The Agency-Semantics Spine of Distinction Theory: Attention, Value, Goal, Meaning, and Action under Finite Capacity

Official website: distinctiontheory.orgPublic portal for the start guide, papers, claim status, failure registry, prior-art boundary, and citation resources. Canonical GitHub repository:https://github.com/yiningwu-research/Distinction-Theory FDS-M0 develops the agency-semantics spine of Distinction Theory. Building on the formal core of Active Finite Distinction Systems, it treats attention, value, goal, meaning, and agency as finite-system roles in boundary maintenance under limited capacity. A distinction becomes attended when admitted into a finite update channel; valuable when it has causal boundary-gradient relevance; goal-relevant when the value ranking is stabilized into a policy orientation; meaningful when compressed into a task-sufficient actionable quotient; and agentic when updates or actions causally affect future boundary-maintenance loss. The paper does not claim to solve consciousness, reduce semantics to physics, derive ethics from survival, or assign strong agency to any system merely because it processes symbols. It provides a conservative bridge for analyzing biological, cognitive, artificial, and social systems as active finite systems that admit, rank, compress, verify, externalize, and act on distinctions under capacity and resource constraints. The paper introduces capacity-limited distinction admission, causal boundary-gradient value, risk-weighted value near collapse thresholds, goal-stability indices, policy-preserving semantic quotients, semantic capacity deficit, semantic collapse, boundary-sensitive agency classification, verification load, self-verifying agency, normalized action-effect misalignment, and epistemic pollution as verification-bandwidth saturation. The accompanying replication package includes deterministic synthetic normal-form code, figures, and CSV outputs. The simulations illustrate nonlinear attention gating, semantic capacity deficit, policy-preserving quotients, goal stability, boundary-sensitive agency classification, misalignment audits, and epistemic pollution. These figures are synthetic normal-form illustrations, not empirical evidence. Scope. FDS-M0 is a spine paper for later work on attention, value, meaning, trust, AI alignment, institutions, scientific error correction, epistemic pollution, education, law, and civilization memory. Domain applications must specify their boundary, memory, update rule, capacity, loss, verification channel, and failure condition.