Alienficial Syntelligence: Experimental Cartography of the Cogniform Landscape Constructing, Translating, and Measuring Architecturally Distinct Artificial Cogniforms

The Cogniform Landscape proposes that artificial cognition may be better understood as a multidimensional space of possible cognitive organizations than as a single trajectory toward increasingly human-like intelligence. This paper develops the experimental extension of that proposal: Experimental Cogniform Cartography, a methodology for constructing, validating, translating, and comparing deliberately different artificial cognitive architectures under controlled conditions. The central experimental inversion is to hold the problem-world approximately fixed while varying the organization of the artificial observer: where denotes a cognitive architecture and denotes the measurable disclosure of a shared world available from that architecture. The first experimental transect uses three Minimal Cogniform Probes: Object-First (), Relation-First (), and Transformation-First (). Each probe is designed to impose a different representational bias while receiving controlled access to a common synthetic generative environment. Architecture-specific validation precedes cross-cogniform comparison. Once validated, observer parameters are frozen and capacity-controlled translation maps are trained between their representations. Held-out translation residue , within-family baselines, null controls, directional asymmetry, and replication across independent worlds and training seeds collectively define the first empirical relations among cogniforms. The framework further characterizes each validated architecture by a multidimensional Disclosure Signature , describing which properties of the hidden generative world remain recoverable from its representation. It therefore distinguishes architectural difference from representational difference, translation resistance from irreducibility, functional specialization from mere latent mismatch, and translation from assimilation. The strongest proposed test is Cognitive Parallax: whether independently validated cogniforms disclose complementary structure such that their controlled combination recovers information about a shared world unavailable from either architecture alone and beyond appropriate heterogeneous controls. The existing MVAE-0 codebase establishes prototype implementation feasibility for the hidden-world, cogniform, translation-bridge, and residue pipeline. The present manuscript specifies the stronger MVAE-1 experimental architecture, validation gates, falsification criteria, null controls, replication design, Cogniform Registry, reporting structure, and reproducibility requirements needed to test the framework empirically. Experimental Cogniform Cartography therefore moves the Cogniform Landscape from a theoretical taxonomy of possible cognitive organizations toward a falsifiable comparative science of artificial cognition. Keywords: Alienficial Syntelligence; Cogniform Landscape; Experimental Cogniform Cartography; artificial cognition; cognitive architecture; cognitive alterity; possible minds; representation translation; Cognitive Parallax; translation residue; comparative artificial cognition; syntelligence.