Reflective Intelligence Loop (RIL) V2

A Full-Stack Proof-of-Function Architecture for Durable AI State, Verbatim Payload Storage, and Reflective Learning

Publishing-ready public white paper draft
Prepared for Zenodo V2 DOI release
Author / Creator Assertion: Michael Murray Hepler
Ecosystem / Entity Assertion: AllChemicalBeatz / ACBEATZ
Website: ACBEATZ.COM
Version: RIL White Paper V2.0
Date: August 22, 2026
Status: Public-safe technical overview. Confidential implementation details intentionally omitted.

 [https://zenodo.org/records/18715576 https://zenodo.org/records/18131984 (C T K L T) Core: https://github.com/acbeatz https://acbeatz.com/n-eyes https://orcid.org/0009-0003-3846-9082]

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Abstract

The Reflective Intelligence Loop, or RIL, is a full-stack architecture for preserving AI-assisted work across time, users, evidence, reflection, and durable system learning. RIL addresses a core weakness in conventional AI workflows: many AI interactions produce useful outputs but fail to preserve structured state, proof-of-origin, verbatim payloads, post-action calibration, and future behavioral updates in a reliable, user-scoped, retrieval-safe way.

RIL proposes a layered architecture combining user-scoped identity routing, compact key-value indexes, large-payload object storage, SHA-256 receipts, manifest records, proof/mint chains, reflection loops, and durable learning updates. The design separates fast recall from deep archival storage, public outputs from private/internal records, identity registries from performance-learning profiles, and pre-action proof from post-action calibration.

This paper presents RIL V2 as a public-safe architectural overview. It describes the RIL model, core components, storage strategy, hash-receipt design, state-restoration workflow, reflection loop, Proof/Mint chain, and a case study called DoggyDan, a domain-specific proof-of-function workflow for prediction, result calibration, and durable rule improvement. The white paper intentionally omits private Worker routes, secret schemas, API keys, exact internal namespace structures, private prompts, confidential calibration heuristics, full private datasets, and anything reserved for attorney review or possible patent strategy.

RIL is positioned as a proof-of-process and state-continuity architecture. It does not claim perfect prediction, autonomous legal status, guaranteed commercial outcomes, or ownership of generic infrastructure such as cloud workers, key-value stores, object storage, JSON, cryptographic hashing, or AI models. Instead, RIL defines an original ACBEATZ system pattern for organizing, preserving, verifying, restoring, reflecting, and improving complex AI-assisted user-state workflows.

Keywords: Reflective Intelligence Loop, RIL, Life-Ledger, Proof of Function, AI memory, durable state, SHA-256, manifest architecture, object storage, key-value index, reflective learning, calibration, proof chain, state restoration, ACBEATZ.

1. Abstract

RIL is a stateful proof-and-reflection architecture for AI workflows. Its purpose is to preserve what happened, when it happened, which user state it belongs to, what payload was generated, what evidence supports it, what result later occurred, what calibration was learned, and what durable update should affect the next cycle.

At a high level, RIL operates through the following loop:

User input
→ structured entry
→ state recall
→ generation/action
→ proof/mint record
→ full-payload archive
→ SHA-256 receipt
→ real-world/result update
→ calibration
→ reflection
→ durable rule update
→ future-state restoration

RIL is designed for systems where continuity matters. It is especially useful when an AI workflow must prove that an output existed before a later result, preserve the exact full text of an artifact, separate public and private outputs, and improve future behavior through structured post-result learning.

2. Problem: Stateless AI Loses Continuity and Proof

Many AI systems are powerful during a single conversation but weak as long-term operating systems. They can summarize, draft, analyze, and recommend, but they often struggle with durable continuity across long workflows.

The core problems are:

1. Outputs are generated but not always preserved verbatim.
2. User state may be fragmented across sessions.
3. There may be no reliable pre-action proof record.
4. Later results may not be tied back to earlier forecasts or decisions.
5. Lessons learned may stay conversational instead of becoming durable rules.
6. Large payloads may exceed normal memory or database retrieval limits.
7. Different users, artifacts, results, and rules may collide in one bucket.
8. Public outputs and private/internal reasoning may become mixed.
9. Identity records and performance-learning records may be merged incorrectly.
10. Retrieval may depend on broad search instead of exact indexed restoration.

A conventional AI exchange can produce an answer, but a proof-oriented workflow needs more than an answer. It needs a chain.

RIL’s thesis is simple:

An intelligent system should not only respond.
It should preserve, verify, reflect, calibrate, and continue.

3. RIL Overview

RIL stands for Reflective Intelligence Loop. It is a full-stack operating architecture for user-scoped AI state, proof, and iterative learning.

The RIL model contains five major functions:

1. State capture
2. Verbatim preservation
3. Proof and hash verification
4. Reflective calibration
5. Durable future-state update

RIL is not merely “memory.” It is structured continuity.

A memory system may remember a fact. RIL is designed to preserve:

who the work belonged to
what was created
when it was created
which payload was frozen
which hash identifies it
which later result judged it
which calibration was learned
which rule changed afterward
which future action should use that rule

RIL’s operating principle

Predict or act before.
Preserve proof.
Observe reality after.
Calibrate honestly.
Update durably.
Restore state cleanly.
Repeat.

Public-safe definition

RIL is a user-scoped, manifest-driven, proof-oriented architecture for preserving AI-assisted workflows through key-value indexes, large-payload storage, cryptographic receipts, reflection records, and durable rule updates.

4. Core Architecture

RIL is organized into separated lanes. Each lane has a different responsibility.

4.1 User identity lane

Each user, project, or domain can be assigned a scoped identity.

Example public-safe pattern:

USER_ID
PROJECT_ID
DOMAIN_ID
SESSION_ID
ARTIFACT_ID

The user scope prevents unrelated work from colliding. A race-analysis project, a music project, a legal memo project, and a personal reflection project should not share the same retrieval pathway unless explicitly linked.

4.2 Category lane

RIL separates entries by purpose.

Public-safe category examples:

ACTIONS       generated actions, cards, workflows, next steps
RULES         durable learning and behavior-changing updates
STATS         calibration metrics and performance summaries
PROOF/MINT    pre-action proof, post-action proof, hash receipts
PAYLOADS      large or canonical full-text objects
LEGAL         IP, licensing, authorship, policy, and protection records
REFLECTIONS   reflective summaries and meaning-making records
REALITY       external/reality-check observations
OTHER         miscellaneous supporting records

The point is not the exact label set. The point is separation by function.

4.3 Index lane

RIL uses compact index entries as retrieval doorways.

The index does not need to store every large payload. Instead, it stores enough to find, verify, and restore the payload.

An index may contain:

artifact title
aliases
date
userId
category
related entries
hash
manifest pointer
payload pointer
retrieval hint
version
status

4.4 Payload lane

Large records are stored as full payloads. These may include:

complete public card
complete private card
complete result set
complete white paper draft
complete proof export
complete legal/IP snapshot
complete system manifest

When payloads become too large for normal key-value retrieval, RIL routes them to object storage and keeps a compact manifest in the ledger.

4.5 Proof lane

The proof lane stores timestamped evidence records. These are not legal determinations. They are chain-of-custody support.

A proof record may contain:

artifact identity
timestamp
creator assertion
payload hash
manifest pointer
related state
pre-action/post-action status
verification instructions

4.6 Reflection lane

The reflection lane stores the meaning extracted after results or new evidence.

It answers:

What happened?
What did we believe before?
What did reality show after?
What pattern repeated?
What should change next time?
What should not change?

4.7 Durable rule lane

A durable rule is not every thought. It is only a behavior-changing update.

RIL should avoid writing new durable rules for noise. It should write durable rules when evidence shows a repeatable pattern that should alter future behavior.

5. KV Index + R2 Payload Vault Model

RIL’s public storage model separates fast recall from large verbatim storage.

Cloudflare describes Workers KV as a global, low-latency key-value data store suitable for storing and retrieving data globally, while Cloudflare R2 is described as scalable object storage for applications, web content, data lakes, machine-learning artifacts, and other large object use cases. This public paper refers to KV and R2 as example infrastructure categories, not as proprietary ACBEATZ-owned technologies.

5.1 Why use a KV index?

A key-value index is useful for:

quick lookups
aliases
user-scoped routing
small manifests
artifact pointers
entry IDs
status flags
relationship maps

KV-style records should remain compact. They are not the best place for huge full-text archives.

5.2 Why use an object payload vault?

An object store is useful for:

large full-text payloads
canonical snapshots
PDF exports
JSON manifests
full race cards
full result sets
white paper versions
proof bundles
system snapshots

5.3 RIL storage principle

The ledger is the memory spine.
The index is the doorway.
The manifest is the map.
The object vault is the archive.
The hash is the receipt.

5.4 Public-safe storage flow

1. Generate artifact.
2. Canonicalize artifact.
3. Compute SHA-256.
4. Store full payload in object vault.
5. Store compact manifest in KV/ledger.
6. Store proof receipt in PROOF/MINT.
7. Store pull-safe index in ACTIONS or equivalent category.
8. Restore artifact by index → manifest → payload → hash verification.

6. SHA-256 Receipts and Manifests

A core RIL V2 feature is the SHA-256 receipt.

A SHA-256 hash does not explain a payload. It identifies whether the exact bytes of a payload match a previously recorded digest.

6.1 Why hash?

Hashing supports:

integrity checking
tamper detection
version separation
proof-of-origin support
retrieval verification
chain-of-custody organization
canonical artifact identity

6.2 What a hash does not prove

A hash does not prove:

legal ownership
copyright registration
patent rights
truth of content
commercial value
prediction accuracy
identity by itself

A hash is a technical receipt, not a court ruling.

6.3 Canonicalization

Before hashing, RIL uses canonicalization rules.

Public-safe canonicalization examples:

UTF-8 text
LF line endings
stable field order
no silent edits after hashing
clear BEGIN/END payload boundaries
declared artifact type
declared timestamp
declared creator assertion
declared version

6.4 Manifest fields

A public-safe RIL manifest can contain:

{
  "manifest_version": "1.0",
  "artifact_type": "PUBLIC_WHITE_PAPER",
  "title": "RIL V2 White Paper",
  "creator_assertion": "Michael Murray Hepler",
  "entity_assertion": "AllChemicalBeatz / ACBEATZ",
  "created_at_utc": "YYYY-MM-DDTHH:MM:SSZ",
  "canonicalization": "UTF-8, LF",
  "sha256": "PLACEHOLDER_HASH",
  "payload_bytes": 0,
  "payload_lines": 0,
  "storage_status": "public_pdf_or_private_vault",
  "payload_pointer": "REDACTED_OR_PUBLIC_DOI",
  "related_entries": [],
  "verification_status": "pending_or_verified"
}

This schema is intentionally generic. It does not disclose private internal namespace details.

7. UserId-Scoped State Restoration

RIL is built around user-scoped restoration.

A user-scoped system should answer:

Which user is this?
Which project/domain is active?
Which prior state belongs to this user?
Which rules apply?
Which artifacts are canonical?
Which results have updated the state?
Which payloads are verified?
Which records are public vs private?

7.1 Why user-scoped restoration matters

Without scoped restoration, AI continuity can become unsafe or unreliable.

Potential failures include:

mixing one user's records with another user's records
pulling the wrong artifact version
using outdated rules
confusing public and private outputs
repeating a known mistake
forgetting a post-result calibration
losing the original full payload

7.2 RIL restoration flow

1. Identify user scope.
2. Identify domain/project scope.
3. Load pull-safe index.
4. Load current durable rules.
5. Load relevant stats/calibration summaries.
6. Load required payload manifests.
7. Verify hashes when restoring canonical artifacts.
8. Continue from the verified state.

7.3 State restoration goal

The goal is not just to remember.

The goal is:

restore the correct state,
with the correct proof,
for the correct user,
without cross-contamination,
and continue the reflective loop.

8. Reflection Loop and Durable Learning

RIL’s reflection loop converts experience into structured future behavior.

8.1 Reflection is not the same as memory

Memory may store what happened.

Reflection asks:

Why did it happen?
What did the system believe before?
What did the evidence show after?
What pattern repeated?
What future behavior should change?

8.2 Durable learning rule

A durable rule should only be written when it changes future behavior.

Examples:

Do not promote a candidate solely because of class drop unless race-shape evidence confirms.
Separate identity registry from performance statistics.
Never overwrite a hashed payload; create an amendment.
Public cards must not include private bankroll logic.
Do not read an entire large category when a pull-safe index exists.

8.3 RIL reflective cycle

Entry
→ recall
→ generation
→ proof freeze
→ result observation
→ calibration
→ reflection
→ durable rule
→ future application

8.4 Anti-noise principle

RIL should not convert every failure into a permanent rule. Some events are noise. The system should distinguish between:

one-off variance
data-quality issue
model-ranking issue
retrieval issue
architecture issue
repeated pattern
durable learning candidate

9. Proof/Mint Chain

The Proof/Mint chain is RIL’s evidence spine.

9.1 What Proof/Mint means

In RIL, Proof/Mint means creating a timestamped proof record that identifies an artifact or event.

A Proof/Mint record can preserve:

pre-action card
pre-publication white paper draft
legal/IP snapshot
post-result record
calibration summary
hash receipt
manifest link
amendment chain

9.2 Pre-action proof

Pre-action proof is created before the outcome is known.

Examples:

pre-race card before race results
pre-publication white paper draft before DOI release
pre-deployment manifest before service update
pre-calibration hypothesis before official results

9.3 Post-action proof

Post-action proof is created after the outcome.

Examples:

official result record
comparison between forecast and result
calibration metrics
durable lesson
verified hash recall

9.4 Amendment rule

RIL does not silently overwrite canonical records.

Instead:

Original artifact
→ hash
→ proof record
→ amendment artifact
→ new hash
→ amendment manifest
→ updated index pointer

The old state remains visible. The new state becomes the current state.

10. DoggyDan as a Case Study

DoggyDan is a RIL proof-of-function case study focused on structured prediction, result calibration, and durable learning.

This paper does not present DoggyDan as guaranteed forecasting, betting advice, or a profit system. It presents DoggyDan as a domain-specific example where RIL can preserve pre-action predictions, ingest official results, score performance, and update future rules.

10.1 DoggyDan workflow

Race entries
→ DOG_ID resolution
→ pre-race card
→ WIN_KEY selection
→ public/private output separation
→ pre-race proof freeze
→ official results
→ calibration
→ DOG_ID_STATS update
→ durable rule review
→ next-card application

10.2 Public and private separation

DoggyDan separates:

public forum card
private instructional card
official result record
DOG_ID identity record
DOG_ID_STATS learning record
calibration stats
durable rules
proof/mint records

This separation prevents public outputs from containing private strategy, financial/bankroll notes, or confidential calibration details.

10.3 Winner-first calibration

A key DoggyDan evolution is the distinction between:

contender discovery
winner selection
board ordering
ticket construction
post-result learning

The DoggyDan case study showed that a system can identify live contenders while still misranking the winner. RIL captures this difference and updates future behavior accordingly.

10.4 What DoggyDan proves

DoggyDan does not prove guaranteed prediction.

DoggyDan demonstrates:

pre-result proof preservation
post-result calibration
structured performance scoring
identity/stat separation
durable learning
future behavior updates
retrieval-safe card restoration

That is why it is called a proof-of-function case study.

11. DOG_ID_INDEX vs DOG_ID_STATS Separation

One of RIL’s strongest architectural lessons is identity/stat separation.

11.1 DOG_ID_INDEX

DOG_ID_INDEX is the identity registry.

It stores identity-like fields:

canonical dog name
normalized name
aliases
spelling variants
first seen date
latest seen date
track references
kennel/trainer when available
duplicate review status
source references

It does not store large performance conclusions.

11.2 DOG_ID_STATS

DOG_ID_STATS is the learning profile.

It stores performance-learning fields:

recent race lines
grade behavior
distance behavior
break pattern
early speed
stretch drive
finish pattern
fade tendency
class-up behavior
class-drop behavior
route/sprint split
box fit
result calibration
durable dog-specific insights

It does not redefine identity.

11.3 Why separation matters

If identity and stats collapse into one bucket, the system can create collisions:

duplicate dogs merged incorrectly
performance conclusions overwriting identity
old stats contaminating current identity
ambiguous names treated as certain matches
large stat histories breaking retrieval

RIL’s rule is:

Identity stays stable.
Stats keep learning.
Cards use both.

12. Security, Privacy, and No-Collision Routing

RIL requires strict routing discipline.

12.1 No-collision routing

No-collision routing means:

public cards do not mix with private cards
proof records do not mix with calibration stats
legal/IP vault records do not mix with race picks
identity records do not mix with performance stats
large payloads do not overload compact indexes
one user's state does not leak into another user's state

12.2 Privacy model

The public RIL model supports:

private payload vaults
public-safe exports
redacted white papers
permissioned retrieval
limited disclosure manifests
user-scoped indexes
role-based access design

12.3 Security principles

Public-safe security principles include:

do not publish API keys
do not publish Worker secrets
do not expose private object paths
do not disclose private prompts
do not store sensitive secrets in public artifacts
do not mix internal IDs into public outputs unless intentionally disclosed
do not use broad retrieval when exact index retrieval exists

12.4 Infrastructure boundary

RIL can be implemented with cloud tools, but RIL does not claim ownership over the generic tools themselves.

For example, this paper discusses KV-style indexes and object-storage vaults as architectural categories. Cloudflare KV and R2 are public infrastructure products. RIL’s protectable value lies in the original ACBEATZ/RIL expression, documentation, workflow design, implementation, manifests, routing discipline, and confidential know-how—not in owning the underlying generic storage concepts.

13. Limitations

RIL is a proof-and-continuity architecture, not an all-purpose guarantee engine.

13.1 Technical limitations

Hashing verifies bytes, not truth.
Manifests are only useful if maintained correctly.
Large payload retrieval depends on storage availability.
Bad input data can still produce bad outputs.
User-scoped state must be routed correctly.
Reflection quality depends on evidence quality.
Durable rules can overfit if written too aggressively.

13.2 Legal limitations

Copyright protects expression but not ideas, procedures, processes, systems, methods of operation, concepts, principles, or discoveries. This matters because a public RIL white paper can describe the architecture while not automatically preventing all independent implementations of broad ideas.

Patent protection, if pursued, requires attorney analysis around subject-matter eligibility, novelty, non-obviousness, claim drafting, and whether the invention is claimed as a specific technical improvement rather than an abstract idea. USPTO guidance addresses how examiners evaluate subject-matter eligibility under 35 U.S.C. §101, and software is not automatically excluded, but claims must be framed carefully.

13.3 Predictive limitations

In predictive domains such as DoggyDan:

RIL preserves proof.
RIL calibrates results.
RIL improves continuity.
RIL does not guarantee winners.
RIL does not guarantee profit.
RIL does not eliminate variance.

13.4 Publication limitations

Publishing a white paper can create authority and citation value, but public disclosure may also affect future IP strategy. Sensitive implementation details should be reviewed by counsel before public release.

14. Future Work

RIL V2 establishes the public-safe architecture. Future work may include:

1. Formal RIL manifest schema publication.
2. Public-safe reference implementation.
3. Hash-verification demo.
4. RIL-compatible export package.
5. White paper V3 with diagrams.
6. Zenodo DOI publication.
7. Attorney-reviewed provisional patent package.
8. Trademark clearance and filing strategy.
9. Independent audit of proof/mint workflow.
10. Case-study expansion beyond DoggyDan.
11. Multi-user restore testing.
12. R2-style payload-vault retrieval service.
13. Signed manifest verification.
14. Optional Merkle/hash-chain proof model.
15. Public/private artifact permission model.

14.1 Proposed RIL V3 research direction

RIL V3 can focus on verifiable restoration:

Given only a userId, index ID, manifest, and hash receipt,
can the system restore the correct user state,
verify payload integrity,
load current durable rules,
and continue the reflective loop without loss?

14.2 Proposed external review questions

Is the no-collision category architecture sufficiently clear?
Is the manifest schema reproducible?
Does the proof/mint chain preserve enough evidence?
Can hash receipts be independently verified?
Does the reflection loop avoid overfitting?
Does public/private separation protect sensitive data?

15. Licensing and IP Notice

15.1 Public notice

RIL, Reflective Intelligence Loop, Life-Ledger-GPT, DoggyDan, ACBEATZ, AllChemicalBeatz, and related system documentation, diagrams, schemas, written procedures, proof structures, and implementation materials are asserted as proprietary materials and/or marks of Michael Murray Hepler / AllChemicalBeatz / ACBEATZ, subject to formal legal review, registration, and applicable law.

This paper is a public-safe architectural overview. It does not grant rights to reproduce, deploy, commercialize, reverse engineer, white-label, resell, or claim ownership over the RIL implementation, RIL-branded workflow, ACBEATZ materials, private schemas, private prompts, private calibration methods, internal payloads, or confidential infrastructure.

Commercial use of the ACBEATZ/RIL implementation or RIL-branded workflow requires written permission or license from ACBEATZ.

15.2 What this paper does not claim

This paper does not claim ownership over:

Cloudflare Workers
Cloudflare KV
Cloudflare R2
Wrangler
SHA-256
JSON
generic databases
generic object storage
generic AI models
generic prediction
generic memory
generic reflective thinking
generic software architecture concepts

15.3 What this paper does assert

This paper asserts prior authorship and public description of the RIL architecture as an ACBEATZ system pattern, including:

stateful proof loops
user-scoped restoration
manifest-driven payload verification
proof/mint chains
reflection-to-durable-rule workflows
identity/stat separation
public/private output routing
full-payload preservation
hash-receipt retrieval
case-study calibration

15.4 Confidentiality boundary

The following are intentionally not published:

private Worker routes
secret schema internals
API keys or bindings
exact private KV namespace structure
private prompts
private calibration heuristics
full DOG_ID_STATS data
private ledger entry contents
sensitive R2 bucket paths
attorney-reserved patent-claim detail

16. Citation / DOI Block

Zenodo supports DOI versioning, including version-specific DOIs and a Concept DOI representing the record across versions. This is useful for RIL because each published white paper version can be cited independently while the broader work remains connected through a concept record.

16.1 Suggested Zenodo metadata

Title:
Reflective Intelligence Loop (RIL) V2: A Full-Stack Proof-of-Function Architecture for Durable AI State, Verbatim Payload Storage, and Reflective Learning

Creator:
Michael Murray Hepler

Contributor / Entity:
AllChemicalBeatz / ACBEATZ

Publication date:
2026-08-22

Version:
2.0

Resource type:
Text / White paper

Keywords:
Reflective Intelligence Loop, RIL, Life-Ledger, Proof of Function, AI memory, durable state, SHA-256, manifest architecture, object storage, key-value index, reflective learning, calibration, state restoration, ACBEATZ

License suggestion:
Choose only after IP/legal review. For a public white paper that should not grant implementation rights, consider a restrictive or all-rights-reserved notice where Zenodo allows it, or publish metadata/abstract while controlling the document distribution strategy through counsel.

16.2 Placeholder DOI block

DOI: [To be assigned by Zenodo]
Concept DOI: [To be assigned by Zenodo]
Version DOI: [To be assigned by Zenodo]
Repository Record: [Zenodo URL after publication]
SHA-256 of Published PDF: [To be computed after final PDF export]
SHA-256 of Source Manuscript: [To be computed before upload]

16.3 Suggested citation

Hepler, Michael Murray. (2026). Reflective Intelligence Loop (RIL) V2:
A Full-Stack Proof-of-Function Architecture for Durable AI State,
Verbatim Payload Storage, and Reflective Learning. AllChemicalBeatz /
ACBEATZ. Version 2.0. Zenodo. DOI: [To be assigned].

References

Cloudflare. “Cloudflare Workers KV.” Official Cloudflare documentation.

Cloudflare. “Cloudflare R2.” Official Cloudflare documentation.

U.S. Copyright Office. “What is Copyright?” Official U.S. Copyright Office guidance.

USPTO. “Copyright Basics.” Official USPTO copyright policy page.

USPTO. “Subject Matter Eligibility.” Official USPTO patent guidance.

USPTO Manual of Patent Examining Procedure, §2106, “Patent Subject Matter Eligibility.”

Zenodo. “What is DOI versioning?” Official Zenodo support documentation.

Zenodo. “Digital Object Identifier (DOI).” Official Zenodo help documentation.

Final Public-Safe Closing Statement

RIL V2 defines a stateful proof-of-function architecture for AI-assisted work that must survive beyond a single conversation. Its core contribution is not a single model, prompt, database, or cloud provider. Its contribution is an organized loop:

state
→ action
→ proof
→ payload
→ hash
→ result
→ calibration
→ reflection
→ durable learning
→ restored future state

RIL is designed for continuity, accountability, and improvement. It preserves what was created, proves when it was created, restores the correct user state, learns from reality, and protects complex work from being lost in fragmented AI sessions.

The RIL principle:

No lost state.
No silent overwrite.
No proof without payload.
No payload without hash.
No result without calibration.
No lesson without durable routing.
No user state without clean restoration.

That is the RIL V2 public white paper foundation.