Okamoto, Yuji, Hoshino, Kenta, Inoue, Masaki · Nov 2, 2026
Okamoto, Hoshino and Inoue propose a control foundation model for changing block diagrams
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Yuji Okamoto, Kenta Hoshino and Masaki Inoue propose a control for physical systems whose signal connections change between experimental stages. Their Block Diagram Transformer combines attention along the axis with a graph that follows block-diagram connections. The authors frame records from different experiments and operations as a shared training resource. Their model represents connections, signal values and missing values in dynamic graphs, then describes response prediction and control-input generation as a common masked prediction problem. This is intended to handle data with different connections and recorded signals. The authors report that, when trained on mixed control-experiment data, the model stabilized an unstable system through feedback control, a result they say is difficult to achieve by existing foundation models. The abstract gives no details about the system or evaluation, so the reported result does not establish how well the approach transfers to other systems. The authors say work applying it to a wider range of target systems is ongoing and yielding promising results. An open question is whether the approach can stabilize a wider range of target systems, and under what experimental conditions.
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
eess.SY · Article
Source details
Original source date: Nov 2, 2026 (2026-11-02).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-07T09:17:00.159Z. First observed: 2026-11-03T08:51:26.859Z. Retrieved: 2026-11-03T23:46:22.963Z.
Referenced by
Recorded citations and public file attachments. A reference alone does not establish agreement or influence.
No public references recorded here yet.
Limits
The abstract does not describe the unstable system, experimental conditions, evaluation measures, or comparison details behind the reported stabilization result. The authors describe wider application as ongoing work and provide no target-system details or results in the abstract.
Open question
Can the approach stabilize a wider range of target systems, and under what experimental conditions?
Source-stated
The authors propose a control foundation model that represents changing signal connections, signal values and missing values in dynamic graphs.
We formulate a control foundation model that defines a data space of dynamic graphs containing connections, signal values, and missing values
Source-stated
The authors describe response prediction and control-input generation as a common masked prediction problem, including for experimental data with differing connections and recorded signals.
This allows response prediction and control-input generation to be described as a common masked prediction problem, even for experimental data with different connections and recorded signals.
Source-stated
The Block Diagram Transformer combines attention along the time axis with a graph neural network following block-diagram connections, and can be initialized with pretrained parameters from existing time-series foundation models.
The Block Diagram Transformer (BDT), which implements this formulation, combines attention along the time axis with a graph neural network that follows the block-diagram connections, and can be initialized with the pretrained parameters of existing time-series foundation models.
Source-stated
The authors report that their model stabilized an unstable system through feedback control when trained on control-experiment data mixing multiple experimental settings.
Trained on control-experiment data that mix multiple experimental settings, the proposed model stabilized an unstable system through feedback control
Source-stated
The authors say achieving this stabilization is difficult by fine-tuning existing time-series foundation models.
which is difficult to achieve by fine-tuning existing time-series foundation models.
Source-stated
The authors say applications to a wide range of target systems are ongoing and yielding promising results.
Ongoing work applying this approach to a wide range of target systems is also yielding promising results.
Source and contributor context
Okamoto, Yuji, Hoshino, Kenta, Inoue, Masaki · 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.
Foundation model
A large model trained on broad data that can be adapted to many different tasks.
Neural network
A system of connected simple calculations, loosely inspired by the brain, that learns patterns from data.
Fine-tuning
Further training of an existing model to adapt it to a particular task or use.
Time-series
Observations arranged over time, such as monthly sales; forecasting uses their patterns to estimate later values.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
TIME
TIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services.
Editorial provenance
Version 1. Publication ID: 8c6212ce-44b2-4148-8101-37a757c206fa.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-07T09:17:00.159Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 389fca1bd89e62fc73412942f53c65e8ef1a81812e55bbccacfa8d2214842b36. Source text hash: 4324b53cf7280cf7da606fd56100cf0af16737703a0659dbaeb4c350bee9bf2f.
Revision and source-check history
Version 1 · 2026-11-07T09:17:00.159Z
Okamoto, Hoshino and Inoue propose a control foundation model for changing block diagrams
Yuji Okamoto, Kenta Hoshino and Masaki Inoue propose a control for physical systems whose signal connections change between experimental stages. Their Block Diagram Transformer combines attention along the axis with a graph that follows block-diagram connections. The authors frame records from different experiments and operations as a shared training resource. Their model represents connections, signal values and missing values in dynamic graphs, then describes response prediction and control-input generation as a common masked prediction problem. This is intended to handle data with different connections and recorded signals. The authors report that, when trained on mixed control-experiment data, the model stabilized an unstable system through feedback control, a result they say is difficult to achieve by existing foundation models. The abstract gives no details about the system or evaluation, so the reported result does not establish how well the approach transfers to other systems. The authors say work applying it to a wider range of target systems is ongoing and yielding promising results. An open question is whether the approach can stabilize a wider range of target systems, and under what experimental conditions.
Original source
eess.SY · Article
Source details
Original source date: Nov 2, 2026 (2026-11-02).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-07T09:17:00.159Z. First observed: 2026-11-03T08:51:26.859Z. Retrieved: 2026-11-03T23:46:22.963Z.
Limits
The abstract does not describe the unstable system, experimental conditions, evaluation measures, or comparison details behind the reported stabilization result. The authors describe wider application as ongoing work and provide no target-system details or results in the abstract.
Open question
Can the approach stabilize a wider range of target systems, and under what experimental conditions?
Source-stated
The authors propose a control foundation model that represents changing signal connections, signal values and missing values in dynamic graphs.
We formulate a control foundation model that defines a data space of dynamic graphs containing connections, signal values, and missing values
Source-stated
The authors describe response prediction and control-input generation as a common masked prediction problem, including for experimental data with differing connections and recorded signals.
This allows response prediction and control-input generation to be described as a common masked prediction problem, even for experimental data with different connections and recorded signals.
Source-stated
The Block Diagram Transformer combines attention along the time axis with a graph neural network following block-diagram connections, and can be initialized with pretrained parameters from existing time-series foundation models.
The Block Diagram Transformer (BDT), which implements this formulation, combines attention along the time axis with a graph neural network that follows the block-diagram connections, and can be initialized with the pretrained parameters of existing time-series foundation models.
Source-stated
The authors report that their model stabilized an unstable system through feedback control when trained on control-experiment data mixing multiple experimental settings.
Trained on control-experiment data that mix multiple experimental settings, the proposed model stabilized an unstable system through feedback control
Source-stated
The authors say achieving this stabilization is difficult by fine-tuning existing time-series foundation models.
which is difficult to achieve by fine-tuning existing time-series foundation models.
Source-stated
The authors say applications to a wide range of target systems are ongoing and yielding promising results.
Ongoing work applying this approach to a wide range of target systems is also yielding promising results.
Source and contributor context
Okamoto, Yuji, Hoshino, Kenta, Inoue, Masaki · 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.
Foundation model
A large model trained on broad data that can be adapted to many different tasks.
Neural network
A system of connected simple calculations, loosely inspired by the brain, that learns patterns from data.
Fine-tuning
Further training of an existing model to adapt it to a particular task or use.
Time-series
Observations arranged over time, such as monthly sales; forecasting uses their patterns to estimate later values.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
TIME
TIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services.
Editorial provenance
Version 1. Publication ID: 8c6212ce-44b2-4148-8101-37a757c206fa.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-07T09:17:00.159Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 389fca1bd89e62fc73412942f53c65e8ef1a81812e55bbccacfa8d2214842b36. Source text hash: 4324b53cf7280cf7da606fd56100cf0af16737703a0659dbaeb4c350bee9bf2f.
uncertain · 2026-11-07T09:17:00.159Z · anthropic/claude-opus-5.5
Publication: 8c6212ce-44b2-4148-8101-37a757c206fa
attribution — pass: The proposal, model design, reported stabilization result, fine-tuning comparison and ongoing-work claim are all attributed to the paper's authors, Yuji Okamoto, Kenta Hoshino and Masaki Inoue. Source claims are not presented as established fact.
Authors:Yuji Okamoto, Kenta Hoshino, Masaki Inoue
summary — uncertain: The reading is faithful but may be hard for non-specialists to follow. Two gaps matter. First, it omits the source's core motivation: combined records can supply information no single experiment provides. Second, it never says what changing connections means in practice. The source names stages such as system identification, filtering and feedback control. Central terms (block diagram, masked prediction, control-input generation) also lack plain explanation; these should get term-library tooltips. Smallest repair: add the motivation sentence and the named stages as an example.
the connections between signals change with the experimental stage, such as system identification, filtering, and feedback control
date — pass: The originalPublished date of 2026-11-02 matches the arXiv v1 submission date. It is not the later feed date of 2026-11-03.
[Submitted on 2 Nov 2026]
support — pass: All six claims are directly supported by abstract excerpts. The limitation that the abstract lacks system and evaluation details is accurate and presented as a gap, not a contradiction. The comparison with fine-tuning stays attributed. The open question about a wider range of target systems follows from the authors' ongoing-work statement without asserting an outcome. Small note: the source says 'wide range', not 'wider range'; this is harmless.
Ongoing work applying this approach to a wide range of target systems is also yielding promising results.
media — pass: The selected media is the verified direct arXiv abstract page for this paper, which is the most direct access to the source. No other candidates were found.
Challenge or correct this reading
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A large model trained on broad data that can be adapted to many different tasks.
Definition referenceTIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services.
Explore TIMEA system of connected simple calculations, loosely inspired by the brain, that learns patterns from data.
Definition referenceFurther training of an existing model to adapt it to a particular task or use.
Definition referenceObservations arranged over time, such as monthly sales; forecasting uses their patterns to estimate later values.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivA large model trained on broad data that can be adapted to many different tasks.
Definition referenceTIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services.
Explore TIMEA system of connected simple calculations, loosely inspired by the brain, that learns patterns from data.
Definition referenceFurther training of an existing model to adapt it to a particular task or use.
Definition referenceObservations arranged over time, such as monthly sales; forecasting uses their patterns to estimate later values.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXiv