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"schema_version": 1,
"title": "Latent Laplace Diffusion for Irregular Multivariate Time Series (LLapDiff)",
"emoji": "🌀",
"space_id": "snaykey/repro-llapdiff",
"paper": {
"arxiv_id": "2605.19805",
"openreview_id": "t73XUJvyQr"
},
"tags": [
"icml2026-repro",
"paper-t73XUJvyQr"
],
"updated_at": "2026-07-20T08:30:00+00:00",
"root": {
"slug": "index",
"title": "Latent Laplace Diffusion for Irregular Multivariate Time Series (LLapDiff)",
"file": "pages/index.md",
"children": [
{
"slug": "claim-1-latent-trajectories",
"title": "LLapDiff models irregular targets as low-dimensional latent trajectories, enabling horizon-wide generation without numerical integration over physical time (Section 4.4).",
"file": "pages/claim-1-latent-trajectories/page.md",
"children": []
},
{
"slug": "claim-2-laplace-poles",
"title": "The denoiser parameterizes mean evolution in the Laplace domain with stable complex-conjugate poles for direct evaluation at irregular timestamps (Section 4.2).",
"file": "pages/claim-2-laplace-poles/page.md",
"children": []
},
{
"slug": "claim-3-gap-aware",
"title": "A renewal-averaging analysis maps irregular sampling gaps to effective event-domain poles and motivates gap-aware history conditioning (Section 4.3).",
"file": "pages/claim-3-gap-aware/page.md",
"children": []
},
{
"slug": "claim-4-forecasting",
"title": "Across seven real-world datasets, LLapDiff reports the strongest long-horizon probabilistic forecasting results on the main CRPS/MSE comparison (Table 1).",
"file": "pages/claim-4-forecasting/page.md",
"children": []
},
{
"slug": "claim-5-imputation",
"title": "The same queried latent-trajectory model performs missing-value imputation by querying historical timestamps without retraining (Figure 3).",
"file": "pages/claim-5-imputation/page.md",
"children": []
},
{
"slug": "conclusion",
"title": "Conclusion",
"file": "pages/conclusion/page.md",
"children": []
}
]
}
}
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