LISA: Laplacian In-context Spectral Analysis
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917248658046976 |
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| author | Candanedo, Julio |
| author_facet | Candanedo, Julio |
| contents | We propose Laplacian In-context Spectral Analysis (LISA), a method for inference-time adaptation of Laplacian-based time-series models using only an observed prefix. LISA combines delay-coordinate embeddings and Laplacian spectral learning to produce diffusion-coordinate state representations, together with a frozen nonlinear decoder for one-step prediction. We introduce lightweight latent-space residual adapters based on either Gaussian-process regression or an attention-like Markov operator over context windows. Across forecasting and autoregressive rollout experiments, LISA improves over the frozen baseline and is often most beneficial under changing dynamics. This work links in-context adaptation to nonparametric spectral methods for dynamical systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_04906 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LISA: Laplacian In-context Spectral Analysis Candanedo, Julio Machine Learning We propose Laplacian In-context Spectral Analysis (LISA), a method for inference-time adaptation of Laplacian-based time-series models using only an observed prefix. LISA combines delay-coordinate embeddings and Laplacian spectral learning to produce diffusion-coordinate state representations, together with a frozen nonlinear decoder for one-step prediction. We introduce lightweight latent-space residual adapters based on either Gaussian-process regression or an attention-like Markov operator over context windows. Across forecasting and autoregressive rollout experiments, LISA improves over the frozen baseline and is often most beneficial under changing dynamics. This work links in-context adaptation to nonparametric spectral methods for dynamical systems. |
| title | LISA: Laplacian In-context Spectral Analysis |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2602.04906 |