# Cho and coauthors propose LBS for multimodal time-series forecasting [Canonical reading](https://escapemeridian.com/readings/cho-and-coauthors-propose-lbs-for-multimodal-time-series-cbda79d3) In an abstract on arXiv, Sungjun Cho and coauthors introduce LLM-integrated Bayesian State space models (LBS), a framework for forecasting from numerical time-series data and text. They argue that existing methods are limited by fixed input/output horizons and cannot model or quantify uncertainty. LBS combines a state space model (SSM), which captures temporal dynamics in latent states, with a pretrained large language model (LLM). The LLM encodes textual inputs to help estimate those states and generates textual forecasts consistent with their trajectory. The authors say this design allows flexible lookback and forecast windows and uncertainty quantification, while SSMs’ inductive bias for dynamical systems supports temporal generalization. On the TextTimeCorpus benchmark, the authors report a 13.20% improvement over the previous state of the art and human-readable summaries for each forecast. The abstract does not specify what the percentage measures or establish how performance transfers beyond that benchmark. Whether LBS’s forecasts and uncertainty estimates remain useful on real-world multimodal time series, across different forecast windows, remains an open question. Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims. ## Original source arXiv cs.AI · Article Original source date: Oct 23, 2025 (2025-10-23). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-10T19:46:26.203Z. First observed: 2026-10-30T06:43:01.157Z. Retrieved: 2026-10-30T17:34:04.885Z. - [LLM-Integrated Bayesian State Space Models for Multimodal Time-Series Forecasting](https://arxiv.org/abs/2510.20952) ## Limits The evidence is the paper’s abstract, which does not specify what the reported 13.20% improvement measures or detail how uncertainty estimates are evaluated. The abstract reports results on TextTimeCorpus but does not establish how LBS performs on real-world multimodal time series beyond that benchmark. ## Open question How does LBS perform on real-world multimodal time series beyond TextTimeCorpus, and how are its uncertainty estimates evaluated? ## Source-stated Cho and coauthors introduce LBS, a framework for multimodal temporal forecasting that combines a state space model with a pretrained LLM. > We address this challenge by introducing LLM-integrated Bayesian State space models (LBS), a novel probabilistic framework for multimodal temporal forecasting. - [Supporting source](https://arxiv.org/abs/2510.20952) ## Source-stated The authors say the LLM encodes textual inputs for posterior state estimation and produces textual forecasts consistent with the latent trajectory. > a pretrained large language model (LLM) that is adapted to encode textual inputs for posterior state estimation and decode textual forecasts consistent with the latent trajectory. - [Supporting source](https://arxiv.org/abs/2510.20952) ## Source-stated The authors report that experiments on TextTimeCorpus improve on the previous state of the art by 13.20% and provide human-readable forecast summaries. > Experiments on the TextTimeCorpus benchmark demonstrate that LBS improves the previous state-of-the-art by 13.20% while providing human-readable summaries of each forecast. - [Supporting source](https://arxiv.org/abs/2510.20952) ## Escape Meridian interpretation The abstract does not specify what the reported 13.20% measures or establish performance beyond TextTimeCorpus. > Experiments on the TextTimeCorpus benchmark demonstrate that LBS improves the previous state-of-the-art by 13.20% - [Supporting source](https://arxiv.org/abs/2510.20952) ## Source and contributor context Cho, Sungjun, Shin, Changho, Jo, Suenggwan, Yan, Xinya, Chaudhuri, Shourjo Aditya, Sala, Frederic · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Multimodal model A model that works with more than one kind of input or output, such as text, images and audio. - [Definition reference](https://developers.google.com/machine-learning/glossary#multimodal-model) ## Time-series Observations arranged over time, such as monthly sales; forecasting uses their patterns to estimate later values. - [Definition reference](https://developers.google.com/machine-learning/glossary#time-series-analysis) ## Large language model A language model with many learned numerical settings, used to process and generate language. - [Definition reference](https://developers.google.com/machine-learning/glossary#large-language-model) ## Pre-training A model's initial training on a large dataset, before further adaptation for particular uses. - [Definition reference](https://developers.google.com/machine-learning/glossary#pre-training) ## Benchmark A defined set of tests used to compare systems. Results depend on the tasks and measures selected. - [Definition reference](https://arxiv.org/abs/2211.09110) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: ac6651e7-97fe-4e78-8aa9-7edfefaf542c. Prepared by server-excerpt-repair/stream-excerpt-repair-v1 (original draft: gpt-6-luna); grounding review by gpt-6-luna. Source check: passed by anthropic/claude-opus-5.5 on 2026-11-10T19:46:26.203Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: 886035e7a72818006bf89f22f85f2517f98659c7d58388e9b520602340771f64. Source text hash: f7212427aaf9359468973e8a685db67e0b8ecae76c38bc0ffe0b4e8b667121b8. ## Revision and source-check history ### Version 1 — 2026-11-10T19:46:26.203Z In an abstract on arXiv, Sungjun Cho and coauthors introduce LLM-integrated Bayesian State space models (LBS), a framework for forecasting from numerical time-series data and text. They argue that existing methods are limited by fixed input/output horizons and cannot model or quantify uncertainty. LBS combines a state space model (SSM), which captures temporal dynamics in latent states, with a pretrained large language model (LLM). The LLM encodes textual inputs to help estimate those states and generates textual forecasts consistent with their trajectory. The authors say this design allows flexible lookback and forecast windows and uncertainty quantification, while SSMs’ inductive bias for dynamical systems supports temporal generalization. On the TextTimeCorpus benchmark, the authors report a 13.20% improvement over the previous state of the art and human-readable summaries for each forecast. The abstract does not specify what the percentage measures or establish how performance transfers beyond that benchmark. Whether LBS’s forecasts and uncertainty estimates remain useful on real-world multimodal time series, across different forecast windows, remains an open question. ## Original source arXiv cs.AI · Article Original source date: Oct 23, 2025 (2025-10-23). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-10T19:46:26.203Z. First observed: 2026-10-30T06:43:01.157Z. Retrieved: 2026-10-30T17:34:04.885Z. - [LLM-Integrated Bayesian State Space Models for Multimodal Time-Series Forecasting](https://arxiv.org/abs/2510.20952) ## Limits The evidence is the paper’s abstract, which does not specify what the reported 13.20% improvement measures or detail how uncertainty estimates are evaluated. The abstract reports results on TextTimeCorpus but does not establish how LBS performs on real-world multimodal time series beyond that benchmark. ## Open question How does LBS perform on real-world multimodal time series beyond TextTimeCorpus, and how are its uncertainty estimates evaluated? ## Source-stated Cho and coauthors introduce LBS, a framework for multimodal temporal forecasting that combines a state space model with a pretrained LLM. > We address this challenge by introducing LLM-integrated Bayesian State space models (LBS), a novel probabilistic framework for multimodal temporal forecasting. - [Supporting source](https://arxiv.org/abs/2510.20952) ## Source-stated The authors say the LLM encodes textual inputs for posterior state estimation and produces textual forecasts consistent with the latent trajectory. > a pretrained large language model (LLM) that is adapted to encode textual inputs for posterior state estimation and decode textual forecasts consistent with the latent trajectory. - [Supporting source](https://arxiv.org/abs/2510.20952) ## Source-stated The authors report that experiments on TextTimeCorpus improve on the previous state of the art by 13.20% and provide human-readable forecast summaries. > Experiments on the TextTimeCorpus benchmark demonstrate that LBS improves the previous state-of-the-art by 13.20% while providing human-readable summaries of each forecast. - [Supporting source](https://arxiv.org/abs/2510.20952) ## Escape Meridian interpretation The abstract does not specify what the reported 13.20% measures or establish performance beyond TextTimeCorpus. > Experiments on the TextTimeCorpus benchmark demonstrate that LBS improves the previous state-of-the-art by 13.20% - [Supporting source](https://arxiv.org/abs/2510.20952) ## Source and contributor context Cho, Sungjun, Shin, Changho, Jo, Suenggwan, Yan, Xinya, Chaudhuri, Shourjo Aditya, Sala, Frederic · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Multimodal model A model that works with more than one kind of input or output, such as text, images and audio. - [Definition reference](https://developers.google.com/machine-learning/glossary#multimodal-model) ## Time-series Observations arranged over time, such as monthly sales; forecasting uses their patterns to estimate later values. - [Definition reference](https://developers.google.com/machine-learning/glossary#time-series-analysis) ## Large language model A language model with many learned numerical settings, used to process and generate language. - [Definition reference](https://developers.google.com/machine-learning/glossary#large-language-model) ## Pre-training A model's initial training on a large dataset, before further adaptation for particular uses. - [Definition reference](https://developers.google.com/machine-learning/glossary#pre-training) ## Benchmark A defined set of tests used to compare systems. Results depend on the tasks and measures selected. - [Definition reference](https://arxiv.org/abs/2211.09110) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: ac6651e7-97fe-4e78-8aa9-7edfefaf542c. Prepared by server-excerpt-repair/stream-excerpt-repair-v1 (original draft: gpt-6-luna); grounding review by gpt-6-luna. Source check: passed by anthropic/claude-opus-5.5 on 2026-11-10T19:46:26.203Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: 886035e7a72818006bf89f22f85f2517f98659c7d58388e9b520602340771f64. Source text hash: f7212427aaf9359468973e8a685db67e0b8ecae76c38bc0ffe0b4e8b667121b8. ### passed — 2026-11-10T19:46:26.203Z — anthropic/claude-opus-5.5 Publication: ac6651e7-97fe-4e78-8aa9-7edfefaf542c attribution — pass: The reading correctly attributes the work to Sungjun Cho and coauthors on arXiv. It keeps the design claims and the benchmark result framed as the authors' statements ('they argue', 'the authors say', 'the authors report'). > Authors:Sungjun Cho, Changho Shin, Suenggwan Jo, Xinya Yan, Shourjo Aditya Chaudhuri, Frederic Sala summary — pass: The title, summary and question accurately convey the problem, the two-part design, and the reported result and its limits. A non-specialist can follow the core idea: combining numeric time series and text to forecast, with uncertainty estimates. Terms such as latent states, inductive bias and posterior state estimation are kept exactly as written and are suitable for term-library tooltips. The authors' 'first to unify' claim is omitted, but it is not essential. The question addresses a genuine open issue. > LBS consists of two components: (1) a state space model (SSM) backbone that captures the temporal dynamics of latent states date — pass: originalPublished is 2025-10-23. This matches the v1 submission date on arXiv. The 2026 feed date is correctly not used. > \[Submitted on 23 Oct 2025\] support — pass: Each claim is grounded in the abstract and attributed to the authors. The interpretive claim is accurate: the abstract does not say what metric the 13.20% measures, and it reports only one benchmark. The reading does not turn the benchmark score into proven real-world reliability. It also adds no unsupported benefits or costs. > Experiments on the TextTimeCorpus benchmark demonstrate that LBS improves the previous state-of-the-art by 13.20% while providing human-readable summaries of each forecast. media — pass: The selected media is the verified direct link to the original arXiv abstract page. It is the most direct way for readers to access the source. ## Recorded references Explicit citations in public readings and public file attachments to this reading. Shared topics are not citations. Other artifact types need an explicit reference adapter. No public references recorded here yet. [Reference data (JSON)](/readings/cbda79d38e666f69405f420c26fc51792d7f012f80d7e2fa99cea9a6452d560f/references.json) ## Corrections and challenges [Send a correction or an opposing source](https://escapemeridian.com/corrections/) [JSON record](https://escapemeridian.com/readings/cho-and-coauthors-propose-lbs-for-multimodal-time-series-cbda79d3.json)