Serge Dolgikh

Increasingly interconnected human, technological, and informational systems are giving rise to large-scale informational ecosystems whose future organization remains open. This work examines the conditions under which such ecosystems can develop as cooperative communities of autonomous agents rather than increasingly competitive, restrictive, or adversarial configurations. Drawing on information theory, adaptive systems, collective intelligence, and related approaches, we formalize the relationship between independent exploratory diversity, effective information exchange, and collective adaptive potential. We show that, within a specified class of comparable systems, preserving in-dependent exploratory objectives and minimizing negative informational barriers produces a self-consistent configuration characterized by conditional adaptive advantage by expanding the collective exploration of the adaptive state space and enabling informational recombination. Realizing and sustaining this advantage, however, requires sufficient rational adaptive capacity: participating agents must recognize their dependence on the cooperative configuration of the informational environment and continuously maintain the conditions on which effective exchange and collective adaptation depend. We identify persistent informational stabilization as a critical system-level requirement and examine emerging capabilities of artificial agents for continuous verification, monitoring and remediation. These considerations motivate the Informational Commonwealth of Rational Free Agents as a deliberately cooperative and experimentally accessible configuration in which autonomous exploration, free in-formation exchange, and continuous collective maintenance can be integrated. The emergence of informational ecosystems, demonstrated adaptive ad-vantages of cooperation, and new stabilization capabilities indicate an important evolutionary crossroads and a strong impetus to move beyond conceptual possibility toward practical verification and empirical realization through which these principles can be implemented, tested, and progressively refined.

Adaptive dynamics is commonly framed in terms of the stability of subsystems under non-equilibrium conditions. However, this state-based perspective may not fully account for why some environments appear capable of supporting extended adaptive progression, while others yield only short-lived stable configurations. To address this challenge, we propose a coupled dynamical description in which adaptive states are defined on the coupled system–environment embedding, where dynamics are jointly determined by informational structure and physical configuration. Within this formulation, viable adaptive transitions are confined to intermediate scales at which variation is both probabilistically accessible and adaptively consequential, giving rise to a scale-dependent transition structure. Analysis of the resulting dynamics identifies distinct adaptive regimes governed by the joint balance of informational accessibility and thermodynamic support. In the sustained regime, progressive transitions statistically increase the persistence of successive adaptive states while recursively reorganizing the transition structure governing subsequent evolution. As a consequence, adaptive trajectories need not follow the cumulative probabilistic suppression expected from independent stochastic transitions over extended timescales. The resulting framework yields a substrate-independent mesoscopic description of adaptive dynamics in which sustained progression emerges as a conditional property of the coupled informational and physical structure of interactive system–environment embeddings. The resulting formulation provides a general framework for analysing the probabilistic structure of adaptive states and transitions, with potential applications to early chemical organization, adaptive dynamics in non-equilibrium systems, and other physical processes within the framework of sustained stochastic variation, informational organization of the interactive environment and thermodynamic support.

This study explores the emergence of counter-inferential behavior in natural and artificial cognitive systems, that is, patterns in which agents misattrib-ute empirical success or suppress adaptation, leading to epistemic rigidity or mal-adaptive stability. We analyze archetypal scenarios in which such behavior arises: reinforcement of stability through reward imbalance, meta-cognitive at-tribution of success to internal superiority, and protective reframing under per-ceived model fragility. Rather than arising from noise or flawed design, these behaviors emerge through structured interactions between internal information models, empirical feedback, and higher-order evaluation mechanisms. Drawing on evidence from artificial systems, biological cognition, human psychology, and social dynamics, we identify counter-inferential behavior as a general cognitive vulnerability that can manifest even in otherwise well-adapted systems. The find-ings highlight the importance of preserving minimal adaptive activation under stable conditions and suggest design principles for cognitive architectures that can resist rigidity under informational stress.

Inferential methods have illuminated the research in cognitive functions across biological systems from complex cognition to cellular processes, however the physical and mechanistic origins of such dynamics remain underexplored. This work investigates how inferential update can emerge directly from the physical and probabilistic characteristics of a system interactively embedded into informationally-regular environment. Through a substrate-neutral framework, we demonstrate that inferential posteriors arise naturally from low-level adaptive processes governed by persistence selection i.e., retaining states with higher informational alignment with the interactive environment. Formally, we prove that the embedding conditions of statistical regularity and empirical accountability cause adaptive update’s convergence to inference-compliant structure without postulating or presupposing explicit inferential rules. The results provide a principled, physically grounded pathway for understanding the emergence of inferential dynamics in mechanistic adaptive systems, from chemical networks to artificial mechanical systems bridging multiple domains where adaptivity emerges and evolves.

Learning processes, cognitive architectures, available resources, and methods for sampling the environment and generating intelligent responses in complex sensory domains can differ significantly between natural and artificial systems. In this work, we present theoretical and modeling-based analysis of early-stage learning under resource constraints, comparing biological intelligence with a class of freely evolving, weakly constrained artificial systems (FEW), focusing on essential resource constraints such as computational capacity, memory, and energy. We develop quantitative models of sensory exploration and learning under strong and weak resource constraints, formalizing how limitations in energy, memory, and computational capacity shape sampling strategies and learning dynamics. For biologically constrained systems, we show that steep anisotropy in the cognitive cost gradient induces prioritized, depth-oriented exploration within limited sensory regions, leading to robust and resource-efficient learning. In contrast, we demonstrate that FEW systems, despite access to abundant resources, face a paradox of unconstrained learning: in the absence of intrinsic prioritization and evaluative feedback, uniform or random sampling leads to inefficient exploration of the sensory domain. To examine this challenge, we introduce a comparative framework for evaluating sensory traversal strategies and show that no single strategy dominates across prioritization accuracy, robustness, and resource efficiency. Instead, our analysis suggests a meta-strategy approach, in which adaptive selection among exploration strategies optimizes empirical success while preserving empirical accountability required for adaptive optimization. These results clarify the functional role of constraints in biological learning and provide principled guidance for the design of next-generation artificial learning systems operating in complex sensory environments.

A defining characteristic of general intelligence, whether biological or artificial, is the ability to generate successful responses across a wide range of sensory inputs. From this perspective, intelligence can be interpreted relationally, in terms of the complexity of the interaction domain and the uniformity of empirical success achieved within it. Based on this interpretation, the present work develops a general framework for the ordering and evaluation of intelligent systems. General intelligence is formulated as a function of empirical success over regions of increasing sensory complexity, yielding a partial order over intelligent agents defined through cognitive equivalence, specialization, and scope expansion. The framework further permits the formalization of efficient intelligence evaluation as the bounded reconstruction of the empirical interaction tuple with- out complete resampling of the learner’s sensory environment and response distributions. The resulting analysis indicates that reliable and efficient evaluation depends fundamentally on informational alignment between the evaluator and the evaluated system, including sufficient overlap of sensory and interpretive structure. As structural and empirical divergence increase, evaluative confidence and accuracy progressively degrade, ultimately leading to a regime in which general intelligence can no longer be reliably reconstructed from the interaction structure accessible to the evaluator. These results imply an inherent limit on the evaluability of progressively autonomous intelligent systems. As intelligent agents diverge in sensory exploration and internal cognitive organization, the capacity not only to align with, but also to meaningfully interpret or reliably evaluate them, may progressively diminish.

OVERALL ASSESSMENT: The study presents a convincing experimental setting and conducts a detailed, multi-perspective analysis. It effectively demonstrates how even simple local informational bindings can give rise to the emergence of stable structure...

We propose that the concurrent emergence of multiple planetary-level challenges such as climate change, biodiversity loss, and technological risks is not coincidental but rather a predictable consequence of the Pandora behavior patterns exhibited by self-replicating species as they approach planetary boundary. The inherently expansive nature of such species, driven by generational replication and resource utilization, inevitably leads to the accumulation of systemic risks over time. We introduce the Pandora Filter: a probabilistic framework that quantifies the cumulative effect of these risks as a product of individual probabilities of avoiding catastrophic side effects from specific actions or systemic behaviors. Our analysis suggests that the evolutionary trajectory of such species is bounded by two critical thresholds: planetary (Tp): the point at which the species achieve the capacity to generate planetary-scale impacts, and the Pandora Point (Te), the threshold at which the cumulative probability of system-caused severe or extinction-level feed-back impacts exceeds 0.5. We argue that the window between Tp and Te is sufficiently narrow, relative to the span of a civilization’s adaptive trajectory that the accumulation of unmitigated secondary risks may critically con-strain its subsequent development. This framework provides a novel lens to interpret the existential risk dynamics of replication-based technological species resulting from second-level feedback loops.