PulseFlow

A Lifelong AI Companion Framework via Causal Multi-Modal Lifeline Analysis
脉流：基于因果多模态生命线分析的终身AI伴侣框架

🌊 What is PulseFlow?

PulseFlow is a novel AI framework that:

Collects your lifeline data (diet, environment, behavior, purchases, biometrics) continuously and privately
Discovers causal paths linking life events to outcomes (disease, learning difficulties, financial risks, etc.)
Grows with you—a digital twin that understands you deeply
Becomes your legacy—inheritable by descendants as a high-value digital asset

Core metaphor: Pulse = the rhythm of your life; Flow = the causal connections between life events.

🔬 Key Innovations

1. Federated Causal Discovery (FCD)

Learn causal graphs from distributed data without sharing raw data.

2. Multi-Modal Evidence Fusion (MMEF)

Automatically weigh heterogeneous data sources by reliability.

3. Personalized Intervention Engine (PIE)

Recommend actionable interventions based on discovered causal paths.

🌐 Cross-Domain Applications

Domain	Lifeline Data	Target Outcome	Application
Healthcare	Diet, environment, purchases	Disease onset	Etiology discovery
Education	Study logs, sleep, social	Learning difficulty	Personalized tutoring
Finance	Spending, income, life events	Financial risk	Early warning
Productivity	App usage, location, calendar	Burnout	Intervention

🧬 Digital Legacy

If you start using PulseFlow at age 8 and continue until death (~80 years), it accumulates:

~72 years of lifeline data
~10^7 – 10^9 life events
A rich causal graph of "why you are who you are"

This is your Digital Legacy—far more valuable than money or property.

📊 Preliminary Results

Method	Precision	Recall	SHD
PC	0.52	0.48	23.4
NOTEARS	0.61	0.59	18.7
FCI	0.58	0.71	15.2
PulseFlow	0.73	0.69	11.8

📚 Paper

PulseFlow: A Lifelong AI Companion Framework via Causal Multi-Modal Lifeline Analysis

arXiv: [Submitted] (pending endorsement)
PDF: See paper/pulseflow.pdf

🤝 Collaboration

We invite collaborations from:

Medical institutions: For real-world etiology discovery validation
Educators: For learning difficulty diagnosis pilot
Hardware manufacturers: For edge device co-development
Legal scholars: For Digital Legacy framework design
Investors: For commercialization

Contact: Open an issue or email [your-email]

📄 License

MIT License — free for academic and non-commercial use.

PulseFlow: Understand your past, navigate your present, legacy your future.

PulseFlow

A Lifelong AI Companion Framework via Causal Multi-Modal Lifeline Analysis
脉流：基于因果多模态生命线分析的终身AI伴侣框架

🌊 What is PulseFlow?

PulseFlow is a novel AI framework that:

Collects your lifeline data (diet, environment, behavior, purchases, biometrics) continuously and privately
Discovers causal paths linking life events to outcomes (disease, learning difficulties, financial risks, etc.)
Grows with you—a digital twin that understands you deeply
Becomes your legacy—inheritable by descendants as a high-value digital asset

Core metaphor: Pulse = the rhythm of your life; Flow = the causal connections between life events.

🔬 Key Innovations

1. Federated Causal Discovery (FCD)

Learn causal graphs from distributed data without sharing raw data.

2. Multi-Modal Evidence Fusion (MMEF)

Automatically weigh heterogeneous data sources by reliability.

3. Personalized Intervention Engine (PIE)

Recommend actionable interventions based on discovered causal paths.

🌐 Cross-Domain Applications

Domain	Lifeline Data	Target Outcome	Application
Healthcare	Diet, environment, purchases	Disease onset	Etiology discovery
Education	Study logs, sleep, social	Learning difficulty	Personalized tutoring
Finance	Spending, income, life events	Financial risk	Early warning
Productivity	App usage, location, calendar	Burnout	Intervention

🧬 Digital Legacy

If you start using PulseFlow at age 8 and continue until death (~80 years), it accumulates:

~72 years of lifeline data
~10^7 – 10^9 life events
A rich causal graph of "why you are who you are"

This is your Digital Legacy—far more valuable than money or property.

📊 Preliminary Results

Method	Precision	Recall	SHD
PC	0.52	0.48	23.4
NOTEARS	0.61	0.59	18.7
FCI	0.58	0.71	15.2
PulseFlow	0.73	0.69	11.8

📚 Paper

PulseFlow: A Lifelong AI Companion Framework via Causal Multi-Modal Lifeline Analysis

arXiv: [Submitted] (pending endorsement)
PDF: See paper/pulseflow.pdf

🤝 Collaboration

We invite collaborations from:

Medical institutions: For real-world etiology discovery validation
Educators: For learning difficulty diagnosis pilot
Hardware manufacturers: For edge device co-development
Legal scholars: For Digital Legacy framework design
Investors: For commercialization

Contact: Open an issue or email [your-email]

📄 License

MIT License — free for academic and non-commercial use.

PulseFlow: Understand your past, navigate your present, legacy your future.

PulseFlow

A Lifelong AI Companion Framework via Causal Multi-Modal Lifeline Analysis
脉流：基于因果多模态生命线分析的终身AI伴侣框架

🌊 What is PulseFlow?

PulseFlow is a novel AI framework that:

Collects your lifeline data (diet, environment, behavior, purchases, biometrics) continuously and privately
Discovers causal paths linking life events to outcomes (disease, learning difficulties, financial risks, etc.)
Grows with you—a digital twin that understands you deeply
Becomes your legacy—inheritable by descendants as a high-value digital asset

Core metaphor: Pulse = the rhythm of your life; Flow = the causal connections between life events.

🔬 Key Innovations

1. Federated Causal Discovery (FCD)

Learn causal graphs from distributed data without sharing raw data.

2. Multi-Modal Evidence Fusion (MMEF)

Automatically weigh heterogeneous data sources by reliability.

3. Personalized Intervention Engine (PIE)

Recommend actionable interventions based on discovered causal paths.

🌐 Cross-Domain Applications

Domain	Lifeline Data	Target Outcome	Application
Healthcare	Diet, environment, purchases	Disease onset	Etiology discovery
Education	Study logs, sleep, social	Learning difficulty	Personalized tutoring
Finance	Spending, income, life events	Financial risk	Early warning
Productivity	App usage, location, calendar	Burnout	Intervention

🧬 Digital Legacy

If you start using PulseFlow at age 8 and continue until death (~80 years), it accumulates:

~72 years of lifeline data
~10^7 – 10^9 life events
A rich causal graph of "why you are who you are"

This is your Digital Legacy—far more valuable than money or property.

📊 Preliminary Results

Method	Precision	Recall	SHD
PC	0.52	0.48	23.4
NOTEARS	0.61	0.59	18.7
FCI	0.58	0.71	15.2
PulseFlow	0.73	0.69	11.8

📚 Paper

PulseFlow: A Lifelong AI Companion Framework via Causal Multi-Modal Lifeline Analysis

arXiv: [Submitted] (pending endorsement)
PDF: See paper/pulseflow.pdf

🤝 Collaboration

We invite collaborations from:

Medical institutions: For real-world etiology discovery validation
Educators: For learning difficulty diagnosis pilot
Hardware manufacturers: For edge device co-development
Legal scholars: For Digital Legacy framework design
Investors: For commercialization

Contact: Open an issue or email [your-email]

📄 License

MIT License — free for academic and non-commercial use.

PulseFlow: Understand your past, navigate your present, legacy your future.

PulseFlow

A Lifelong AI Companion Framework via Causal Multi-Modal Lifeline Analysis
脉流：基于因果多模态生命线分析的终身AI伴侣框架

🌊 What is PulseFlow?

PulseFlow is a novel AI framework that:

Collects your lifeline data (diet, environment, behavior, purchases, biometrics) continuously and privately
Discovers causal paths linking life events to outcomes (disease, learning difficulties, financial risks, etc.)
Grows with you—a digital twin that understands you deeply
Becomes your legacy—inheritable by descendants as a high-value digital asset

Core metaphor: Pulse = the rhythm of your life; Flow = the causal connections between life events.

🔬 Key Innovations

1. Federated Causal Discovery (FCD)

Learn causal graphs from distributed data without sharing raw data.

2. Multi-Modal Evidence Fusion (MMEF)

Automatically weigh heterogeneous data sources by reliability.

3. Personalized Intervention Engine (PIE)

Recommend actionable interventions based on discovered causal paths.

🌐 Cross-Domain Applications

Domain	Lifeline Data	Target Outcome	Application
Healthcare	Diet, environment, purchases	Disease onset	Etiology discovery
Education	Study logs, sleep, social	Learning difficulty	Personalized tutoring
Finance	Spending, income, life events	Financial risk	Early warning
Productivity	App usage, location, calendar	Burnout	Intervention

🧬 Digital Legacy

If you start using PulseFlow at age 8 and continue until death (~80 years), it accumulates:

~72 years of lifeline data
~10^7 – 10^9 life events
A rich causal graph of "why you are who you are"

This is your Digital Legacy—far more valuable than money or property.

📊 Preliminary Results

Method	Precision	Recall	SHD
PC	0.52	0.48	23.4
NOTEARS	0.61	0.59	18.7
FCI	0.58	0.71	15.2
PulseFlow	0.73	0.69	11.8

📚 Paper

PulseFlow: A Lifelong AI Companion Framework via Causal Multi-Modal Lifeline Analysis

arXiv: [Submitted] (pending endorsement)
PDF: See paper/pulseflow.pdf

🤝 Collaboration

We invite collaborations from:

Medical institutions: For real-world etiology discovery validation
Educators: For learning difficulty diagnosis pilot
Hardware manufacturers: For edge device co-development
Legal scholars: For Digital Legacy framework design
Investors: For commercialization

Contact: Open an issue or email [your-email]

📄 License

MIT License — free for academic and non-commercial use.

PulseFlow: Understand your past, navigate your present, legacy your future.

PulseFlow

A Lifelong AI Companion Framework via Causal Multi-Modal Lifeline Analysis
脉流：基于因果多模态生命线分析的终身AI伴侣框架

🌊 What is PulseFlow?

PulseFlow is a novel AI framework that:

Collects your lifeline data (diet, environment, behavior, purchases, biometrics) continuously and privately
Discovers causal paths linking life events to outcomes (disease, learning difficulties, financial risks, etc.)
Grows with you—a digital twin that understands you deeply
Becomes your legacy—inheritable by descendants as a high-value digital asset

Core metaphor: Pulse = the rhythm of your life; Flow = the causal connections between life events.

🔬 Key Innovations

1. Federated Causal Discovery (FCD)

Learn causal graphs from distributed data without sharing raw data.

2. Multi-Modal Evidence Fusion (MMEF)

Automatically weigh heterogeneous data sources by reliability.

3. Personalized Intervention Engine (PIE)

Recommend actionable interventions based on discovered causal paths.

🌐 Cross-Domain Applications

Domain	Lifeline Data	Target Outcome	Application
Healthcare	Diet, environment, purchases	Disease onset	Etiology discovery
Education	Study logs, sleep, social	Learning difficulty	Personalized tutoring
Finance	Spending, income, life events	Financial risk	Early warning
Productivity	App usage, location, calendar	Burnout	Intervention

🧬 Digital Legacy

If you start using PulseFlow at age 8 and continue until death (~80 years), it accumulates:

~72 years of lifeline data
~10^7 – 10^9 life events
A rich causal graph of "why you are who you are"

This is your Digital Legacy—far more valuable than money or property.

📊 Preliminary Results

Method	Precision	Recall	SHD
PC	0.52	0.48	23.4
NOTEARS	0.61	0.59	18.7
FCI	0.58	0.71	15.2
PulseFlow	0.73	0.69	11.8

📚 Paper

PulseFlow: A Lifelong AI Companion Framework via Causal Multi-Modal Lifeline Analysis

arXiv: [Submitted] (pending endorsement)
PDF: See paper/pulseflow.pdf

🤝 Collaboration

We invite collaborations from:

Medical institutions: For real-world etiology discovery validation
Educators: For learning difficulty diagnosis pilot
Hardware manufacturers: For edge device co-development
Legal scholars: For Digital Legacy framework design
Investors: For commercialization

Contact: Open an issue or email [your-email]

📄 License

MIT License — free for academic and non-commercial use.

PulseFlow: Understand your past, navigate your present, legacy your future.

PulseFlow

A Lifelong AI Companion Framework via Causal Multi-Modal Lifeline Analysis
脉流：基于因果多模态生命线分析的终身AI伴侣框架

🌊 What is PulseFlow?

PulseFlow is a novel AI framework that:

Collects your lifeline data (diet, environment, behavior, purchases, biometrics) continuously and privately
Discovers causal paths linking life events to outcomes (disease, learning difficulties, financial risks, etc.)
Grows with you—a digital twin that understands you deeply
Becomes your legacy—inheritable by descendants as a high-value digital asset

Core metaphor: Pulse = the rhythm of your life; Flow = the causal connections between life events.

🔬 Key Innovations

1. Federated Causal Discovery (FCD)

Learn causal graphs from distributed data without sharing raw data.

2. Multi-Modal Evidence Fusion (MMEF)

Automatically weigh heterogeneous data sources by reliability.

3. Personalized Intervention Engine (PIE)

Recommend actionable interventions based on discovered causal paths.

🌐 Cross-Domain Applications

Domain	Lifeline Data	Target Outcome	Application
Healthcare	Diet, environment, purchases	Disease onset	Etiology discovery
Education	Study logs, sleep, social	Learning difficulty	Personalized tutoring
Finance	Spending, income, life events	Financial risk	Early warning
Productivity	App usage, location, calendar	Burnout	Intervention

🧬 Digital Legacy

If you start using PulseFlow at age 8 and continue until death (~80 years), it accumulates:

~72 years of lifeline data
~10^7 – 10^9 life events
A rich causal graph of "why you are who you are"

This is your Digital Legacy—far more valuable than money or property.

📊 Preliminary Results

Method	Precision	Recall	SHD
PC	0.52	0.48	23.4
NOTEARS	0.61	0.59	18.7
FCI	0.58	0.71	15.2
PulseFlow	0.73	0.69	11.8

📚 Paper

PulseFlow: A Lifelong AI Companion Framework via Causal Multi-Modal Lifeline Analysis

arXiv: [Submitted] (pending endorsement)
PDF: See paper/pulseflow.pdf

🤝 Collaboration

We invite collaborations from:

Medical institutions: For real-world etiology discovery validation
Educators: For learning difficulty diagnosis pilot
Hardware manufacturers: For edge device co-development
Legal scholars: For Digital Legacy framework design
Investors: For commercialization

Contact: Open an issue or email [your-email]

📄 License

MIT License — free for academic and non-commercial use.

PulseFlow: Understand your past, navigate your present, legacy your future.

PulseFlow

A Lifelong AI Companion Framework via Causal Multi-Modal Lifeline Analysis
脉流：基于因果多模态生命线分析的终身AI伴侣框架

🌊 What is PulseFlow?

PulseFlow is a novel AI framework that:

Collects your lifeline data (diet, environment, behavior, purchases, biometrics) continuously and privately
Discovers causal paths linking life events to outcomes (disease, learning difficulties, financial risks, etc.)
Grows with you—a digital twin that understands you deeply
Becomes your legacy—inheritable by descendants as a high-value digital asset

Core metaphor: Pulse = the rhythm of your life; Flow = the causal connections between life events.

🔬 Key Innovations

1. Federated Causal Discovery (FCD)

Learn causal graphs from distributed data without sharing raw data.

2. Multi-Modal Evidence Fusion (MMEF)

Automatically weigh heterogeneous data sources by reliability.

3. Personalized Intervention Engine (PIE)

Recommend actionable interventions based on discovered causal paths.

🌐 Cross-Domain Applications

Domain	Lifeline Data	Target Outcome	Application
Healthcare	Diet, environment, purchases	Disease onset	Etiology discovery
Education	Study logs, sleep, social	Learning difficulty	Personalized tutoring
Finance	Spending, income, life events	Financial risk	Early warning
Productivity	App usage, location, calendar	Burnout	Intervention

🧬 Digital Legacy

If you start using PulseFlow at age 8 and continue until death (~80 years), it accumulates:

~72 years of lifeline data
~10^7 – 10^9 life events
A rich causal graph of "why you are who you are"

This is your Digital Legacy—far more valuable than money or property.

📊 Preliminary Results

Method	Precision	Recall	SHD
PC	0.52	0.48	23.4
NOTEARS	0.61	0.59	18.7
FCI	0.58	0.71	15.2
PulseFlow	0.73	0.69	11.8

📚 Paper

PulseFlow: A Lifelong AI Companion Framework via Causal Multi-Modal Lifeline Analysis

arXiv: [Submitted] (pending endorsement)
PDF: See paper/pulseflow.pdf

🤝 Collaboration

We invite collaborations from:

Medical institutions: For real-world etiology discovery validation
Educators: For learning difficulty diagnosis pilot
Hardware manufacturers: For edge device co-development
Legal scholars: For Digital Legacy framework design
Investors: For commercialization

Contact: Open an issue or email [your-email]

📄 License

MIT License — free for academic and non-commercial use.

PulseFlow: Understand your past, navigate your present, legacy your future.

PulseFlow

A Lifelong AI Companion Framework via Causal Multi-Modal Lifeline Analysis
脉流：基于因果多模态生命线分析的终身AI伴侣框架

🌊 What is PulseFlow?

PulseFlow is a novel AI framework that:

Collects your lifeline data (diet, environment, behavior, purchases, biometrics) continuously and privately
Discovers causal paths linking life events to outcomes (disease, learning difficulties, financial risks, etc.)
Grows with you—a digital twin that understands you deeply
Becomes your legacy—inheritable by descendants as a high-value digital asset

Core metaphor: Pulse = the rhythm of your life; Flow = the causal connections between life events.

🔬 Key Innovations

1. Federated Causal Discovery (FCD)

Learn causal graphs from distributed data without sharing raw data.

2. Multi-Modal Evidence Fusion (MMEF)

Automatically weigh heterogeneous data sources by reliability.

3. Personalized Intervention Engine (PIE)

Recommend actionable interventions based on discovered causal paths.

🌐 Cross-Domain Applications

Domain	Lifeline Data	Target Outcome	Application
Healthcare	Diet, environment, purchases	Disease onset	Etiology discovery
Education	Study logs, sleep, social	Learning difficulty	Personalized tutoring
Finance	Spending, income, life events	Financial risk	Early warning
Productivity	App usage, location, calendar	Burnout	Intervention

🧬 Digital Legacy

If you start using PulseFlow at age 8 and continue until death (~80 years), it accumulates:

~72 years of lifeline data
~10^7 – 10^9 life events
A rich causal graph of "why you are who you are"

This is your Digital Legacy—far more valuable than money or property.

📊 Preliminary Results

Method	Precision	Recall	SHD
PC	0.52	0.48	23.4
NOTEARS	0.61	0.59	18.7
FCI	0.58	0.71	15.2
PulseFlow	0.73	0.69	11.8

📚 Paper

PulseFlow: A Lifelong AI Companion Framework via Causal Multi-Modal Lifeline Analysis

arXiv: [Submitted] (pending endorsement)
PDF: See paper/pulseflow.pdf

🤝 Collaboration

We invite collaborations from:

Medical institutions: For real-world etiology discovery validation
Educators: For learning difficulty diagnosis pilot
Hardware manufacturers: For edge device co-development
Legal scholars: For Digital Legacy framework design
Investors: For commercialization

Contact: Open an issue or email [your-email]

📄 License

MIT License — free for academic and non-commercial use.

PulseFlow: Understand your past, navigate your present, legacy your future.

Name		Name	Last commit message	Last commit date
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README.md		README.md
pulseflow.md		pulseflow.md

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LICENSE		LICENSE
README.md		README.md
pulseflow.md		pulseflow.md

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README.md		README.md
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README.md		README.md
pulseflow.md		pulseflow.md

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README.md		README.md
pulseflow.md		pulseflow.md

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PulseFlow

🌊 What is PulseFlow?

🔬 Key Innovations

1. Federated Causal Discovery (FCD)

2. Multi-Modal Evidence Fusion (MMEF)

3. Personalized Intervention Engine (PIE)

🌐 Cross-Domain Applications

🧬 Digital Legacy

📊 Preliminary Results

📚 Paper

🤝 Collaboration

📄 License

About

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Stars

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Contributors

Latest commit

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Folders and files

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PulseFlow

🌊 What is PulseFlow?

🔬 Key Innovations

1. Federated Causal Discovery (FCD)

2. Multi-Modal Evidence Fusion (MMEF)

3. Personalized Intervention Engine (PIE)

🌐 Cross-Domain Applications

🧬 Digital Legacy

📊 Preliminary Results

📚 Paper

🤝 Collaboration

📄 License

About

Resources

Stars

Watchers

Forks

Releases

Packages

Contributors

Latest commit

History

Folders and files

Repository files navigation

PulseFlow

🌊 What is PulseFlow?

🔬 Key Innovations

1. Federated Causal Discovery (FCD)

2. Multi-Modal Evidence Fusion (MMEF)

3. Personalized Intervention Engine (PIE)

🌐 Cross-Domain Applications

🧬 Digital Legacy

📊 Preliminary Results

📚 Paper

🤝 Collaboration

📄 License

About

Resources

Stars

Watchers

Forks

Releases

Packages

Contributors

Latest commit

History

Folders and files

Repository files navigation

PulseFlow

🌊 What is PulseFlow?

🔬 Key Innovations

1. Federated Causal Discovery (FCD)