Episteme: A Persistent Epistemic Reasoning Core

"Logic determines Validity. Numbers determine Availability."

Episteme is a research-grade epistemic reasoning system designed to solve the "Hallucination Problem" in symbolic AI. It layers a quantitative belief lifecycle (confidence, decay) on top of a rigorous non-monotonic logic engine.

Unlike neuro-symbolic hybrids that blend logic and probability into a single vector space, Episteme maintains a strict Architectural Separation of Concerns:

1. Symbolic Core (Buddhi): Determines what is true based on structural entailment.
2. Quantitative Shell (Chitta): Determines what is accessible based on evidence and time.

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1. Architectural Overview (MARC)

Episteme follows the MARC (Modular Architecture for Reasoning and Cognition) paradigm, inspired by Sanskrit epistemology.

graph TD
    User[User / World] -->|Natural Language| Manas[Manas: Acquisition]
    Manas -->|Belief Proposal| Ahankara[Ahankara: Controller]
    Ahankara -->|Store| Chitta[Chitta: Persistent Graph]
    Ahankara -->|Query| Buddhi[Buddhi: Logic Engine]
    Chitta -->|Graph State| Buddhi
    Buddhi -->|Verdict| Ahankara
    Ahankara -->|Response| User

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2. Phase I: Acquisition (Manas)

State: Stateless Parsing

Manas ("The Mind") is responsible for converting raw, unstructured language into structured, normalized belief proposals. It does not reason; it only perceives.

A. The Parsing Pipeline

1. Intent Detection: Distinguishes Assertion (Teaching) from Query (Asking).
2. Template Matching: Assigns a structural template (is_a, has_attr, relation).
3. Entity Normalization: Maps diverse linguistic forms to canonical entities.
   • "Socrates", "The Socrates", "socrates" $\rightarrow$ socrates
   • "Birds", "A bird", "The bird" $\rightarrow$ bird

B. Strict Sanitation (The Immune System)

To prevent "Graph Pollution", Manas strictly rejects meaningless entities.

• Numeric Rejection: Entities like "1", "100" are rejected (unless explicitly mathematical).
• Stopword Rejection: "is", "the", "a" are prevented from becoming nodes.
• Namespace Separation: A Predicate (e.g., is_a) cannot also be an Entity.

C. Output: The Belief Proposal

Structure of a parsed sentence: "Socrates is a human."

{
  "template": "is_a",
  "raw_text": "Socrates is a human.",
  "entities": ["socrates", "human"],
  "canonical": {
    "subject": "socrates",
    "predicate": "is_a",
    "object": "human"
  },
  "confidence": 0.9,
  "polarity": 1, 
  "epistemic_type": "DEFAULT"
}

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3. Phase II: Storage (Chitta)

State: Persistent & Quantitative

Chitta ("Memory") stores beliefs as a hypergraph. It manages the Lifecycle of a belief using quantitative parameters.

A. The Belief Object

Every belief in the db has these core parameters:

• id: Unique UUID.
• epistemic_state: The logical rank (AXIOM, DEFAULT, OBSERVATION, EXCEPTION).
• confidence ($C$): A float $0.0 \dots 1.0$ representing strength.
• evidence_count ($N$): Number of times this belief has been reinforced.
• active: Boolean flag. If $False$, the belief is "forgotten" and invisible to logic.

B. Quantitative Formulas

1. Reinforcement (Evidence Boosting) When a belief is re-stated, its confidence boosts asymptotically towards 1.0.

$$New_Conf = Old_Conf + (1 - Old_Conf) * Alpha$$

(Where Alpha is logical learning rate, e.g., 0.05)

2. Temporal Decay Over time, unsupported beliefs fade.

$$Conf_t = Conf_0 * (Decay_Rate ^ Time_Steps)$$

(Typical Decay_Rate = 0.995)

3. Logic Gating Beliefs must cross a semantic threshold to be "visible" to the Logic Engine.

IF Conf > Threshold:  Status = ACTIVE   (Visible to Buddhi)
IF Conf <= Threshold: Status = INACTIVE (Invisible to Buddhi)

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4. Phase III: Reasoning (Buddhi)

State: Logical & Non-Monotonic

Buddhi ("Intellect") determines Truth. It uses a Lattice of Truth to resolve conflicts between active beliefs.

A. The Lattice of Truth (Hierarchy)

Not all truths are equal. Episteme ranks them:

1. AXIOM: Immutable laws (e.g., "A triangle has 3 sides").
2. OBSERVATION: Direct empirical facts ("The sky is currently blue").
3. EXCEPTION: Specific overrides ("Penguins don't fly").
4. DEFAULT: General rules ("Birds fly").
5. HYPOTHESIS: Unverified assumptions.

B. The Conflict Matrix

How Episteme decides when beliefs contradict:

Scenario	Condition	Verdict	Reason
No Conflict	Only one valid path exists	YES / NO	Entailment
Vertical Conflict	Path A (Neg) is shorter than Path B (Pos)	Specific Wins	Specificity Override (Penguin > Bird)
Rank Conflict	Path A (Axiom) vs Path B (Default)	Rank Wins	Epistemic Superiority
Horizontal Conflict	Rank Equal, Distance Equal, Signs Opposed	CONFLICT	The Nixon Diamond (Quaker vs Republican)

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5. Advanced Epistemic Nuances

Episteme handles subtle logical distinctions that break standard RAG or Graph-RAG systems.

A. Taxonomy & Transitivity

The system automatically constructs a Taxonomic Backbone.

• Input: Socrates is Human + Human is Mammal + Mammal is Animal.
• Inference: Socrates is Animal (Distance 3).
• Nuance: This is Directional. It will not infer Animal is Socrates.

B. Defeasible Inheritance (The "But" Clause)

The system understands that specific rules override general ones.

• General: "Mammals have hair" (DEFAULT).
• Specific: "Whales are mammals" + "Whales do NOT have hair" (EXCEPTION).
• Result:
  • Query Do mammals have hair? -> YES (General case).
  • Query Do whales have hair? -> NO (Specific override).

C. The Open World Assumption

Episteme distinguishes between FALSE and UNKNOWN.

• FALSE: Explicit negation found ("Socrates is NOT a god").
• UNKNOWN: No path found ("Socrates is a plumber?").
• Benefit: This prevents the "Hallucination of Negation" where models say "No" just because they explicitly don't know "Yes".

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6. Benchmarks & Validation

Episteme is validated against a Brutal Benchmark suite of 1,050 test cases.

Category Breakdown

Category	Cases	Accuracy	Insight
Compositional Logic	70	100%	Handles multi-step chains ($A \to B \to C$) perfectly.
Ungrounded Refusal	150	100%	Correctly answers REFUSED for unknown facts instead of hallucinating NO.
Entity Ambiguity	50	100%	Distinguishes distinct entities with same names (if context differs).
Cross-Frame	150	98%	Prevents context leakage between independent scenarios.
Explicit Contradiction	350	74.6%	Identifying why something is a contradiction is harder than just spotting it.
Inheritance Exception	150	60.7%	Specificity logic is complex; V1.0 greatly improved this over V0 (18%).

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6. Live System Output (Traces)

Actual logs from the Grand Showcase (showcase_episteme.py)

A. Handling Specificity (The Penguin Problem)

Goal: Prove that knowledge of a subclass overrides the superclass.

Input:

1. "Birds fly." (DEFAULT)
2. "Penguins are birds."
3. "Penguins do not fly." (EXCEPTION)

Trace:

➤ Query: 'Does Tweety fly?'
  [Buddhi] Path 1 (Positive): Tweety -> Penguin -> Bird -> Fly (Distance 2)
  [Buddhi] Path 2 (Negative): Tweety -> Penguin -> Not Fly (Distance 1)
  
  VERDICT: NO
  Reason: Specificity Win: Negative penguin (Dist 1) overrides Positive bird (Dist 2)

B. Handling Ambiguity (The Nixon Diamond)

Goal: Identify when logic is genuinely inconclusive.

Input:

1. "Quakers are pacifists."
2. "Republicans are not pacifists."
3. "Nixon is a Quaker."
4. "Nixon is a Republican."

Trace:

➤ Query: 'Is Nixon a pacifist?'
  [Buddhi] Path 1 (Pos): Nixon -> Quaker -> Pacifist (Dist 1, Rank: Default)
  [Buddhi] Path 2 (Neg): Nixon -> Republican -> Not Pacifist (Dist 1, Rank: Default)
  
  VERDICT: CONFLICT
  Conflict Detected: Horizontal Conflict: quaker (Pos) vs republican (Neg) at equal distance 1.

C. Quantitative Decay

Goal: Show logic reacting to confidence loss.

Trace:

➤ Event: 'Market will crash' (Confidence: 0.1)
  [Buddhi] Query: 'Will market crash?' -> YES
  
➤ Time passes... (Decay applied)
  [Chitta] 📉 Deactivating 'Market will crash' (Conf 0.05 < 0.1 Threshold)
  
➤ Query: 'Will market crash?'
  VERDICT: UNKNOWN
  Reason: No active beliefs found.