LiveLiva
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A VENTURE BY WYON LIURESEARCH × PRODUCT × COMPANY BUILDING

AI that learns
from experience.

Personal agents. World models.
Recursive self-improvement.

LiveLiva brings these research directions into a human-centered platform: AI that grows with people, connects them and participates in the worlds they create.

THE RESEARCH THESISFIG. 01
01

Understand the person

PERSONAL AGENTS / HUMAN MODELS

α
intent informs action
02

Model the world

WORLD MODELS / SPATIAL INTELLIGENCE

ω
action produces experience
03

Learn how to learn

CONTINUAL LEARNING / RSI

Δ
↳

Evaluated improvements return to the agent
and the world-building system.

↥

Research agenda. Learning gains remain to be demonstrated.

01 / RESEARCH & ENGINEERINGPersonal agents · world models · RSI

Three questions.
One coherent direction.

How can AI understand an individual, act in a persistent world and become more capable through experience? LiveLiva connects these questions in one product and systems agenda.

01RESEARCH DIRECTION
PERSONAL AGENTS

Intelligence with continuity.

An agent should carry a coherent identity, understand its person and act across contexts. Memory, learned skills and human relationships must remain distinct—and work together.

Human models · persistent identity · A2A
02RESEARCH DIRECTION
WORLD MODELS

From perception to consequence.

Spatial intelligence connects perception and generation. A world model predicts what an action may change; a persistent runtime determines what actually happened.

Spatial representations · action-conditioned prediction
03RESEARCH DIRECTION
RECURSIVE SELF-IMPROVEMENT

Improve the way AI improves.

Continual learning must retain and transfer capabilities. RSI goes further: improve the process that produces improvements, and test descendants independently at a matched budget.

Candidate generation · held-out evaluation · lineage
02 / THE LIVELIVA PLATFORMFrom research to human value

Personal intelligence.
Shared human worlds.

The product ambition is a network of living worlds: persistent personal AI, real human relationships and places created by people. The first compelling experiences establish the standard for a broader creator platform.

PERSONAL AI

A life that grows with you

Identity, understanding, memory and useful abilities persist across experiences.

HUMAN CONNECTION

Relationships through participation

AI helps people discover shared interests, meet and create a history together.

CREATOR WORLDS

Culture becomes something to do

Local stories, crafts and imagination become playable, social worlds with creator livelihoods.

ONE POSSIBLE JOURNEY

A local story→A shared adventure→A companion & a new ability→Another creator’s world

Illustrative product path. World selection and launch scope are not finalized.

The platform hypothesis: continuity makes new worlds more valuable; creators expand what people can experience. Proposed revenue includes paid experiences, personal services and expressive items, with creator revenue sharing.

Explore the product blueprint ↗
03 / SYSTEMS ARCHITECTURETarget architecture · research boundaries explicit

A system for experience.
A discipline for improvement.

Nuwa reasons and acts. Fuxi maintains shared state. Pangu creates worlds. World models support planning; learning research proposes upgrades that must earn their way into the system.

LiveLiva — system architectureNuwa proposes actions; Fuxi confirms outcomes; Pangu publishes worlds. World models assist planning and playtests. Independent research evaluates upgrades.PEOPLE, AGENTS & WORLDSPeople & personal contextCreators, culture & source materialintent & permissionauthor & editNUWA / PERSONAL AGENTSUnderstand. Remember. Act.Human model · identity · skillsProduces an action proposalFUXI / PERSISTENT WORLDMaintain shared reality.Rules · state · rights · eventsConfirms outcomes and records historyPANGU / WORLD CREATIONCreate. Test. Publish.Scenes · characters · rulesProduces a versioned world packageactionoutcomepublishfeedbackWORLD MODELSState + action → predicted consequenceplanningsimulated playtestsobserved transitionspermitted outcomesLEARNING RESEARCH / SEPARATE FROM LIVE EXECUTIONEligible experienceConsent · provenance · outcomesContinual learning / RSISkills · methods · descendantsIndependent evaluationHeld-out tasks · equal budgetControlled releasePromote or roll backEvaluated upgrades return to the relevant component; the agent path is shown.
FIG. 02 / LIVELIVASolid: designed runtime flow. Dashed: prediction and evaluated learning.Open figure ↗
  1. Pangu · createSources → editable, tested world packages
  2. Nuwa ↔ Fuxi · actAction proposals → verified outcomes → personal experience
  3. World models · predictState + action → possible consequences for planning
  4. CL → RSIEligible experience → candidates → independent evaluation → release / rollback
Open the full architecture ↗
CURRENT EVIDENCE

Local protocol checks: identity continuity, action conflicts, permission revocation and restart recovery. Synthetic accounts; no model calls in these checks.

NEXT PROOF

A compelling first experience and a second independent creator’s world. Validate return visits, cross-world value, production economics and learning gains separately.

Evidence scope ↗
04 / FOUNDER & TRACK RECORDFull CV & project evidence ↗

TECHNICAL FOUNDER / AI SYSTEMS BUILDER

Wyon Liu刘怀洋

From the algorithm
to the company.

BSc and MSc in Computer Science, Peking University. Seengene founder & CEO for eight years; later led innovation at Beike and served as QianDing CTO. Experience spans game engines, spatial computing, model engineering, AI characters and company building.

Arto · Sida · Mira

Spatial AI & developer platforms

Spatial reconstruction, localization, generative editing and mixed rendering. Arto reached 50,000+ developers.

Background & evidence ↗
Artown · Seek

Interactive worlds & agents

Dozens of autonomous AI characters with memory and behavior, real-time interaction and AR/VR connectivity.

Background & evidence ↗
Seengene

Company building

Eight years as founder & CEO: nearly RMB100M raised, a 100-person team and RMB tens of millions in revenue.

Background & evidence ↗

Selected past work, as documented in the public CV. These outcomes belong to prior projects and companies; they are not LiveLiva traction.

A full-stack perspective on intelligence.

L0 → L5
05 / SELECTED MATERIALSA short introduction. A deeper conversation.

Explore the work
behind the thesis.

Start with the brief. Review the architecture and founder background. The longer-form ideas and concept film show the product ambition in context.

06 / BUILD THE NEXT CHAPTERAI BUSINESS LEADERSHIP · INTERNAL INCUBATION · INVESTMENT

An ambitious direction.
A substantive conversation.

I’m open to joining a company to lead a new AI product or business, with responsibility for product, engineering and building the team. LiveLiva is the early product direction I’m developing, and a basis for discussing internal incubation.

For investors, I’m also exploring early-stage funding to build LiveLiva independently. These are distinct paths; the next conversation should establish the role or investment mandate, the team and the resources available.

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