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JUNEO LAB · LAPLACE-1

The trust layer for
people and agents

Laplace foresees the consequence of an action before an agent takes it, and makes the four decisions organizations delegate: who does the work, who sees the information, what runs, and what is accepted

Looking back from the age of intelligence

Every leap of civilization
was an expansion of trust

The scale of cooperation has never been set by how intelligent individuals are, but by how far strangers can trust one another

~70,000 years ago

The tribe

Trust came from
Familiarity

~5,000 years ago

Cities and empires

Trust came from
Writing and law

1602

The joint-stock company

Trust came from
Contract

1990s

The internet

Trust came from
Platforms and payments

2030s

People and agents

Trust came from
Foresight of consequences

The consensus

In the age of AI, what is scarce is stronger intelligence

Our view

Intelligence is no longer scarce
What is scarce is trust

The fifth leap

Trust will come from foreseeing consequences

Laplace builds that trust for the second workforce

Four decisions

Organizations delegate
more than actions

Whenever people and agents work together, four questions come up
Laplace foresees the consequence of each candidate, then decides

How Laplace decides: organization state, chain of authority, and candidates go in; Laplace foresees each consequence and makes four decisions: allocate work, share information, act, and evaluate outcomes

01

Allocate work

Who should own this task?

02

Share information

Who may see what, and how much?

03

Act

Run it, adjust it, or ask first?

04

Evaluate outcomes

Does the result meet the standard?

Laplace-1 · Open weights

First on most
decision benchmarks

A flagship and an on-device size, evaluated against models from OpenAI, Perplexity, ByteDance, Alibaba, and others. Complete weights, Apache-2.0

27B · Flagship

Laplace-1-Atlas

First on 7 of 9 decision benchmarks

89.4% on TrustDecision, 5.4 points ahead of the next-best model

Model card  ↗

4B · On-device

Laplace-1-Vector

First on 7 of 8 decision benchmarks

Ahead of IBM’s Granite Switch 4.1 8B, twice its size, on all 8. Judgment runs where agents act

Model card  ↗

First places include ties

Decision Forge · Open source

The standard of judgment
lives inside the organization

Public data records what happened, never what an action caused. Decision Forge turns an organization’s own policies, approvals, and tickets into verified decision data, and every label traces back to a rule and its source

xDecision context

The state, permissions, and options at the moment an agent acts

ℓJudgment standard

What this organization allows, forbids, or escalates

s′Execution receipt

What actually changed after the action

The Laplace flywheel: Decision Forge turns organization documents into verified decision data that trains Laplace; Laplace is deployed in AI-native organizations, and execution receipts and human confirmations flow back. Data stays inside the organization

The better Laplace judges, the more work organizations delegate
More delegation brings more receipts, and more receipts teach Laplace the organization

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Juneo Lab · The trust layer for people and agents

Era images: Lascaux cave paintings; Thomas Cole, The Course of Empire: The Consummation (1836); H. C. Vroom, Return to Amsterdam of the Second Expedition to the East Indies (1599); NASA, Earth at Night. Public domain, via Wikimedia Commons

中文
夜色中光点沿城市网络蔓延

JUNEO LAB · LAPLACE-1

智能时代的
可信协作基础设施

Laplace 在 Agent 动手之前推演后果,替组织做好四类判断:工作交给谁、信息给谁、动作做不做、结果收不收

站在智能时代的终局回望

人类文明的每一次跃迁
都是一次信任的扩张

协作的规模,从来不取决于个体有多聪明,而取决于陌生人之间能否互相信任

约 7 万年前

部落

信任来自
熟人

约 5000 年前

城邦与帝国

信任来自
文字与法律

1602 年

股份公司

信任来自
契约

1990 年代

互联网

信任来自
平台与支付

2030 年代

人与 Agent 的组织

信任来自
对后果的预见

共识

AI 时代,稀缺的是更强的智能

我们的判断

智能已不稀缺
稀缺的是信任

第五次跃迁

信任来自对后果的预见

Laplace 为第二种劳动力建造信任基础设施

四类判断

组织交给 AI 的
从来不只是一个动作

人与 Agent 一起工作时,每天都要回答四个问题
Laplace 先推演每个候选的后果,再给出判断

Laplace 的判断过程:输入组织状态、授权链与候选项,Laplace 推演每个后果,并完成分配任务、共享信息、执行操作、验收成果四类判断

01

分配任务

这件事交给谁?

02

共享信息

这条信息能给谁、给多少?

03

执行操作

直接做、调整,还是先请示?

04

验收成果

结果合不合格?

Laplace-1 · 开源权重

同台 OpenAI、字节、阿里
拿下多数第一

旗舰与端侧两个版本,权重完整开源,Apache-2.0 协议

27B · 旗舰

Laplace-1-Atlas

9 项决策基准 · 7 项第一

可信决策 TrustDecision 89.4%,领先第二名 5.4 分

查看模型卡  ↗

4B · 端侧

Laplace-1-Vector

8 项决策基准 · 7 项第一

全部 8 项超过参数量是它两倍的 IBM Granite Switch 4.1 8B,判断在 Agent 身边完成

查看模型卡  ↗

“第一”含并列

Decision Forge · 开源

判断标准
只存在于组织内部

公开数据只记录发生了什么,不记录一个动作造成了什么。Decision Forge 把组织自己的制度、审批和工单变成可验证的决策数据,每个标签都能追溯到一条规则,每条规则都能追溯到原文

x决策上下文

Agent 动手那一刻,组织的状态、权限与候选项

ℓ判断标准

这个组织允许什么、禁止什么、什么需要升级

s′执行回执

动作完成之后,组织真实发生了什么

Laplace 飞轮:Decision Forge 把组织文档变成已验证的决策数据,训练 Laplace;Laplace 部署在 AI Native 组织中,执行回执与人工确认回流到 Decision Forge,数据始终留在组织内部

判得越准,组织交出的工作越多
交出的越多,回执越多,Laplace 就越懂这个组织

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juneo.ai  ↗

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敢把工作交给 Agent

Juneo Lab · 智能时代的可信协作基础设施

时代图片:拉斯科洞穴壁画;Thomas Cole《帝国的历程·鼎盛》(1836);H. C. Vroom《第二次东印度远航返回阿姆斯特丹》(1599);NASA 地球夜景。均为公有领域,来自 Wikimedia Commons