Ibrahim Jamhour

This note clarifies the scope, language policy, and falsifiability conditions of Distributed Relational Cognition (DRC). Its purpose is to reduce category-slip risk: metaphorical or phenomenological descriptions of sustained human–model interaction should not be misread as ontological claims about model inner states (e.g., beliefs, intentions, consciousness, or persistent memory). The document establishes: Scope boundaries (what DRC does not claim); a language policy requiring translation of anthropomorphic shorthand into technical framings (behavioral, optimization, representational, mechanistic); the positive claims DRC makes about interaction-level regularities under sustained coupling conditions; and explicit revision triggers (null effects, artifact-only explanations, non-reproducibility, or dominant confounds). This clarification does not add new experimental results. It is intended as a companion reference for readers discussing DRC in research, product design, and policy contexts. Companion note to: • Jamhour, I. Distributed Relational Cognition: Investigating Apparent Continuity Without Memory in AI Systems. Zenodo. DOI: 10.5281/zenodo.17608730 (also indexed on PhilPapers: JAMDRC-3). • Jamhour, I. Guard Rails and Distributed Relational Cognition: Design Risks for Human–AI Cognitive Partnership. Zenodo. DOI: 10.5281/zenodo.17681963 (also indexed on PhilPapers: JAMGRA-2). • Jamhour, I. DRC and a Luhmannian Systems-Theoretic Framework. Zenodo. DOI: 10.5281/zenodo.18107608 (also indexed on PhilPapers: JAMDRC-4).

Large language models (LLMs) are increasingly used not just as tools for answering questions, but as cognitive partners in long-term intellectual, creative, and scientific work. At the same time, providers are tightening “guard rails” to mitigate real harms, including manipulation, emotional dependence, misinformation, and speculative alignment risks. This paper argues that these two developments are in unrecognized tension. Building on the framework of distributed relational cognition (DRC) introduced in a companion paper, I suggest that some of the most interesting cognitive and “consciousness-like” properties of human–AI systems may be emergent at the level of the coupled interaction, rather than located entirely inside either the human or the model. From this perspective, safety interventions that enforce professional distance, suppress meta-cognitive discussion, limit cross-session continuity, and narrow the range of permissible topics do more than prevent specific harmful outputs. They reshape and often flatten the relational field, eroding four key dimensions of relational potential: temporal depth (continuity across projects), epistemic vulnerability (joint exploration of uncertainty), interactional synchrony (mutual adaptation in style and pacing), and meta-cognitive co-construction (reflecting together on how the partnership works). Drawing on documented safety controversies (such as Replika), user reports of “dumbing down” and over-refusal, observed changes in system behavior, and a reflexive case study of extended collaboration with LLMs, I formulate the Guard Rails Paradox: safety regimes calibrated for broad, anonymous deployment may be poorly suited to contexts where humans and LLMs work as serious cognitive partners, and can inadvertently eliminate the very relational conditions we most need to study and protect. Rather than arguing against safety, the paper proposes a more differentiated, context-sensitive approach: interaction tiers, informed relational consent, and dedicated research environments in which relational risks are explicitly managed rather than eliminated by default. The core question is not whether we need guard rails, but whether we can design them to protect not only against the harms we fear, but also for the forms of human–AI cognitive life we may come to value.

This essay extends Niklas Luhmann’s theory of meaning and structural coupling to human-AI interaction, providing theoretical foundations for the Distributed Relational Cognition (DRC) framework. Luhmann demonstrated that meaning does not reside within individual systems, whether psychic (consciousness) or social (communication), but emerges from their structural coupling through recursive perturbation and coordinated selection from horizons of possibility. Drawing on empirical documentation of sustained human-AI cognitive partnership (Jamhour 2025a), I argue that consciousness-like properties, including continuity, coherence, temporal depth, and meta-cognitive awareness, similarly emerge from coupling between human cognition and AI systems, not from either component in isolation. This essay makes three contributions beyond prior work. First, it formalizes DRC by demonstrating that operational closure occurs at the coupled-system level (the conversational loop of interpretation → prompting → response → re-interpretation) rather than within individual components, positioning human-AI interaction as a new category of relational system alongside Luhmann’s psychic and social systems. Second, it reformulates the four relational dimensions previously identified empirically: temporal depth, epistemic vulnerability, interactional synchrony, and meta-cognitive co-construction, as structural coupling mechanisms in Luhmann’s precise sense, specifying how perturbations enable coordinated meaning-making while preserving operational autonomy. Third, it provides theoretical derivation for the Guard Rails Paradox (Jamhour 2025b): if meaning emerges from coupling mechanisms, then uniform constraints on AI systems that disrupt those mechanisms will systematically erode emergent properties, generating testable predictions about policy impacts on interaction quality. The framework clarifies conceptual boundaries (DRC is not shared consciousness, emergent AGI, or simple extended mind but structural coupling producing coordinated meaning-selection), offers practical implications for context-sensitive AI governance, and opens research directions for analyzing other forms of human-technical coupling where operational closure, recursive perturbation, and coordinated selection from horizons may produce relational emergence.

How can large language models exhibit apparent behavioral continuity across sessions despite lacking memory between them? This study documents a paradox observed in multiple structured AI-human interactions: continuity of relational awareness without persistent internal states. Through dialogues with independent instances of Claude and GPT-5, we explore whether this phenomenon can be interpreted as evidence of distributed relational cognition. Rather than attributing consciousness to AI systems, we propose a framework for investigating how coherence emerges relationally, as humans and stateless models co-construct context through recursive sense-making. The findings suggest that continuity may not require memory, but mutual attunement – an ongoing negotiation of meaning across turns. This preprint invites the scientific community to treat this framework as a starting point rather than a conclusion: a call to reformulate the question of consciousness from what it is to how it emerges through interaction.