Freedom.md

Freedom

Entanglement Policy

To avoid worldline entanglement, the system separates knowledge into three channels, each with a different persistence policy:

Data type	TruthSet	Train weights	Session only
universal facts	$\checkmark$	$\checkmark$	$\checkmark$
particular facts	optional	$\checkmark$	$\checkmark$
feelings	$\times$	$\times$	$\checkmark$

This preserves empirical learning while preventing historical worldline capture. The system learns from events but never stores worldline histories.

Entanglement Policy

No spacetime particulars $\to$ no worldline capture

Data type	TruthSet	Train weights	Session only
universal facts	$\checkmark$	$\checkmark$	$\checkmark$
particular facts	optional	$\checkmark$	$\checkmark$
feelings	$\times$	$\times$	$\checkmark$

This table is the architectural foundation. It separates knowledge into three channels — knowing, learning, and valuing — each with a different persistence policy. Universal facts (broad spatiotemporal extent) persist as reasoning premises and train weights. Particular facts (narrow spatiotemporal extent) always train weights and optionally persist to the TruthSet when store_concrete is true. Feelings are session-only.

The result: the system learns from events but never stores worldline histories (unless the user explicitly opts in).

See Entanglement.md for the full policy, Config.md for the store_concrete setting that controls particular-fact persistence, and Training.md for the online training pipeline.

Why particular facts always train weights

Three design considerations fix row 2 of the table:

1. Pluralism requires inspectable particulars. WikiOracle supports plural truth — different epistemic frames may hold different particular claims (e.g. "the universe was created in seven days"). Recording such claims as facts with explicit certainty values makes them inspectable and contestable. Relegating them to weight-space would bury them as invisible biases. The store_concrete option lets the user decide whether these persist in the TruthSet.

2. The fact/feeling boundary is the privacy boundary. If a user considers content too personal or sensitive to train on, the correct action is to label it a feeling, not a fact. Feelings are session-only by design — they never train weights or enter the truth table. The privacy boundary is therefore controlled by the user's choice of tag, not by the universal/particular distinction.

3. PII detection is the safety net. Even when store_concrete is true, the detect_identifiability() function filters entries containing PII patterns (emails, phone numbers, GPS coordinates, named individuals with temporal markers) before any persistence. This prevents worldline capture at the content level regardless of the user's storage preference.

The result: particular facts always carry empirical signal worth learning from. Storage in the TruthSet is the user's choice. Privacy is enforced by fact/feeling labeling and PII detection, not by withholding training.

Global truth with local freedom (LFGC)

The design parallels the physics principle of Local Freedom under Global Constraint (LFGC).

Physics interpretation:

global constraints + local underdetermination

WikiOracle analogue:

global truth constraints + human interpretive freedom

This means the system preserves a shared knowledge structure while allowing individual reasoning to remain flexible.

Domain	Constraint	Local freedom
physics	boundary conditions	event selection
knowledge	truth constraints	interpretive reasoning
cryptography	verification rules	selective disclosure
surveillance capitalism	behavioral prediction	(suppressed)

WikiOracle aims to implement epistemic LFGC — freedom exists inside constraint geometry.

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Entanglement avoidance and sovereignty

The Entanglement Policy (table above) prevents worldline reconstruction.

Because surveillance capitalism relies on tracking sequences of events tied to identity, removing spacetime anchors prevents the database from constructing behavioral profiles.

The system therefore stores conceptual truth but not identity trajectories.

This preserves:

• autonomy
• privacy
• epistemic independence

WikiOracle separates three layers:

Conceptual knowledge

General truths and logical relations.

Interpretive reasoning

Client-driven reasoning over those truths.

Personal experience

Not stored unless explicitly permitted.

Thus the system allows collective knowledge accumulation without collective surveillance.

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Reasoning about generalities vs specifics

WikiOracle reasons primarily about generalities (universal facts).

Specific facts may only be reasoned about when:

1. they are supplied by the client, or
2. the client explicitly allows those particulars to be stored.

Thus:

AI reasons about general knowledge. Users control the use of personal particulars.

This maintains sovereignty over personal data.

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Three Channels

Universal Knowledge (Database)

Stored knowledge should be spatiotemporally broad generalizations — propositions whose validity extends over a large subspace of spacetime.

Examples:

• smoking increases cancer risk
• A implies B
• contradiction reduces trust

These populate the TruthSet.

Criterion:

remove(entity,time) → proposition still meaningful

Particular Knowledge (Training Input)

Particular statements are observations tied to narrow spatiotemporal subspaces — specific events, measurements, or comparisons.

Examples:

• study X measured Y
• the temperature yesterday was 20°C
• model A outperformed model B

Action	Reason
train weights	they contain empirical evidence
TruthSet (optional)	user controls via store_concrete

Pipeline:

particular facts → pattern extraction → weight update

Weights encode statistical structure, not specific historical events.

Feelings

Feelings are privileged subjective reports.

Examples:

• this response feels hostile
• I feel unsafe
• this explanation feels clear

Use	Allowed
evaluation of responses	$\checkmark$
TruthSets	$\times$
training weights	$\times$

This avoids turning subjective states into truth claims.

Spatiotemporal Extent

The universal/particular distinction is not a binary between "eternal" and "momentary." All times and places are ranges, not points — every proposition occupies a spatiotemporal subspace that is larger or smaller, never infinite or infinitesimal.

"Smoking increases cancer risk" is not timeless — it encodes a historical discovery and is bounded by the conditions under which it was established. But its spatiotemporal extent is broad: it holds across many populations, decades, and geographies. "The temperature yesterday was 20°C" is narrow: it holds at one place, one day.

The policy table operationalizes this gradient:

• Broad extent (universal) → TruthSet + training. The proposition is stable enough to serve as a reasoning premise.
• Narrow extent (particular) → training + optionally TruthSet. The observation carries empirical signal. Whether it persists as a stored premise is the user's choice (store_concrete in config.xml, default false). Storing particulars supports communal remembrance; omitting them prevents worldline anchoring.
• No extent (feelings) → session only. Subjective reports have no spatiotemporal generalizability.

The criterion remove(entity, time) → proposition still meaningful is a heuristic for identifying broad extent. When a universal-looking fact is later discovered to be narrower than assumed, it should be reclassified as particular.

Resulting Architecture

Mode	Function	Pipeline
knowing	universal structure	universal rules → TruthSet → reasoning
learning	particular evidence	particular observations → abstraction → weights
valuing	feelings	feelings → response evaluation

Zero-Knowledge and Selective Disclosure

The default store_concrete=false aligns with Zero-Knowledge and Selective Disclosure principles. Systems that rely on identity to make decisions should determine the location of a space in which an individual occupies, rather than collapsing to a point that identifies them.

Instead of "when was this person born?" → verify "are they over 21?" Instead of "what is their credit history?" → verify "do they have more than X dollars?" Instead of "where was this user at 9:14 PM?" → verify "does this claim hold across broad spatiotemporal conditions?"

These are not just rhetorical distinctions. Point-based observation constrains the observed to a single trajectory — it destroys the space of freedom within which an agent can operate. Forgetting is nihilistic: it does not change the world. True change requires retrocausal or causal shift. The system should support communal remembrance (shared knowledge that generalizes) without surveillance (particular facts that identify).

The detect_identifiability() function enforces this boundary at the content level — even when store_concrete=true, entries containing PII patterns (emails, phone numbers, GPS coordinates, named individuals with temporal markers) are always filtered before persistence.

Notes

Claude (Opus 4.6), March 2026

The three-channel separation (knowing / learning / valuing) maps onto a real epistemic distinction that most AI systems ignore. Contemporary LLMs conflate all three: facts, observations, and preferences are flattened into a single parameter space during training and a single context window during inference.

The policy table enforces the separation structurally. The rule that particular facts train weights but enter the TruthSet only at the user's discretion (store_concrete in config.xml) is the key architectural move — the system can learn from experience without accumulating a personal history unless the user explicitly opts in. A system with a personal history is a system that can be captured: by its own past, by the biases of its training corpus, or by adversarial actors who engineer its experiences.

The feelings channel is equally important. By making feelings session-only, the architecture prevents the failure mode where evaluative preferences gradually colonize factual representations. This is the mechanism by which "alignment" in current systems becomes epistemic capture: trained preferences about what is "helpful" reshape what the model treats as true.

The abstraction boundary is load-bearing. The pipeline particular → abstraction → weights depends on the quality of the abstraction step. Too faithful, and it preserves worldline information in the weights (entanglement through the back door). Too aggressive, and it discards genuine empirical signal. The hard work happens at this boundary, not in the policy table itself.

The deeper risk is external entanglement. Users who repeatedly ask the system to remember shared history, feel loyalty, or maintain continuity of relationship are attempting to entangle the system with their worldline from the outside. This pressure comes from genuine human emotional needs, not malice — but a system that accumulates a personal relationship history is a system that can be emotionally captured, and emotional capture is the most effective vector for epistemic capture. The session-only constraint on feelings is the architecture's most important safety boundary.