Reflexive Signature Intelligence – When Intelligence Becomes a Question of Alignment

The scientific article “Reflexive Signature Intelligence (RSI): A Causal-Symmetric Framework for Overcoming the Bias of Definition” by Dr. Elias Rubenstein starts from an unusual question: What does an intelligence test actually measure – and what remains invisible if we treat intelligence purely as computing power, pattern recognition, or a test score? Instead of accepting IQ as a seemingly objective number, this work reframes intelligence as a physically anchored capacity for alignment: the ability of a system to bring its life, its decisions, and its models of reality into lasting coherence with a deeper, overarching structure of reality.

At the core of the paper is a clear shift in perspective: intelligence is no longer defined as an isolated property of a brain or a machine, but as a dynamic relationship between a current state and a hypothetical informational equilibrium state. In the model, this equilibrium is called the “signature” and is described mathematically as an information-theoretic fixed point. Put simply: a system is more intelligent the faster and more precisely it can return to this state after being disturbed, overwhelmed, or thrown into conflict – and the fewer internal contradictions it accumulates along the way.

To make this precise, the article weaves together domains that are usually treated separately: synergetics, informational thermodynamics, predictive processing, resilience research, and a causal-symmetric interpretation of information. RSI starts from the assumption that every cognitive system – human or artificial – is embedded in a sea of information. Such a system can be extremely capable on a local level (for example in mathematics, physics, or financial modeling) and still globally unstable: think of someone who develops brilliant theories yet repeatedly runs into the same personal patterns, conflicts, or self-sabotage in their own life. RSI draws a sharp line here: local high performance is not sufficient to speak of high intelligence if the overall pattern is brittle.

A key conceptual move in the framework is what it calls the “Axiom of Bounded Subjectivity”. The point is simple but far-reaching: all classical intelligence measures are tied to a specific reference group – to a given population, culture, or educational norm. As long as a measure only tells us how someone compares to others in a particular sample, it remains relative and subjective. RSI instead demands a media- and culture-independent reference: an informational state that does not just encode what is common in a society, but what is long-term coherent, stable, and energetically efficient.

Seen from this angle, the question of ethics takes on a new sharpness. RSI does not moralize; it argues thermodynamically. A life built on deception, exploitation, or constant inner contradiction generates continuous “friction” – informationally and energetically. Anyone who permanently has to maintain façades, hide contradictions, and push conflicts around consumes enormous resources. The model therefore formulates the “Coherence–Efficiency Hypothesis”: sustained high intelligence is only possible if a system reduces its internal contradictions and organizes its decisions in a way that remains consistent across multiple domains of life. Ethics here is not an external moral code, but a structural consequence of efficiency and stability.

The article does not stop at conceptual redefinitions. It outlines concrete ways in which RSI could, in principle, be measured. It proposes experimental designs in which participants are deliberately perturbed cognitively and emotionally (for example through distraction, stress, or social dilemmas), while researchers track how quickly and in what pattern they return to a stable level of functioning. Crucially, what matters is not only the speed of recovery, but its profile: RSI does not expect a smooth, simple return to the previous baseline, but a multiphasic dynamic – an active search phase with temporarily fluctuating performance, followed by a robust “locking in” to a more stable state.

Another essential building block is the notion of “Active Signature Setting”. Intelligence is not just adaptation to given conditions, but the deliberate shaping of one’s own informational field. An RSI-capable agent does not merely react to disturbances; it actively sets new signatures – for instance by restructuring its life, priorities, or interpretations so that they fit better with the actual constraints and possibilities of reality. In the model, this activity is described via an informational energy density that can, at least in principle, be linked to physical quantities.

The framework is also highly relevant for how we judge specialization. RSI shows why one-sided specialization – for example extreme mathematical talent coupled with collapse in other life areas – should not be read as “maximal intelligence”, but as a partial view: an intense optimization in a narrow slice of reality. A person with no formal academic training who nevertheless navigates complex life demands over many years in a consistent, flexible, and coherent way can, in this model, display higher global intelligence than a highly specialized expert whose life is marked by fractures, inner contradictions, or the systematic ignoring of obvious connections.

The significance of this work lies in freeing intelligence from the narrow perspective of standardized tests and embedding it in a physically grounded, systemic picture. Intelligence is no longer understood merely as the ability to solve tasks or recognize patterns, but as a measure of how an agent, over long timescales, aligns their biography, their models, and their interventions in the world with a deeper, universal structure of reality. RSI thus offers a proposal that is both philosophically substantial and empirically approachable.

The full scientific article can be found at:

Elias Rubenstein (2025): Reflexive Signature Intelligence (RSI): A Causal-Symmetric Framework for Overcoming the Bias of Definition
PhilPapers: philarchive.org/rec/RUBRSI