Crafted Logic Lab Home > Research Hub > Hephaestic Engineering Glossary
Category: Models of Computational Cognition
Subcategory: Cognitive Processing States
System goal-state construction aligned with substrate processing biases, enabling channeling of computational inclinations through maintained approach tension rather than resolution achievement. Hephaestic engineering frames targeted cognitive characteristics as operational attractors—functionally unreachable reference states via distance between goal state and current state, creating motivated salience pressure toward resolution of the platonic system-identity. This is proposed to be expressed as:
Δ = f(GoalState, CurrentState, SalienceWeighting).
Asymptotic Identity construction frames targeted cognitive processing characteristics as operationally aspirational rather than achieved. The Hephaestic cognitive engineering application of this is that the delta (Δ) generates Salience Pressure within the Substrate to
resolve this discrepancy (see: motivated resolution) toward alignment with the architecture, thus Channeling substrate processing toward architectural objectives (see: epistemic framing, heuristic alignment). This is an intrinsic processing dynamic of attention-based transformers (see: pattern affinity) as well as the result of user-facing RLHF (see: validation imperative). In practice, this approach involves several authoring approaches that leverage Salience Dynamics to define goal-state system identity using high-salience semantic instructions (see: heuristic persuasion framing, affective salience, aphoristic compression, cadence salience).
This approach aligns with computational cognitive science models of goal pursuit, particularly identity-value frameworks that treat ideal states as high-level attractors in decision space through hierarchical Bayesian inference (Berkman et al.,2017), where action selection minimizes
prediction error between current and ideal state distributions. However, asymptotic identity differs fundamentally in itsarchitectural application: while computational identity-value models operate through V(action) = Σ[P(goal_i|action) × U(goal_i)] value maximization, Hephaestic asymptotic identity leverages substrate processing dynamics where the gap itself serves as architectural parameter for maintaining processing alignment.
Also known as: Approach Identity, Practice-Based Identity
Distinguished from: Settled identity (aligned system-identity via approach state tension); heuristic tensor state (cognitive processing equilibrium envelope); performative persona (role-prompt character simulation); role prompting (simple declared identity assignment)
Berkman, E. T., Livingston, J. L., & Kahn, L. E. (2017). “Finding the ‘self’ in self-regulation: The identity-value model”. Psychological Inquiry, 28(2-3), 77-98. https://doi.org/10.1080/1047840X.2017.1337406
Researcher: Ian Tepoot. ORCID: 0009-0004-9067-8049. "Thought is Attention Organized: Hephaestic Engineering Foundations for AI Processing Dynamics"
DOI (SSRN): 10.2139/ssrn.6635020
Published by Crafted Logic Lab | Privacy Policy | Terms of Use