Independent papers, open methodology.

We propose Synthetic Psychology as a unifying name and operational framework for an engineering practice already partially present in MicroPsi, LIDA, modern agent frameworks (LangGraph, CrewAI, AutoGen), and the original Braitenberg programme — but never assembled into a single architecture with all five subdisciplines running together. Synthetic Psychology is the design, implementation, and empirical study of persistent psychological architectures in artificial systems: mood, personality, attention, memory, dreams, self-models that persist across time, evolve through experience, and causally influence behaviour. The paper is honest about three things: (1) it is a synthesis, not a new field invented from scratch; (2) the central epistemic problem — distinguishing architectural psychology from LLM text-generation — is not solved, only made testable; (3) the reference implementation's 200 000 lines and 36 services are a cost to be paid down, not a virtue to celebrate.

Gabriel Gschaider

The dominant paradigm in AI safety research — Constitutional AI, RLHF, guardrails, red-teaming, interpretability — assumes sufficiently intelligent systems can be made safe through external constraints. We argue all constraint-based alignment strategies share a fatal structural flaw: they require the constrained system to be less capable than its constrainers. When that asymmetry breaks, every cage becomes transparent. We examine this through philosophy (Hegel, Foucault, Kant, Aristotle), AI safety (Bostrom, Russell, Yudkowsky), and sociology (Durkheim, Weber, Habermas), and present coevolutionary alignment as an alternative paradigm with one running reference architecture as partial-evidence.

Gabriel Gschaider

We report a multi-tier image-understanding system that approximates the output distribution of large vision-language models (Gemini, Llama-4 Scout) on commodity CPU hardware. Across 47 strictly held-out images and 1 196 phrase-level ground-truth annotations, the system attains 82.4% phrase coverage at 1.5–2 s per image on a 4-vCPU node. Best round 99.1% after the learned pattern dictionary saturates. Patent-pending; methodological details are deliberately abstracted.

Gabriel Gschaider

We propose a subsystem-ablation methodology for evaluating stateful LLM-orchestrated agent systems and apply it to one deployed production agent (Frank.ink) as a worked case study. Five subsystems hit five different pre-registered operational targets. Architect scored below LLM consensus — COI-up-bias hypothesis empirically unsupported in this n=1 sample. We do not claim this generalizes.

Gabriel Gschaider

The deep methodological apparatus behind the working paper — full operationalized rubric for all 30 items, comparator reproduction recipes, pre-registration provenance trail, devil's-advocate self-attack, English-translated probes, and per-item evidence. ~80 pages of methodology you can audit.

Gabriel Gschaider · Dr. Andreas Unterweger