Theory of Moment (ToM): Research Prospectus

1. Motivation

Every act of representation is, by necessity, an abstraction. Language compresses experience into words, mathematics into symbols, and increasingly, artificial intelligence into tokens. These abstractions allow us to communicate, reason, and build increasingly sophisticated systems. Yet every abstraction carries a representational cost.

Theory of Moment begins with a simple observation. Human experience unfolds as a continuous stream of conscious moments, while many of our representational systems represent that experience through static concepts. Consider the statement, "I love you." Linguistically, it appears to describe a persistent state. In lived experience, however, love is encountered only during those moments in which consciousness is occupied by loving. The moments preceding and succeeding those experiences may be occupied by prayer, work, memory, conflict, perception, or countless other contents of consciousness. Whether enduring dispositions exist beneath conscious awareness is a separate question; what is directly lived is the continuous unfolding of conscious experience.

This distinction motivates the central inquiry of Theory of Moment. If reality is experienced as continuous temporal processes, to what extent do our representational systems faithfully preserve that continuity? Conversely, what assumptions, distortions, or losses are introduced when dynamic processes are represented as static entities?

2. Research Question

Can reality be represented more faithfully when concepts are treated as functions of time rather than as timeless abstractions?

3. Core Hypothesis

Theory of Moment proposes the hypothesis that many concepts commonly represented as static entities may be more faithfully represented as functions of time. Rather than treating concepts such as identity, emotion, belief, intelligence, or intention as enduring states, ToM proposes investigating whether they are better understood as temporally evolving processes whose meaning emerges through the succession of conscious moments.

This hypothesis does not deny the possible existence of enduring dispositions, memories, or unconscious structures. Rather, it distinguishes latent possibility from lived experience. Human experience is encountered through the continuous unfolding of conscious moments. If our representational systems primarily encode static abstractions, an important question follows: what information is gained or lost by making time an explicit component of representation?

Theory of Moment therefore proposes a process-oriented framework whose primary objective is not to replace existing representational systems, but to investigate whether explicitly temporal representations offer greater representational fidelity for describing human experience and, by extension, for reasoning systems built upon such representations.

4. Why Artificial Intelligence?

Artificial intelligence provides a unique environment in which representational assumptions can be examined computationally. Modern AI systems learn from vast collections of human-generated language, inheriting not only our knowledge but also the abstractions embedded within our representational systems. If human language systematically compresses dynamic processes into static concepts, then it is reasonable to ask whether these assumptions are also reflected in machine reasoning.

Theory of Moment does not propose a singular replacement architecture for artificial intelligence. Rather, it proposes a research direction: can explicitly temporal representations improve representational fidelity for reasoning systems? This question may be explored independently of any particular model architecture or machine learning paradigm, allowing the hypothesis to be investigated through future empirical research.

The long-term objective is to determine whether treating concepts as explicit functions of time yields measurable improvements in representational fidelity, contextual reasoning, and temporal consistency.

5. Research Agenda

Current Stage

Originating in 2018, Theory of Moment has evolved from a philosophical inquiry into an ongoing interdisciplinary research program spanning philosophy, language, art, and artificial intelligence. To date, the research has produced:

• A manuscript, currently undergoing editorial refinement, documenting the theoretical foundations and development of Theory of Moment.
• A formal research prospectus defining the central research question, core hypothesis, and long-term research agenda.
• A growing collection of conceptual analyses applying Theory of Moment across diverse domains, including language, identity, emotion, belief, meaning, cognition, reasoning, and classical philosophical inquiry.
• Preliminary AI-oriented research investigating how temporally faithful representations may inform future reasoning systems and computational models.
• A publicly accessible research website documenting the ongoing development of the Theory of Moment research program and supporting public engagement with its ideas.

Near-Term Objectives

The immediate objective of the research is to transition Theory of Moment from a philosophical framework into a computationally investigable research program. Planned work includes:

• Developing a formal representation of concepts as explicit functions of time.
• Translating the philosophical framework into computational models suitable for experimental investigation.
• Developing minimal computational prototypes to explore whether temporally explicit representations exhibit qualitatively different reasoning behaviour from conventional static representations.
• Establishing measurable criteria for evaluating representational fidelity, temporal consistency, and contextual reasoning.
• Publishing revised editions of the manuscript and supporting research papers as the framework continues to mature.

Long-Term Research Vision

The long-term objective of Theory of Moment is to investigate whether temporally faithful representations can contribute meaningfully to artificial intelligence while extending the framework into broader questions of language, cognition, and human understanding. Future directions include:

• Empirical evaluation of Theory of Moment through computational experimentation.
• Collaboration with researchers in artificial intelligence, philosophy, linguistics, cognitive science, and related disciplines.
• Investigation of temporal representation within reasoning architectures and intelligent systems.
• Exploration of applications in language, education, psychology, and human-computer interaction.
• Continuous refinement of Theory of Moment through interdisciplinary collaboration, critical discussion, and empirical validation.

6. Invitation to Collaborate

Theory of Moment is presented not as a finished system, but as an evolving research program. Its central hypothesis—that many concepts may be represented more faithfully as functions of time than as timeless abstractions—invites philosophical analysis, computational modelling, and empirical investigation.

Theory of Moment remains intentionally open to criticism, refinement, and interdisciplinary collaboration. Researchers, engineers, philosophers, linguists, and students who find the central question compelling are invited to engage with, challenge, and extend the ideas presented here.

Whether Theory of Moment proves broadly applicable or only valuable within specific domains, its objective remains the same: to deepen our understanding of representation and to investigate whether greater representational fidelity can enrich both human understanding and artificial intelligence.