The Nature of Narrative Consistency
Narrative consistency is the property of a story whose events, characters and explanations fit together in a way that feels coherent, intelligible and stable over time. A consistent narrative is not merely a list of facts; it is an ordered structure in which earlier statements constrain what can be said later, and in which causal and temporal relations are respected.
Philosophers of history and literature have long noted that humans understand the world through narratives: we track who did what, when, for which reasons, and with what consequences. In this view, inconsistency is not only a logical defect (holding incompatible propositions) but also a narrative defect: the storyline fractures, characters behave inexplicably, or events appear without plausible antecedents. For a reasoning system that interacts with humans, maintaining narrative consistency is therefore a core dimension of intelligibility and trust.
Narrative Consistency in Artificial Reasoning
In traditional knowledge-based systems, consistency is often treated purely as a logical property: the knowledge base must avoid explicit contradictions. However, narrative consistency requires more than logical satisfiability. A system may avoid direct contradictions while still producing answers that jump across time, ignore earlier context, or silently change the underlying assumptions of a scenario.
Modern AI applications, especially those used in law, medicine, compliance, and operations, must give answers that respect a temporal and causal storyline. If a system says that a patient received a treatment, it should not later act as if the treatment never happened; if a legal reasoning chain assumes a specific contract clause, later steps should not switch to a different clause without explicit acknowledgement. Narrative consistency ensures that the system’s explanations behave more like a careful human expert than like a sequence of isolated predictions.
Narrative Consistency in AGISystem2
AGISystem2 treats narrative consistency as an explicit design goal that spans its geometric representation, theory layering, and reasoning pipeline. Instead of producing answers in isolation, the system operates within theory contexts and temporal traces that encode “what the story is so far”.
Several components contribute to this behaviour:
- Temporal Memory. Temporal memory maintains chronologies of events, states and transitions. It allows the system to interpret a sequence of facts as a timeline, not just as a set, so that queries can be evaluated relative to earlier and later states. This supports questions like “Was the policy in force at the time of this decision?” or “What changed between these two consultations?”.
- The Reasoner and Theory Stack. The Reasoner assembles concepts and relations from the active theory stack, ensuring that conclusions follow from a consistent set of assumptions. When multiple theories (for example, conflicting guidelines or alternative scenarios) are active, the meta-rational stack keeps track of which narrative branch each answer belongs to, instead of silently mixing them.
- Sys2DSL and explicit scenarios. Through Sys2DSL scripts, authors can describe scenarios as sequences of facts, counterfactuals and queries. Topological evaluation ensures that dependencies between variables and steps are respected, while theory overlays and counterfactual layers allow the system to explore “what if” narratives without corrupting the baseline storyline.
- Validation and audit. Validation components can re-run reasoning under controlled conditions to check that explanations remain stable when inputs and theory contexts are unchanged. Audit logs capture which theories, dimensions and masks influenced each answer, making it possible to reconstruct the narrative path that led to a given conclusion.
Together, these mechanisms ensure that AGISystem2 is not just answering isolated questions, but maintaining coherent stories about the domains in which it operates.
Implications and Use Cases
Narrative consistency has important practical implications. In compliance and governance scenarios, it supports the reconstruction of decisions as complete stories: which facts were known, which rules applied, which exceptions were considered, and why a particular outcome was chosen. In medicine, it underpins longitudinal patient narratives, where each new observation must integrate with an existing clinical story.
For users, narrative consistency is also a key ingredient of trust. A system that changes its explanations every time it is queried, or that forgets earlier commitments, quickly loses credibility. By design, AGISystem2 aims to make such failures visible: when theory changes or contradictions prevent a stable narrative, the system prefers to surface the conflict rather than fabricate a smooth but inconsistent story.
Academic References
The importance of narrative coherence has been discussed in philosophy of history, literary theory and cognitive science. Paul Ricoeur’s work on time and narrative examines how temporal ordering and plot structure contribute to understanding. In AI, research on story generation, plan recognition and case-based reasoning has explored how systems can maintain coherent sequences of events and explanations.
For broader background, standard references on narrative theory and discourse structure provide useful context for how humans assess coherence and plausibility in stories.
Technical Implementation References
The following design and test specifications describe how narrative consistency is implemented and validated in AGISystem2:
- DS[/reason/temporal_memory.js] – temporal and causal timelines for events and states.
- DS[/reason/reasoner.js] – assembly of answers from layered theories and contexts.
- DS[/theory/dsl_engine.js] – Sys2DSL execution and scenario scripting.
- DS[/reason/validation.js] – re-checking and validating reasoning runs.
- DS[/tests/narrative_consistency/runSuite] – narrative consistency regression tests and example story flows.
For precise behavioural requirements and traceability to user and system requirements, see the specification matrix in the specs section of the documentation.