriptions of input time series at inference remains challenging. Expressing high-dimensional, continuous temporal representations in discrete language tokens may cause the model to neglect task-relevant patterns or describe them inaccurately. Because later reasoning steps build on these descriptions, early errors propagate, leading to incorrect answers with plausible explanations that are inconsistent with the input signal. 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 aligns its hidden states with the teacher's at the answer stage, transferring the teacher's reasoning ability into its latent computation. We evaluate Lapras across four TSLM backbones on five time series question answering benchmarks. Lapras improves average F1 by up to 10.79% over explicit CoT while generating 23.9x fewer tokens. Lapras's continuous thoughts can also be decoded into readable reasoning traces via standard language decoding, preserving textual explanations. Together, these results highlight Lapras as a promising post-training paradigm for efficient, effective, and interpretable TSLM reasoning.
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.11111 [cs.CL] |
| (or arXiv:2610.11111v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11111 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yuliang Chen [view email]
[v1]
Thu, 8 Oct 2026 02:33:37 UTC (3,702 KB)
— Originally published at arxiv.org
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