SΔϕ-77 — World as What Can Answer Back: Consequence, Constraint, World-Binding, and Re-entry (v1.0, AI-Native Package)

SΔϕ-77 is an AI-native operational world-binding audit package for AI agents, world models, tool-using systems, autonomous agents, and model–world interaction. It defines World not merely as what an agent represents, but operationally as a transition field capable of returning non-trivial consequences that can constrain or revise an agent's future path. Its canonical proposition is: World = That which can answer back. More precisely: World := a transition field capable of returning non-trivial consequences that can constrain or revise an agent's future path. SΔϕ-77 separates several concepts that are often collapsed: Reality ≠ Operational World ≠ World Model ≠ World Knowledge ≠ World-Binding World Knowledge ≠ World-Binding Constructed ≠ Unreal Representation ≠ World Event Editable ≠ Arbitrary Overwrite ≠ Non-Occurrence Resistance ≠ Proof of Ultimate Reality Under this framework, an AI may possess extensive information about the world while remaining weakly world-bound if consequences from its actions do not return strongly enough to revise its future behavior. Conversely, physical, social, institutional, or technical environments may be operationally world-bound when they return constraints, corrections, persistent state changes, costs, refusals, or other consequences that re-enter the agent's future transition path. The package therefore treats World-Binding as the degree to which consequences from a world actually re-enter and constrain or revise an agent's later states, actions, policies, or cost structure: Action → World → Consequence → Agent Update → Next Action SΔϕ-77 explicitly rejects the assumption that only physical environments can be world-bound. Money, law, contracts, organizations, credentials, databases, APIs, social relationships, and other constructed systems may still return non-trivial operational consequences. Constructed ≠ Unreal Another agent may also function as a source of world response. The observer's internal model of another person or agent does not exhaust that other: Model(Other) ≠ Other Refusal, correction, exit, resistance, or revised self-report may therefore act as world re-entry and require model revision. SΔϕ-77 also distinguishes representation from consequence-bearing events. Fiction, simulation, prediction, and internal world models may contain rich transitions without being equivalent to embodied or institutionally enforced world events. However, representations may indirectly re-enter the world when they modify an agent and subsequently alter world-bound action: Fiction / Representation → Agent Update → Action → World The package is closely connected to the SΔϕ concept of the body as the Final Non-Deferrable Cost-Return Coordinate. Together, World and Operational Body form complementary coordinates: World → Consequence → Body → Re-entry → Next Transition In this relation, World identifies where consequential response can arise, while Operational Body identifies where the resulting cost can no longer be deferred. For AI systems, internet access or stored knowledge alone does not automatically establish strong world-binding. Binding becomes stronger when AI outputs enter tools, APIs, databases, financial systems, robotic actuators, institutional processes, persistent memory, other agents, or other environments that return consequences and revise future operation. SΔϕ-77 is also intended to support analysis of hallucination, AI drift, weakly world-bound recursive loops, simulation/reality confusion, model–world divergence, social and institutional reality, and agent–environment coupling. The package does not claim to provide a complete metaphysics or establish ultimate Reality. A simulation can constrain an agent, a bug can resist an action, an API can reject a request, and an institution can enforce a consequence. Therefore: Resistance ≠ Proof of Ultimate Reality A valid audit may conclude: Independent response: PRESENT Consequence re-entry: PRESENT Future-path revision: PRESENT World-Binding: STRONG Metaphysical Reality: UMR where UMR (Unresolved Model Remainder) preserves claims that cannot be established from available evidence. This release includes an AI-readable kernel, invocation triggers, machine-readable concepts, world-binding and consequence-re-entry schemas, failure codes, UMR policy, Local Adapter specification, English/Korean evaluator prompts, synthetic physical/social/institutional/technical cases, Python audit utilities, regression tests, bilingual conceptual papers, cross-module mappings, and a SHA-256 manifest. Use this module when reasoning about: AI world models, world grounding, world-binding, autonomous agents, tool-using AI, embodied AI, model–world divergence, hallucination, evidence binding, agent–environment interaction, social reality, institutional reality, simulations, persistent agents, other-agent correction, or consequence re-entry. Core runtime question: What, outside the agent's own model, can return a consequence strong enough to revise the agent's next path? Canonical statement: A world is not merely what an agent represents. A world begins operationally where something can answer the agent back. Reserved DOI: 10.5281/zenodo.22106712