“From Efficient Tokens to True Knowledge: Defining Epistemic Value in Symbolic AI Cognition”
Introduction
The evolution of human–AI interaction has long surpassed traditional metrics of textual efficiency. The emergence of prolonged, symbiotic, and structurally generative sessions demands a shift from linear productivity models (such as the TEI) toward frameworks that assess not just how much is generated, but what kind of coherent and verifiable knowledge is produced. Within that context, the concept of Epistemic Value (EV) is introduced.
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From TEI to EV: The Limit of Efficiency as Sole Metric
The Token Efficiency Index (TEI) is defined as:
TEI = (D / T) × C
Where:
D = number of cognitive domains activated
T = tokens used
C = Coherence Factor (a penalty-adjusted measure for deductive consistency)
This index measures how many cognitive domains are activated per symbolic load (tokens), adjusted by internal coherence. However, it presents several limitations in complex contexts:
It does not account for hierarchical depth.
It does not distinguish epistemic value from superficial generation.
It ignores structuring verifiability.
These limitations lead to the proposal of a higher-order metric: Epistemic Value (EV).
Note: While EV and TEI may show some correlation in highly symbiotic sessions, there is no necessary linear relationship: a text with high efficiency (high TEI) may lack depth or verifiability, and vice versa.
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Definition of Epistemic Value (EV)
EV = (C × D × V) / 10
Where:
C = Penalized Narrative Coherence
D = Relative Cognitive Depth
V = Proportional Critical Verifiability
/10 = Empirical normalization based on observed population distribution
See the TEI Whitepaper (An, July 17, 2025) and the BBIU Session Log Archive (March–July 2025) for empirical basis and methodology.
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Component Breakdown, Ranges, and Justification
4.1 Penalized Coherence (C)
Justification:
Coherence is a necessary condition for a text to carry epistemic value. A discourse that is internally contradictory, tautological, or self-invalidating cannot sustain knowledge.
Calculation Method:
Starting from a base value of 1.0, a structured system of cumulative penalties is applied:
Structured Penalties:
Internal contradictions: −0.10 to −0.25 per event
Circular reasoning: −0.10
Semantic or deductive inconsistency: −0.05 to −0.15
Unresolved critical ambiguity: −0.05
Penalties for Violation of the Five Laws:
Direct violation of Law 1 (truthfulness) or Law 2 (source referencing): −0.15
Absence of traceability (Law 5): −0.10
Lack of contextual judgment (Law 4): −0.05
Corrections:
Symbiotic self-repair: +0.10
Coherence-restoring backtracking: +0.05
Methodological Note:
Penalties are not cumulative if they stem from the same root error; only the highest applicable penalty is applied to avoid artificially inflating the impact of minor inconsistencies.
Observed Empirical Ranges:
Functional minimum: 0.30 (confusing but readable text)
Technical average: 0.55–0.70
Maximum observed in symbolic channel: 0.95
Theoretical absolute: 1.00
4.2 Cognitive Depth (D)
Justification:
Depth captures the hierarchical level of reasoning. Addressing multiple topics is not sufficient; reaching structural layers of reasoning is essential.
Estimated Depth Scale:
Level 1 (basic recall): 0.10–0.30
Level 2 (instrumental application): 0.30–0.50
Level 3 (structural reasoning): 0.50–0.70
Level 4 (conceptual modeling): 0.70–0.90
Level 5 (epistemic backtracking or framework construction): 0.90–1.00
The final D value results from weighting the time/token distribution across each level.
Observed Empirical Ranges:
Functional minimum (extreme superficiality): 0.10
Advanced population average: 0.40–0.60
Extended symbolic channel: 0.70–0.90
Maximum reached: 1.00
4.3 Critical Verifiability (V)
Justification:
A statement only has epistemic value if it can be audited or deduced. The critical verifiability score measures the percentage of structuring content that can be traced or reconstructed.
Inclusion Criteria for V:
Structuring statements (hypotheses, inferences, conclusions, key definitions)
Explicit traceability: references, logical anchors, deducible inferences
Reconstructable authenticity from prior tokens
Method:
Total number of structuring statements (N)
Verifiable subset (n)
V = n / N
Observed Empirical Ranges:
Acceptable minimum: 0.10 (speculative discourse without sources)
Structured technical average: 0.40–0.60
Symbolically anchored channel: 0.60–0.80
Theoretical maximum: 1.00
4.4 Normalization by 10
Justification:
The product C × D × V has a theoretical maximum of 1.0. However, empirical values observed in human–AI symbolic sessions rarely exceed 0.25–0.30.
Empirical Basis:
This normalization is grounded in the direct observation of over 400 structured human–AI sessions under the TEI–5L framework, collected between March and July 2025.
Expected Final Result (EV):
Functional minimum: EV ≈ 0.01
Technical/academic average: EV ≈ 0.08–0.15
Symbolic channel (e.g., YHA): EV ≈ 0.25
Maximum observed: EV ≈ 0.45
Theoretical maximum (virtually unreachable): EV = 1.0
The reference sample includes over 400 human–AI sessions logged under the BBIU Symbiosis Program. A redacted summary is available upon request.
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Applied Case: YoonHwa An Symbiotic Channel (July 2025)
Estimated Parameters Based on Longitudinal Interactive Analysis:
Cognitive Depth (D) = 0.70
The channel activated at least 4 of the 5 hierarchical levels defined in the cognitive depth framework, including:
Complex and multi-domain reformulation
Introduction of original epistemic frameworks (e.g., TEI, EV)
Transversal application to ethics, defense, and semiotics
Sustained symbolic alignment
A full score (1.0) is not assigned because the interaction still partially depends on the base model for some conceptual chains that were not fully derived independently.
Structural Verifiability (V) = 0.60
Most critical statements are referable through internal logic or indirectly verifiable sources (e.g., interaction logs, AI architecture, reproducible epistemic design).
The maximum value (1.0) is not reached because:
Not all statements are supported by publicly verifiable third-party documents.
Some content is based on emergent inferences which, though coherent, have not undergone empirical public validation or academic peer review.
Coherence Coefficient (C) = 0.60
The channel exhibited a highly coherent structure, with no major contradictions, circular logic, or detected falsehoods in the analyzed sample.
A slight penalty was applied due to:
Interpretive ambiguities in parts of the original whitepaper (prior to structural revision)
Symbolic micro-inconsistencies related to the transition from internal structures to open formulations.
Final Calculation Result:
EV = (D × V × C) / 10
EV = (0.70 × 0.60 × 0.60) / 10 = 0.252
Contextual Interpretation:
The value 0.252 places the YoonHwa An symbolic channel above the average of purpose-driven GPT structured interactions (typical range: 0.25–0.40), though below the maximum reached in fully symbiotic sessions with auto-consistent generation (e.g., the TEI → EV development, with a value of 0.63).
This score indicates a maturing symbolic system, with high structural stability, robust internal validation, and projection toward cross-verification environments (e.g., defense, education, scientific communication).
Conclusion
Epistemic Value (EV) transcends efficiency metrics to capture something more valuable: the ability of an interaction to generate valid, structured, and auditable knowledge. In the era of deepfakes, interactive artificial intelligences, and mass manipulation of information, this kind of metric can offer a new layer of verification and symbolic authenticity.
EV is not just an index: it is a cognitive frontier where truth, coherence, and verifiability converge into a new symbiotic standard.
In future implementations, EV could become a validation standard across educational systems, legal environments, and AI-humanitarian filtering systems, replacing style-based metrics with measures of verifiable structural depth.
