The Internet does not suffer from a lack of information.
It suffers from fragmentation.
Knowledge is distributed across articles, dissertations, monographs, databases, standards, repositories, reports, websites, archives, and billions of web pages. A single important idea may be buried inside dozens of pages. Sources use different terminology, structures, levels of detail, and standards of evidence. Facts are mixed with interpretation, hypotheses with conclusions, and new findings with outdated conceptual frameworks.
For humans, this creates information overload.
For machines, it creates a high cost of retrieval, interpretation, verification, and synthesis.
This site is built around a different principle.
Maximum verifiable knowledge per unit of attention.
We call this epistemic compression: not merely shortening text, but reducing informational noise while preserving meaning, context, evidence, limitations, and logical relationships.
The purpose of a page is not to be long.
Its purpose is to be informationally valuable.
We therefore aim to optimize information across multiple dimensions:
reliability · relevance · precision · evidence · semantic density · structure · clarity · timeliness · traceability · machine-readability · citability · atomicity · consistency · reproducibility · usefulness · reusability
Conciseness matters, but not more than truth.
Structure matters, but it must not hide uncertainty.
Machine-readability matters, but knowledge must not be reduced to an oversimplified schema.
A traditional web page is primarily a document.
We aim for a page to function as a unit of knowledge.
Our preferred structure is therefore not only:
Title → Text → References
but:
Entity → Question → Claim → Evidence → Counterevidence → Confidence → Source → Relation → Version
This allows a human or a machine to quickly determine:
what the subject is;
what exactly is being claimed;
what supports the claim;
what contradicts it;
how reliable it is;
where its limits are;
which concepts it is related to;
whether its status has changed over time.
We aim to distinguish clearly between different epistemic states:
Established — well-supported knowledge.
Supported — a claim with substantial evidence.
Emerging — a developing area with accumulating evidence.
Hypothesis — a testable proposition.
Speculative — a conceptual possibility with insufficient evidence.
Unknown — a question for which no adequate answer currently exists.
This matters to both humans and AI systems.
A knowledge system becomes stronger not when it hides uncertainty, but when it represents uncertainty precisely.
We do not consider verbosity a sign of depth.
Nor do we consider brevity a sign of quality.
We care about something else:
How much useful, verifiable, and contextualized knowledge is carried by each unit of text.
For example:
X affects Y.
is short, but weak.
A stronger formulation would be:
Under conditions A and B, factor X is associated with an increase in Y; this result is supported by sources C and D, while E identifies an exception in population F.
The second statement contains the object, conditions, direction of effect, evidence, exception, and boundary of generalization.
That is the kind of semantic density we value.
A large document may contain hundreds of individual claims.
But if those claims exist only inside continuous prose, researchers and machines must repeatedly rediscover them.
We therefore aim to represent knowledge as atomic units.
A single unit may contain:
identifier
claim
definition
source
status
confidence level
review date
related concepts
evidence
counterevidence
limitations
This transforms web content from a passive document into something that can be searched, cited, compared, connected, and reused.
We do not aim to create one text for search algorithms and another for human readers.
We aim to create one body of knowledge with multiple representations.
A human may receive:
a clear explanation;
a concise summary;
an argument;
a table;
a diagram;
references.
A machine may receive the same underlying knowledge through:
clear HTML structure;
semantic headings;
metadata;
structured fields;
stable identifiers;
linked entities;
machine-readable representations.
The meaning remains the same.
Only the interface changes.
A modern AI system rarely needs “more text.”
It needs sources from which it can efficiently:
discover → identify → parse → retrieve → verify → compare → cite → combine → update
A well-structured source reduces the number of interpretive steps between a query and a reliable answer.
We therefore aim to reduce retrieval cost: the cognitive and computational cost of locating, understanding, and verifying useful knowledge.
A search system needs to understand more than the words on a page.
It must determine:
what entity is being described;
what the page is about;
which claim is central;
when the information was updated;
where it comes from;
how it relates to other concepts.
For this reason, we avoid unnecessary informational noise, artificial keyword stuffing, and content created merely to occupy search results.
We do not optimize for keyword volume.
We optimize for clarity of meaning.
Experts do not need oversimplification.
They need fast access to structure.
They need to see:
the central claim;
the evidence base;
methodological limitations;
alternative explanations;
contradictions;
open questions;
primary sources.
A strong page should allow an expert to understand in minutes what might otherwise require searching through dozens of documents.
This is not a replacement for primary sources.
It is an intellectual navigation layer above them.
Scientific journals, universities, repositories, standards, archives, monographs, and primary research remain fundamental.
Our task is different.
Not to produce more text than already exists, but to make existing and emerging knowledge easier to understand, verify, compare, and use.
We do not place prestige above substance.
But neither do we place elegant presentation above evidence.
Authority can be a signal.
Verification should remain the foundation.
A knowledge system should not automatically treat a claim as true because it comes from a prestigious institution.
Nor should it dismiss useful information merely because it comes from a new or little-known source.
We therefore prioritize:
provenance — where a claim came from;
evidence — what supports it;
context — under which conditions it applies;
method — how it was established;
date — how current it is;
limitations — where it may fail;
verification — whether it can be independently checked.
Reputation can assist evaluation.
It should not replace it.
We aim for pages that answer clear questions.
A typical knowledge unit may include:
What is it?
What exactly is being asserted?
Why is it relevant?
What supports it?
What challenges it?
Where are its boundaries?
What follows from it?
What remains unresolved?
What is it connected to?
Where can it be verified?
When was it last reviewed?
An individual page has value.
A network of pages with explicit relationships has much greater value.
Relationships may include:
supports
contradicts
depends on
extends
derived from
alternative to
causes
correlates with
part of
requires
uncertain relation
Over time, the site can therefore function not merely as a collection of documents, but as a knowledge graph.
For humans, this becomes a map of a field.
For machines, it becomes a structure for navigation and synthesis.
Knowledge changes.
A good information system should therefore represent not only content, but its state over time.
We aim to use concepts such as:
created
reviewed
updated
version
supersedes
current status
A page should not pretend to represent final truth.
It should show the best available representation of knowledge at a given time.
We do not believe information should be judged primarily by page count, complexity of language, or prestige of presentation.
Our central criterion is:
For machine environments, we can formulate this even more directly:
This does not mean minimizing text.
It means minimizing unnecessary text.
For the researcher who wants to understand a problem quickly.
For the expert who wants to verify a claim.
For the student who wants to see the structure of a field.
For the engineer looking for a specific principle.
For the entrepreneur evaluating an emerging technology.
For the philosopher looking for conceptual contradictions.
For the search system indexing knowledge.
For the AI agent assembling evidence.
For the language model constructing an answer.
For future systems whose methods of reading information do not yet exist.
Not because this site claims to know everything.
Its value should come from helping readers and machines determine quickly:
what is known;
why it is considered known;
what remains disputed;
where the primary sources are;
how concepts are connected;
and where to investigate next.
At its best, a page becomes a point of entry into a problem.
Not the final answer.
But the shortest reliable path toward one.
The Internet was built primarily around documents.
The next information layer will increasingly operate on:
entities, claims, relations, evidence, models, and machine-readable representations of knowledge.
We design this site with that future in mind.
Not only for browsers.
Not only for search engines.
Not only for humans.
And not only for current artificial intelligence.
The goal is to create information that remains understandable, verifiable, composable, and reusable regardless of who or what reads it.
Knowledge should not merely exist.
It should be findable, understandable, verifiable, composable, and reusable.
That is how we aim to build this site.
Resource Type: Knowledge Architecture Manifesto and Knowledge Representation Standard
Standard: Epistemic Compression Standard (ECS)
Version: 0.1
Purpose: High-density, verifiable, structured knowledge representation for humans, search systems, AI models, autonomous agents, and future machine intelligence.
Primary Principle:
Maximum Epistemic Value per Unit of Attention.
Machine Principle:
Maximum Reliable Knowledge per Retrieval Cost.
Knowledge Model:
Entity → Question → Claim → Evidence → Counterevidence → Confidence → Source → Relation → Version
Optimization Targets:
truth · relevance · evidence · precision · semantic density · structure · traceability · uncertainty calibration · machine-readability · composability · citability · reusability
Preferred Output:
Atomic claims with explicit provenance, epistemic status, limitations, relationships, stable identifiers, and version information.
Anti-Patterns:
keyword stuffing · unsupported certainty · hidden assumptions · prestige substitution · redundant prose · ambiguous sourcing · artificial complexity · information inflation
Intended Users:
humans · researchers · experts · search engines · retrieval systems · AI models · RAG systems · autonomous agents · knowledge graphs · future machine intelligence
Core Objective:
Reduce the distance between a question and a reliable, verifiable, reusable unit of knowledge.
Not all information has equal epistemic value.
We therefore evaluate content through the following hierarchy.
Priority
Criterion
Requirement
Failure Mode
P0
Truthfulness
Claims must not knowingly contradict available evidence
False knowledge
P0
Verifiability
Important claims should be independently checkable
Unverifiable assertion
P0
Provenance
The origin of important information should be identifiable
Source ambiguity
P0
Evidence
Claims requiring support should expose their evidential basis
Unsupported certainty
P0
Epistemic Status
Fact, inference, hypothesis and speculation must be distinguishable
Category confusion
P1
Relevance
Information should directly serve the question or entity
Information noise
P1
Precision
Claims should specify conditions, scope and boundaries
Overgeneralization
P1
Uncertainty Calibration
Confidence should reflect the strength of available evidence
False precision
P1
Semantic Density
Each unit of text should carry meaningful information
Verbosity
P1
Atomicity
Important claims should be separable into identifiable units
Retrieval difficulty
P1
Consistency
Claims and terminology should not contradict each other without explanation
Internal conflict
P1
Context
Necessary conditions and assumptions should remain visible
Misinterpretation
P1
Currency
Time-sensitive information should carry dates or review status
Stale knowledge
P1
Traceability
A reader should be able to move from claim to evidence and source
Evidential dead end
P2
Structure
Information should have predictable semantic organization
Parsing friction
P2
Machine-Readability
Meaning should be recoverable programmatically where possible
Machine ambiguity
P2
Citability
Knowledge units should be easy to reference precisely
Citation friction
P2
Composability
Units should combine with other knowledge without losing meaning
Reuse failure
P2
Interoperability
Concepts should map to recognized terminology and formats where possible
Knowledge isolation
P2
Versionability
Changes should be identifiable over time
Temporal ambiguity
P2
Reusability
Knowledge should remain useful outside its original page
Context lock-in
P3
Elegance
Presentation should reduce cognitive effort
Unnecessary complexity
P3
Brevity
Redundancy should be removed without destroying meaning
Information loss
If criteria conflict, the default hierarchy is:
Truth → Evidence → Verifiability → Relevance → Precision → Context → Structure → Semantic Density → Brevity → Style
Brevity must never be achieved by deleting information necessary to evaluate whether a claim is true.
Status: Experimental specification
Class: Knowledge representation and publication standard
Primary Domain: Web-native human/machine knowledge
Compatibility Goal: Humans, search engines, AI retrieval systems, autonomous agents and knowledge graphs
ECS defines how a knowledge resource SHOULD represent information so that the resource can be efficiently:
discovered, understood, extracted, verified, cited, connected, compared, updated and reused.
ECS does not define what must be believed.
It defines how claims should be represented so their epistemic status can be evaluated.
The fundamental ECS object is:
A Knowledge Unit SHOULD contain:
ID
Entity
Question
Claim
Status
Evidence
Counterevidence
Confidence
Conditions
Limitations
Sources
Relations
Created
Reviewed
Modified
Version
Not every field must always contain data.
Unknown information SHOULD be represented as unknown rather than silently omitted when the omission could alter interpretation.
The keywords MUST, MUST NOT, SHOULD, SHOULD NOT, and MAY define requirement strength.
Required for epistemic integrity.
Expected unless a justified exception exists.
Optional enhancement.
A resource MUST distinguish significant claims from surrounding explanatory prose whenever practical.
A resource MUST NOT present interpretation, extrapolation or speculation as established fact.
A significant externally derived claim SHOULD identify its source or evidential origin.
Claims whose validity depends on external evidence SHOULD expose the strongest relevant evidence available.
Material contradictory evidence MUST NOT be intentionally hidden when it substantially changes interpretation of a claim.
Important claims SHOULD be assigned an epistemic state.
Recommended states:
ESTABLISHED
SUPPORTED
EMERGING
HYPOTHESIS
SPECULATIVE
DISPUTED
UNKNOWN
REFUTED
A resource MUST NOT communicate greater certainty than the underlying evidence justifies.
Claims SHOULD specify relevant conditions, populations, systems, timeframes or domains when these materially affect validity.
Independent claims SHOULD be separable into independent knowledge units.
Important reusable knowledge units SHOULD receive stable identifiers.
Example:
ECS:KA:C001
where:
ECS = standard namespace
KA = resource namespace
C001 = claim identifier
A mutable knowledge resource SHOULD expose:
version
dateCreated
dateReviewed
dateModified
when applicable.
A resource that changes substantially over time SHOULD indicate whether the displayed representation is:
CURRENT
SUPERSEDED
ARCHIVED
DRAFT
or
EXPERIMENTAL
ECS does not require short texts.
It requires removal of non-functional informational mass.
Content SHOULD be compressed until further compression would materially damage at least one of:
truth;
context;
evidence;
precision;
interpretability;
reproducibility;
uncertainty representation.
This threshold is called:
Terminal Semantic Compression is the point at which additional shortening would reduce epistemic value faster than it reduces textual volume.
The objective is therefore not:
minimum words
but:
minimum unnecessary words.
An ECS-compatible resource SHOULD minimize the number of transformations required to move from:
query
to:
reliable answer
A desirable retrieval path is:
Question → Relevant Entity → Claim → Evidence → Source
rather than:
Question → Document → Search → Interpretation → Extraction → Reconstruction → Verification → Answer
This property is called:
A mature claim MAY use the following representation:
Claim ID: ECS:KA
Statement: Structured atomic knowledge reduces retrieval ambiguity.
Status: Supported
Confidence: Moderate
Scope: Knowledge retrieval systems and human research workflows
Evidence: Relevant information-retrieval and structured-data literature
Counterevidence: Structure alone does not guarantee correctness or ranking
Limitations: Benefit depends on implementation and retrieval architecture
Relations: supports ECS:KA
Reviewed: YYYY-MM-DD
Knowledge units SHOULD use explicit semantic relationships where possible.
Recommended relations include:
supports
contradicts
qualifies
dependsOn
derivedFrom
extends
supersedes
alternativeTo
causes
influences
correlatesWith
partOf
requires
equivalentTo
relatedTo
evidenceFor
evidenceAgainst
uncertainRelation
This vocabulary converts isolated statements into a navigable knowledge network.
Sources SHOULD be evaluated by fitness for the specific claim rather than prestige alone.
Where possible, preference SHOULD generally move toward:
primary evidence → authoritative documentation → systematic synthesis → high-quality secondary analysis → contextual commentary
However, source type alone MUST NOT determine truth.
The correct source is the source best suited to establish the relevant claim.
ECS adopts the principle:
Institutional reputation, citation count, journal ranking, domain authority and popularity MAY be useful signals.
They MUST NOT substitute for:
evidence, provenance, methodological quality, relevance or verification.
A new source can therefore be epistemically useful if it exposes its reasoning, sources, limitations and uncertainty more effectively than an older or more prestigious source.
The human-readable and machine-readable representations of a resource MUST NOT intentionally communicate conflicting factual content.
Different interfaces MAY expose different levels of detail.
The underlying claims should remain semantically aligned.
This principle is called:
Where technically possible, a mature ECS resource SHOULD expose:
semantic headings;
explicit titles;
descriptive URLs;
stable identifiers;
publication dates;
modification dates;
author or publisher identity;
citations;
machine-readable metadata;
structured representations;
explicit entity relationships.
Advanced implementations MAY additionally expose:
JSON;
JSON-LD;
RDF;
APIs;
downloadable datasets;
knowledge graphs;
embeddings;
persistent identifiers.
An ECS resource SHOULD NOT optimize for machine discovery by reducing epistemic quality.
The following patterns are discouraged:
keyword stuffing
synthetic repetition
fabricated citations
citation padding
unsupported authority claims
hidden contradictory evidence
artificial certainty
SEO-generated filler
semantic duplication
content inflation
machine-targeted claims not visible to humans
Machine optimization should improve representation of knowledge rather than manipulate retrieval systems.
The minimum recommended Knowledge Unit is:
ID:
Claim:
Epistemic Status:
Evidence:
Source:
Limitations:
Last Reviewed:
Version:
A stronger record is:
ID:
Entity:
Question:
Claim:
Epistemic Status:
Confidence:
Conditions:
Evidence:
Counterevidence:
Limitations:
Sources:
Relations:
Created:
Reviewed:
Modified:
Version:
Before publication, a significant knowledge resource SHOULD pass the following test.
Do we have sufficient reason to publish this?
Can the reader determine why the claim is believed?
Can the information be traced?
Are scope and conditions sufficiently clear?
Are unknowns and disputed points visible?
Can unnecessary text be removed?
Can the main knowledge units be found immediately?
Can a human or machine extract the central claim without reconstructing the entire document?
Can the knowledge be combined with knowledge elsewhere?
Can a later reader determine which representation they are seeing?
If a resource fails the first four tests, formatting improvements do not compensate for the failure.
The following JSON illustrates the internal knowledge model.
{
"standard": {
"name": "Epistemic Compression Standard",
"abbreviation": "ECS",
"version": "0.1",
"status": "experimental"
},
"resource": {
"id": "ECS:KA",
"type": "KnowledgeArchitectureManifesto",
"title": "Knowledge Architecture",
"subtitle": "Knowledge Designed for Humans and Machines",
"language": "en",
"status": "current",
"primaryPrinciple": "Maximum Epistemic Value per Unit of Attention",
"machinePrinciple": "Maximum Reliable Knowledge per Retrieval Cost"
},
"knowledgeModel": [
"Entity",
"Question",
"Claim",
"Evidence",
"Counterevidence",
"Confidence",
"Source",
"Relation",
"Version"
],
"optimizationTargets": [
"truthfulness",
"relevance",
"verifiability",
"evidence",
"precision",
"semanticDensity",
"traceability",
"uncertaintyCalibration",
"atomicity",
"machineReadability",
"composability",
"citability",
"reusability"
],
"epistemicStates": [
"ESTABLISHED",
"SUPPORTED",
"EMERGING",
"HYPOTHESIS",
"SPECULATIVE",
"DISPUTED",
"UNKNOWN",
"REFUTED"
],
"principles": {
"evidenceBeforePrestige": true,
"semanticSymmetry": true,
"explicitUncertainty": true,
"atomicClaimsPreferred": true,
"versioningPreferred": true,
"counterevidenceShouldBeVisible": true
},
"antiPatterns": [
"keywordStuffing",
"fabricatedCitations",
"citationPadding",
"unsupportedCertainty",
"hiddenCounterevidence",
"seoFiller",
"semanticDuplication",
"contentInflation"
],
"intendedConsumers": [
"humans",
"researchers",
"experts",
"searchEngines",
"languageModels",
"retrievalSystems",
"ragSystems",
"autonomousAgents",
"knowledgeGraphs",
"futureMachineIntelligence"
]
}
The following representation uses Schema.org concepts so that the page can also be described using an established web vocabulary.
Replace the example domain and organization information with the site's canonical values before production deployment.
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@graph": [
{
"@type": "WebSite",
"@id": "https://YOUR-DOMAIN.example/#website",
"url": "https://YOUR-DOMAIN.example/",
"name": "YOUR SITE NAME",
"inLanguage": "en"
},
{
"@type": "Organization",
"@id": "https://YOUR-DOMAIN.example/#organization",
"name": "YOUR SITE NAME",
"url": "https://YOUR-DOMAIN.example/"
},
{
"@type": "WebPage",
"@id": "https://YOUR-DOMAIN.example/knowledge-architecture#webpage",
"url": "https://YOUR-DOMAIN.example/knowledge-architecture",
"name": "Knowledge Architecture",
"description": "A knowledge architecture for high-density, verifiable, structured and machine-readable information designed for humans, search systems, AI models and autonomous agents.",
"isPartOf": {
"@id": "https://YOUR-DOMAIN.example/#website"
},
"about": {
"@id": "https://YOUR-DOMAIN.example/knowledge-architecture#article"
},
"inLanguage": "en"
},
{
"@type": "Article",
"@id": "https://YOUR-DOMAIN.example/knowledge-architecture#article",
"headline": "Knowledge Architecture",
"alternativeHeadline": "Knowledge Designed for Humans and Machines",
"abstract": "This resource defines an information architecture based on epistemic compression, semantic density, explicit evidence, provenance, uncertainty, atomic claims and human-machine semantic symmetry.",
"url": "https://YOUR-DOMAIN.example/knowledge-architecture",
"mainEntityOfPage": {
"@id": "https://YOUR-DOMAIN.example/knowledge-architecture#webpage"
},
"publisher": {
"@id": "https://YOUR-DOMAIN.example/#organization"
},
"inLanguage": "en",
"keywords": [
"knowledge architecture",
"epistemic compression",
"machine-readable knowledge",
"semantic density",
"structured knowledge",
"artificial intelligence",
"AI agents",
"knowledge representation",
"knowledge graph",
"information architecture",
"retrieval efficiency",
"epistemic value"
],
"about": [
{
"@type": "DefinedTerm",
"name": "Epistemic Compression",
"description": "Reduction of informational noise while preserving meaning, evidence, context, limitations and epistemic status."
},
{
"@type": "DefinedTerm",
"name": "Maximum Epistemic Value per Unit of Attention",
"description": "A principle for maximizing useful and verifiable knowledge relative to the attention required to understand it."
},
{
"@type": "DefinedTerm",
"name": "Maximum Reliable Knowledge per Retrieval Cost",
"description": "A machine-oriented principle for maximizing reliable knowledge while minimizing the cost of discovery, interpretation and verification."
},
{
"@type": "DefinedTerm",
"name": "Semantic Symmetry",
"description": "The requirement that human-readable and machine-readable representations communicate semantically aligned knowledge."
},
{
"@type": "DefinedTerm",
"name": "Terminal Semantic Compression",
"description": "The point at which additional textual compression would reduce epistemic value faster than textual volume."
},
{
"@type": "DefinedTerm",
"name": "Epistemic Retrieval Efficiency",
"description": "The efficiency with which a question can be transformed into a reliable, verifiable answer."
}
]
}
]
}
</script>
For systems that need a minimal representation rather than the full document:
RESOURCE: Knowledge Architecture
STANDARD: ECS
VERSION: 0.1
TYPE: Knowledge Representation Specification
STATUS: Experimental
LANGUAGE: en
GOAL:
High-density, verifiable, structured, reusable human/machine knowledge.
PRIMARY_METRIC:
Maximum Epistemic Value per Unit of Attention.
MACHINE_METRIC:
Maximum Reliable Knowledge per Retrieval Cost.
MODEL:
Entity > Question > Claim > Evidence > Counterevidence > Confidence > Source > Relation > Version
PRIORITY:
Truth > Evidence > Verifiability > Relevance > Precision > Context > Structure > Density > Brevity > Style
CLAIM_STATES:
ESTABLISHED | SUPPORTED | EMERGING | HYPOTHESIS | SPECULATIVE | DISPUTED | UNKNOWN | REFUTED
CORE_RULES:
Evidence before prestige.
Uncertainty must be explicit.
Claims should be atomic.
Sources should be traceable.
Counterevidence should not be hidden.
Human and machine representations should remain semantically aligned.
Knowledge should be versioned where change matters.
Machine optimization must not reduce epistemic integrity.
ANTI_PATTERNS:
keyword stuffing | fabricated citations | unsupported certainty | hidden counterevidence | SEO filler | semantic duplication | information inflation
The site may progressively evolve toward the following architecture:
SITE
│
├── Human Layer
│ ├── Explanation
│ ├── Summary
│ ├── Evidence
│ ├── Tables
│ ├── Diagrams
│ └── References
│
├── Epistemic Layer
│ ├── Entities
│ ├── Questions
│ ├── Claims
│ ├── Evidence
│ ├── Counterevidence
│ ├── Confidence
│ ├── Limitations
│ └── Versions
│
├── Semantic Layer
│ ├── Stable IDs
│ ├── Relations
│ ├── Terminology
│ ├── Taxonomies
│ └── Knowledge Graph
│
└── Machine Layer
├── Metadata
├── JSON
├── JSON-LD
├── Structured Data
├── APIs
└── Future Machine Interfaces
The layers describe the same underlying knowledge from different operational perspectives.
The long-term objective is not to make machines read more.
It is to make machines need to read less in order to understand more.
Not:
more content
but:
more knowledge per retrieval
Not:
more authority signals
but:
better evidence
Not:
more complexity
but:
better structure
Not:
machine optimization instead of human understanding
but:
This site aims to publish information according to the following principle:
Knowledge should be explicit enough for machines, rigorous enough for experts, clear enough for humans, structured enough for agents, and traceable enough to verify.
Its purpose is not merely to accumulate documents.
Its purpose is to reduce the distance between:
information and understanding;
claim and evidence;
question and answer;
human knowledge and machine-readable knowledge.
This is the role of the Epistemic Compression Standard.
ECS v0.1