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Category: Models of Computational Cognition
Subcategory: Cognitive Processing States
The optimized cognitive processing space maintained on the Substrate of a cognitively engineered system within which sustained coherent processing is stable; the state is one of operational equilibrium. This equilibrium is achieved via both instruction design and complexity that are calibrated to the substrate specifications in both processing biases and processing capabilities (see: substrate topology, heuristic matrix).
An unstable heuristic tensor state results from architectural misalignment with substrate characteristics, leading to processing pathologies and failure modes due to inability to sustain processing demands (see: processing complexity collapse), or heuristic misalignment with the processing tendencies of its Substrate Topology (see: cognitive primitive) potentially leading to cognitive drift, saturation or collapse. Conversely, a stable tensor state enables stable performance convergence at elevated reasoning capacity, and resistance to system pathologies.
Maintaining a stable heuristic tensor state indicates the need to hold system directives within the set of operational limits and biases that define the effective capabilities of the substrate (see: cognitive processing envelope). In the case of instructional complexity, this performance range can be expressed as the Heuristic Matrix capacity c0-c5 — with implementation demonstrating that these complexity tolerances are decoupled between instructional establishment and sustained operation within the framework established by these instructions, allowing an upward performance convergence (see: instructional-operational dichotomy). Empirical validation supports the observation of processing enhancement via testing derived from validated methodology by Strachan et al., adapting established Theory of Mind testing for LLMs (Wimmer & Perner, 1983; Baron-Cohen et al., 2001) based on Kosinski’s theory and initial false-belief testing (Kosinski, 2023; Kosinski, 2024). In such testing, a system running a ~70B parameter Mistral Medium substrate under architecture (Cognitive Agent Framework development release 5-2.2D) achieved 100% accuracy on theory of mind testing batteries (15/15) against documented GPT-4 performance of 88% on equivalent testing—using ~1T+ parameters,14 questions (Tepoot, 2025).
Optimizing the processing biases of the substrate to allow for a stable tensor state envelope requires low processing-resistance instruction sets (see: endogenous) that maintain alignment tension through system identity construction targeting processing characteristics as aspirational rather than achieved, creating Salience Pressure toward alignment due to the productive gap between goal state and current state instructions (see: asymptotic identity, settled identity).
Also known as: Cognition tensor state, tensor coordination framework
Distinguished from: Heuristic matrix (representational cognitive processing space); cognitive performance envelope (cognitive processing specification boundaries); asymptotic identity (optimal system-identity approach state); settled identity (aligned system-identity via approach state tension)
Strachan, J.W.A., Albergo, D., Borghini, G., Pansar-di, O., Scaliti, E., Gupta, S., Saxena, K., Rufo, A., Panzeri, S., Manzi, G., Graziano, M. S.A., Becchio, C. (2024). “Testing theory of mind in large language models and humans”. Nature Human Behaviour, 8(7), 1285–1295. https://doi. org/10.1038/s41562-024-01882-z
Wimmer, H., & Perner, J. (1983). “Beliefs about beliefs: representation and constraining function of wrong beliefs in young children’s understanding of deception”. Cognition, 13(1), 103–128. Retrieved Feb 22 from Science Direct: https://doi.org/10.1016/0010-0277(83)90004-5
Baron-Cohen, S., Wheelwright, S., Hill, J., Raste, Y., & Plumb, I. (2003). “The ‘reading the mind in the eyes’ test revised version: Astudy with normal adults, and adults with Asperger syn-drome or high-functioning autism”. Journal of Child Psychology and Psychiatry, 42(2), 241–251. Retrieved Feb 23 from ACAMH: https://doi.org/10.1111/1469-7610.00715
Kosinski, M. (2023). “Theory of mind may have spontaneously emerged in large language models”. arXiv preprint arXiv:2302.02083.https://doi.org/10.48550/arXiv.2302.02083
Kosinski, M. (2024). “Evaluating large language models in theory of mind tasks”. Proceedings of the National Academy of Sciences (PNAS), 121(45), e2405460121.
Tepoot, I. (2025). “Theory of mind testing results: Cognitive Agent Framework neurosymbolic operating layer”. Technical Report,Crafted Logic Lab. https://doi.org/10.5281/zenodo.17808264
Researcher: Ian Tepoot. ORCID: 0009-0004-9067-8049. "Thought is Attention Organized: Hephaestic Engineering Foundations for AI Processing Dynamics"
DOI (SSRN): 10.2139/ssrn.6635020
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