ceceli33/titan-cognitive-core-v3: AKBASCORE PROMETHEUS — Persistent Neural Memory and Cross-Document Scientific Synthesis in Frozen Qwen2.5-72B: CUT3 Hidden-State Injection, SSD-Resident Memory Cartridges, Native Transformer Reconstruction and Causal Hypothesis Generation (TEST730)

AKBASCORE PROMETHEUS — TEST730

Persistent Neural Memory, Physical Cartridge Injection, Cross-Document Scientific Synthesis and Scientific Hypothesis Generation in a Frozen 72-Billion-Parameter Transformer

Research continuation of AKBASCORE Silicon Hippocampus / Model-Native Associative Memory (MAM)

Author and Developer: Mustafa Akbaş

AKBASCORE AI Technology

October 20, 2026

Copyright © 2026 Mustafa Akbaş.

RESEARCH CONTINUITY AND PRIOR DISCLOSURE

This release documents the next experimental stage of AKBASCORE Silicon Hippocampus, an independently developed research program investigating persistent numerical memory inside frozen transformer architectures.

It is a direct continuation of:

Research Record III — AKBASCORE Silicon Hippocampus v1.4

https://doi.org/10.5281/zenodo.23434686

The preceding record established the physical implementation of persistent numerical memory cartridges in Qwen2.5-72B-Instruct, including model-native hidden-state injection, layer-specific reconstruction, persistent storage, positional restoration, and exact memory-integrity controls.

PROMETHEUS builds on that foundation.

The central research question has advanced from whether a frozen transformer can physically reload and reconstruct previously stored internal numerical states to whether the same architecture can support a controlled workflow involving multiple independent scientific sources, model-generated interpretations, cross-document synthesis, a newly forged MASTER memory cartridge, and subsequent scientific questions without retraining the underlying model.

This release therefore documents an additional experimental capability of the existing MAM architecture rather than introducing an unrelated memory engine.

The original physical memory mechanism remains the computational foundation.

THE ENGINEERING PROBLEM

A conventional large language model ordinarily processes external information through a prompt or active context window.

Retrieval-augmented generation retrieves external material and supplies it to the model as text. Fine-tuning and parameter-efficient adaptation alter model parameters or introduce trained adapters. Ordinary K/V caching preserves attention states associated with an active inference sequence.

AKBASCORE investigates a different implementation route.

The objective is to separate memory creation from memory use.

Source material is processed once through a frozen transformer. Selected internal numerical representations are extracted and committed to persistent storage. The source text does not need to be supplied again with each later question.

When the memory is needed, the system loads the saved numerical checkpoint, restores the necessary lower-layer attention state, injects the saved hidden-state stream at a defined transformer boundary, and allows the unchanged upper transformer layers to reconstruct the remaining operational memory.

The model then answers questions through its native computational pathway.

This is not a claim that the transformer has acquired new permanent parameter knowledge. The persistent memory resides in external numerical artifacts that can be loaded into the model's computation.

The distinction is fundamental: the stored object is neither an ordinary document nor a newly trained set of model weights.

DEVELOPMENT OF THE SILICON HIPPOCAMPUS ARCHITECTURE

The AKBASCORE research line progressed through model-specific implementations and controlled experiments.

Research Record I — Qwen2.5-7B:

https://doi.org/10.5281/zenodo.23381336

Research Record II — Mistral-7B / Silicon Hippocampus v1.4:

https://doi.org/10.5281/zenodo.23418927

Research Record III — Qwen2.5-72B / Silicon Hippocampus:

https://doi.org/10.5281/zenodo.23434686

These records document the progression from smaller frozen transformer experiments to persistent, model-native memory reconstruction in a 72-billion-parameter architecture.

The implementations use model-specific intervention boundaries.

For example, the Mistral-7B implementation uses a CUT6 boundary, while the Qwen2.5-72B implementation uses CUT3.

These boundaries are not presented as universally interchangeable. Their numerical behavior and reconstruction requirements depend on the target architecture.

PROMETHEUS uses the established Qwen2.5-72B CUT3 implementation and extends the experimental workflow to three independently stored academic sources and a synthesized MASTER cartridge.

FROZEN MODEL AND EXECUTION ENVIRONMENT

The experimental model is:

Qwen/Qwen2.5-72B-Instruct

Architecture:

80 transformer layers.

Hidden dimension: 8,192.

Query attention heads: 64.

Key/value attention heads: 8.

Head dimension: 128.

The model uses NF4 four-bit weight quantization with BF16 computation and attention K/V representations.

The experimental execution environment includes an NVIDIA A100-SXM4-80GB GPU.

The model weights remain frozen.

No fine-tuning, LoRA training, gradient-based memory writing, or intentional modification of the pretrained model parameters is part of the demonstrated PROMETHEUS protocol.

Memory is written, stored, reloaded, and activated through the existing model-native computational mechanism.

THE PHYSICAL MEMORY CARTRIDGE

The essential AKBASCORE MAM object is a layer-bounded numerical memory cartridge.

For Qwen2.5-72B, the selected intervention boundary is CUT3.

Each cartridge preserves:

The hidden-state stream H3, corresponding to the output of transformer layer 3. The key tensors of layers 0–3. The value tensors of layers 0–3. The associated memory positions, identity information, and integrity records required by the implementation.

The saved artifact is deliberately smaller than a complete 80-layer operational K/V memory.

The remaining attention states are reconstructed when the cartridge is mounted.

A simplified description is:

SOURCE TEXT

→ TOKENIZATION

→ FROZEN TRANSFORMER FORWARD COMPUTATION

→ EXTRACTION OF H3 AND L0–L3 K/V

→ PHYSICAL CARTRIDGE FILE

→ SSD PERSISTENCE

→ VERIFIED RELOAD

→ CUT3 HIDDEN-STATE INJECTION

→ FROZEN L4–L79 RECONSTRUCTION

→ NATIVE MODEL ANSWER GENERATION

The lower-layer numerical checkpoint and the frozen model jointly define the memory read mechanism.

The cartridge is not an independently intelligent object. It requires the compatible transformer, positional conventions, tensor formats, and reconstruction procedure.

MEMORY WRITING AND PERSISTENCE

During the write phase, the source text is converted into tokens and processed by the frozen model.

The implementation captures the hidden-state output at the selected CUT3 boundary together with the lower-layer K/V tensors.

The resulting numerical representations are serialized to a physical file on persistent storage.

Each cartridge is associated with a physical identity and SHA-256 integrity information.

The write process does not require changing the pretrained weights.

The memory can subsequently be loaded from its physical file rather than regenerating the original source context from text for each question.

The PROMETHEUS experiment uses four physical cartridges:

P01 — First academic source.

P02 — Second academic source.

P03 — Third academic source.

MASTER — Model-generated cross-document synthesis memory.

The three source cartridges and the MASTER cartridge are distinct physical artifacts.

NATIVE MEMORY RELOAD AND TRANSFORMER RECONSTRUCTION

At reload time, the saved numerical checkpoint is restored.

The implementation reconstructs the necessary positional alignment, including rotary positional embedding treatment where required.

The saved H3 stream is placed at the corresponding internal layer boundary.

The original frozen transformer then performs the remaining computation through layers 4–79.

This reconstructs the upper-layer K/V representations required for normal attention-based generation.

The process uses the model's own transformer operations rather than substituting an external answer engine.

In the demonstrated architecture, the physical cartridge represents approximately 10% of the corresponding full 80-layer memory footprint, with the remainder reconstructed at mount time.

This is a storage-versus-computation tradeoff.

It reduces persistent storage requirements relative to saving the complete operational attention memory, while retaining the cost of upper-layer reconstruction and runtime GPU memory allocation.

THREE REAL SCIENTIFIC PUBLICATIONS

PROMETHEUS uses three real academic publications retrieved through Europe PMC.

The experiment verifies the available source metadata and uses licensed full-text excerpts as the source material.

P01

Generative AI in creative education: baseline-linked evidence on learning processes, artistic performance, and authorship.

PMCID: PMC13672588

Source body: 1,924 tokens.

P02

The critical-thinking paradox in generative AI-integrated learning: distinguishing efficiency from cognitive depth.

PMCID: PMC13521781

Source body: 1,987 tokens.

P03

Catalysts of Change: Using AI to Lower the Activation Energy for Developing Gamified Learning Experiences in Health Profession Education.

PMCID: PMC13043820

Source body: 2,036 tokens.

The three publications address different but related aspects of artificial intelligence in education.

They provide material concerning creative learning, perceived versus actual educational outcomes, critical thinking, cognitive depth, and AI-assisted instructional design.

The source excerpts are preserved as separate numerical cartridges.

The original article identities, source attribution, license information, and excerpt SHA-256 values are documented in the accompanying figures and experimental records.

FROM INDEPENDENT SOURCES TO MODEL-GENERATED REFLECTIONS

Each source cartridge is activated separately.

The frozen model reads the source through its reconstructed native attention state and produces a textual reflection.

The three recorded reflections contain approximately:

P01 reflection: 3,953 characters.

P02 reflection: 3,610 characters.

P03 reflection: 3,594 characters.

These reflections are model-generated interpretations of the respective source materials.

They are not direct copies of the original articles, and their scientific accuracy is not assumed solely because the underlying cartridge passes physical integrity checks.

The important architectural separation is that each source is first stored and processed independently.

PROMETHEUS does not simply concatenate three source K/V tensors and call the resulting tensor sequence a scientific synthesis.

Instead, it uses the frozen model to produce intermediate textual interpretations.

CROSS-DOCUMENT SYNTHESIS AND MASTER MEMORY CREATION

The three model-generated reflections are supplied as text to a subsequent synthesis stage.

The frozen model produces a cross-document synthesis discussing common themes, differences, tensions, and possible relationships among the sources.

The recorded synthesis contains approximately 3,794 characters.

This stage includes comparisons involving:

Perceived educational support versus measured learning outcomes.

Short-term efficiency versus deeper cognitive processing.

Assistive versus content-generative uses of artificial intelligence.

Educator and student awareness of benefits and limitations.

Contextual constraints affecting educational implementation.

The synthesis is then incorporated into a new memory body together with the three reflections.

The MASTER memory body contains 2,848 tokens.

A new CUT3 cartridge is forged from this material using the same physical memory-writing mechanism.

This produces MASTER.pt.

The critical distinction is:

P01, P02, and P03 are independent source memories.

The synthesis is model-generated text.

MASTER is a newly forged physical numerical memory of that synthesis material.

The process is text-mediated synthesis followed by model-native numerical memory encoding.

It is not direct tensor fusion of the three source cartridges.

MASTER MEMORY ACTIVATION

The MASTER cartridge is committed to SSD storage and verified using physical integrity controls.

In the recorded experiment, its file size is approximately 94.3 MB.

The cartridge contains the H3 hidden-state representation and the K/V tensors of layers 0–3 for the MASTER body and canonical prefix.

The recorded MASTER memory contains 2,879 positions.

Upon activation, the saved numerical representations are loaded and the frozen upper transformer reconstructs the remaining operational attention state.

The resulting full 80-layer K/V memory is approximately 943.4 MB.

The three original source cartridges remain separately preserved on SSD.

During the six-question evaluation, the experiment uses the MASTER memory rather than simultaneously mounting all three source memories as one tensor-fused object.

This demonstrates a distinction between persistent source storage, intermediate synthesis, and subsequent activation of a derived memory cartridge.

SCIENTIFIC QUESTIONS AND REASONING PROBES

The original TEST730 protocol includes six presealed questions.

Three are human-level academic comparison questions:

H1 — Identify the main conclusions of the three publications and where they disagree.

H2 — Distinguish empirically supported claims from major limitations.

H3 — Produce an independent comparative conclusion, explain uncertainties, and cite source identifiers.

Three are advanced scientific reasoning probes:

A1 — Propose a causal model reconciling apparently conflicting findings, separating evidence, assumptions, and predictions.

A2 — Design a preregistered randomized experiment capable of falsifying the proposed causal model.

A3 — Identify a consequential unresolved contradiction, develop rival explanations, and propose a decisive test.

The question panel was sealed with SHA-256 before the recorded evaluation.

The recorded experiment generated responses to all six questions using the MASTER memory.

All six responses reached the configured 768-token generation limit.

The experiment therefore demonstrates response generation under the defined conditions, but does not establish that every response was complete or independently correct.

The recorded status is:

DEMO_COMPLETED_UNGRADED

The original run reports 58/58 logged checks passing.

These checks concern the implemented experimental and integrity conditions. They should not be interpreted as a 58-question semantic accuracy benchmark.

MODEL-GENERATED SCIENTIFIC HYPOTHESES

The advanced questions produced model-generated research proposals.

These included a causal account of differences between perceived AI assistance and actual learning outcomes, a randomized educational intervention comparing different modes of AI use, and rival explanations involving cognitive offloading and alignment between AI capabilities and educational objectives.

The generated proposals include possible variables, intervention arms, outcome measures, confounders, predictions, and discriminating observations.

This is a significant part of the PROMETHEUS workflow.

The same frozen transformer architecture is used to process independent scientific sources, generate intermediate interpretations, construct a persistent synthesis memory, and respond to questions requesting new scientific hypotheses.

However, the generated hypotheses are research proposals.

Their originality relative to all existing literature, empirical validity, and scientific usefulness have not been independently established by TEST730.

PROMETHEUS documents their production and provenance rather than presenting them as experimentally confirmed scientific discoveries.

LIVE GPU REPRODUCTION

The subsequent live demonstration reloaded the physical cartridges and verified their integrity against the recorded experimental artifacts.

The figures report matching physical file SHA-256 values for P01, P02, P03, and MASTER.

The live session also reports 80/80 transformer-layer parity and unchanged frozen model weights.

Two sealed questions were reproduced:

H1 — BYTE-IDENTICAL to the recorded TEST730 output.

A1 — BYTE-IDENTICAL to the recorded TEST730 output.

Both generated 768 tokens.

The observed live generation times were approximately:

H1: 112.0 seconds, 6.85 tokens per second.

A1: 112.3 seconds, 6.84 tokens per second.

BYTE-IDENTICAL refers to reproducibility of the recorded output.

It is evidence of deterministic reproduction under the tested configuration, not independent proof of the answer's semantic correctness.

NEW QUESTIONS WITH AND WITHOUT MASTER MEMORY

The live demonstration also introduced two additional questions that were not part of the original six-question sealed panel.

N1 asked which source described 133 undergraduate art students and distinguished assistive from content-generative AI use.

N2 asked which source proposed the critical-thinking paradox and what that paradox claimed.

The experiment compared the frozen model with MASTER memory against the corresponding no-memory condition.

For N1, the MASTER condition identified P01 and the relevant distinction between assistive and content-generative AI.

For N2, the MASTER condition identified P02 and described the tension between short-term performance improvements and deeper cognitive processing.

The no-memory responses did not reproduce the same complete source-specific information.

These observations provide a limited behavioral comparison showing that the mounted memory condition can affect source-specific answers.

They do not, by themselves, constitute a statistically powered, blinded causal benchmark across a broad question distribution.

The distinction between physical memory verification, reproducible output generation, and semantic evaluation is maintained throughout this release.

REAL GPU PERFORMANCE AND MEMORY MEASUREMENTS

The accompanying scientific figures include GPU measurements collected during the live session.

For the MASTER cartridge, the reported measurements include:

SSD read: 0.056 seconds.

Verified load: 0.614 seconds.

Host-to-GPU transfer: 0.028 seconds.

Native 80-layer initialization: 7.197 seconds.

Prefix tensor seals: 2.98 seconds.

Layer parity check: 10.59 seconds, 80/80.

The live session reports approximately 39.31 GiB of PyTorch-allocated GPU memory in the MASTER-only state.

These are measured operations from the specified session, not universal performance guarantees.

The original TEST730 run also records six answer-generation times totaling approximately 600.6 seconds, with a mean of approximately 100.11 seconds per question.

Recorded timings and subsequent live timings are shown separately.

NEURAL X-RAY AND PHYSICAL STATE ANALYSIS

PROMETHEUS includes a read-only examination of the model's internal attention-memory representations.

The X-ray analysis measures K/V tensor norms across all 80 transformer layers.

The source cartridges are examined in separate GPU states.

The MASTER cartridge is also examined independently.

The measurements include layer-wise statistics and heat maps of the numerical attention-memory states.

The analysis compares physical representation patterns across the three sources and MASTER while accounting for differences in sequence length.

The figures do not assume token-by-token alignment between independently generated source and MASTER memories.

Accordingly, the observed K/V differences are interpreted as differences in internal numerical states, not direct measurements of intelligence, consciousness, scientific novelty, or synthesis correctness.

The raw X-ray measurements and their derived visualizations provide an additional reproducibility and inspection layer for the physical memory mechanism.

INTEGRITY AND REPRODUCIBILITY

The experimental workflow distinguishes several independent verification targets:

Physical cartridge identity.

File-level SHA-256 integrity.

Tensor-level integrity.

Frozen model weights.

Preservation of source cartridges.

Native reconstruction behavior.

80-layer attention-memory parity.

Reproduction of recorded generated responses.

The original TEST730 run reports 58/58 checks passing.

The subsequent live session reports 43/43 executed checks passing.

The two counts describe different sets of executed checks and are not interchangeable.

Some original experiment operations, including initial cartridge creation and generation of the complete six-question panel, were not repeated in the later live demonstration.

The records preserve that distinction.

This separation is important for reproducibility: a recorded result is not automatically represented as a newly executed result.

WHAT PROMETHEUS ADDS TO THE PREVIOUS MAM RESEARCH

The preceding Silicon Hippocampus work established the physical ability to save, reload, inject, reconstruct, and verify numerical transformer memory without changing model weights.

PROMETHEUS adds a documented experimental workflow involving:

Three independently stored scientific publications.

Three separately generated model reflections.

A model-generated cross-document synthesis.

A derived physical MASTER memory cartridge.

Subsequent scientific comparison and hypothesis-generation questions.

Reproduction of recorded answers.

Live MASTER versus no-memory comparisons.

Full-layer numerical X-ray inspection.

SSD persistence, SHA-256 provenance, and GPU performance measurements.

The resulting architecture connects external physical memory storage to the frozen transformer's internal computational pathway.

The innovation claimed as the author's implementation contribution lies in this documented combination of physical memory checkpointing, layer-specific internal-state reinjection, native upper-layer reconstruction, controlled memory persistence, and multi-stage scientific synthesis.

This release does not assert that these components are individually unprecedented or that the combination has been determined patentable against all prior art.

SCIENTIFIC SCOPE AND EXPERIMENTAL BOUNDARIES

The recorded experiments support the physical persistence and reconstruction of numerical memory cartridges in the stated transformer configuration.

They also document model-generated synthesis and scientific proposals produced through the MASTER memory workflow.

The present evidence does not independently establish:

Unrestricted autonomous learning.

Zero-prompt scientific discovery.

Elimination of hallucinations.

Universal semantic correctness.

Superhuman reasoning performance.

Unlimited memory capacity.

Generalization of CUT3 to every transformer architecture.

The broader research objective is to develop systems capable of persistent memory, evidence-sensitive synthesis, error detection, and adaptive reasoning while preserving a stable pretrained computational core.

PROMETHEUS is an experimental step toward that objective.

SOURCE CODE, RAW LOGS AND REPRODUCTION

The implementation and experimental evidence are publicly referenced below.

Main repository:

https://github.com/ceceli33/titan-cognitive-core-v3

Primary physical memory engine:

https://github.com/ceceli33/titan-cognitive-core-v3/blob/main/709.py

Original 72B physical memory experiment:

https://github.com/ceceli33/titan-cognitive-core-v3/blob/main/709.log

PROMETHEUS TEST730 source:

https://github.com/ceceli33/titan-cognitive-core-v3/blob/main/730.py

PROMETHEUS TEST730 original experiment log:

https://github.com/ceceli33/titan-cognitive-core-v3/blob/main/730.log

PROMETHEUS 1.5 demonstration, Part 1:

https://github.com/ceceli33/titan-cognitive-core-v3/blob/main/Qwen72B_part1_1.5.log

PROMETHEUS 1.5 demonstration, Part 2:

https://github.com/ceceli33/titan-cognitive-core-v3/blob/main/Qwen72B_part2_1.5.log

PROMETHEUS 1.5 demonstration, Part 3:

https://github.com/ceceli33/titan-cognitive-core-v3/blob/main/Qwen72B_part3_1.5.log

PROMETHEUS 1.5 demonstration log:

https://github.com/ceceli33/titan-cognitive-core-v3/blob/main/Qwen72B_1.5.log

Official license:

https://github.com/ceceli33/titan-cognitive-core-v3/blob/main/LICENSE

The original experiment, demonstration implementation, and associated logs are separate evidence artifacts.

Readers wishing to reproduce the experiment should inspect the original engine and recorded experiment before executing the demonstration.

The three demonstration parts are intended to be used together in their documented order.

ACCOMPANYING SCIENTIFIC FIGURE DOCUMENT

This Zenodo release includes an author-assembled PDF containing 13 scientific figures.

The PDF is a visual technical record of the experimental architecture, physical memory operations, source provenance, model-generated synthesis, verification results, and live measurements.

It is not represented as an independent peer-reviewed paper.

The figures are:

Fig. 1 — Three Real Publications, One Physical MASTER Memory.

Fig. 2 — Three Verified 2026 Publications.

Fig. 3 — Physical Neural Memory Injection Protocol.

Fig. 4 — Silicon Hippocampus: Persistent SSD and Live GPU Memory.

Fig. 5 — Physical Source Memory X-Ray.

Fig. 6 — Source Cartridges vs Synthesized MASTER.

Fig. 7 — From Three Academic Perspectives to a New Synthesis.

Fig. 8 — MASTER Cartridge: Write, Seal, Reload.

Fig. 9 — Six Presealed Academic Questions.

Fig. 10 — From Stored Synthesis to New Scientific Hypotheses.

Fig. 11 — MASTER Memory Fast-Path: Measured Performance.

Fig. 12 — Integrity, Reproducibility and Experimental Boundaries.

Fig. S1 — Live MASTER Queries in This Session.

Together, these figures illustrate the end-to-end experimental process from source acquisition to physical memory creation, numerical injection, model-generated synthesis, derived MASTER memory, question answering, GPU measurements, and integrity verification.

INTELLECTUAL PROPERTY, PRIOR DISCLOSURE AND LICENSE

Copyright © 2026 Mustafa Akbaş.

The original AKBASCORE implementation, explanatory materials, software expression, figures, and research documentation are identified as the author's contributions, subject to applicable law, the operative repository license, and any pre-existing third-party rights.

This release forms part of a dated public technical disclosure and research chronology.

The earlier AKBASCORE records are expressly acknowledged as preceding disclosures. PROMETHEUS is presented as a continuation of that work, not as an attempt to claim that its underlying MAM mechanism was first disclosed in this release.

The operative software license is the license published in the official repository:

https://github.com/ceceli33/titan-cognitive-core-v3/blob/main/LICENSE

The previously documented AKBASCORE RESEARCH SOFTWARE LICENSE permits research, educational use, reproduction, modification, and certain commercial uses under its stated conditions.

Under the previously documented revenue-based licensing framework, commercial use by organizations whose combined consolidated annual gross revenue is below USD 50,000,000 is permitted subject to the operative license. Commercial use at or above that threshold requires a separate commercial license from the copyright holder.

The threshold refers to consolidated annual gross revenue, not company valuation, investment received, or market capitalization.

The operative LICENSE file controls over any summary in these release notes.

Earlier AKBASCORE materials released under the MIT License retain their previously granted permissions. This release does not retroactively revoke those permissions.

Third-party model weights, tokenizers, libraries, academic source publications, and other external materials remain subject to their respective licenses.

The publication of this technical disclosure documents the author's implementation and research record. It is not a patent application, patent grant, determination of worldwide novelty, or assertion of exclusive rights over independently developed methods.

Potential patent rights, priority questions, prior-art effects, and jurisdiction-specific filing requirements must be evaluated separately.

CONCLUSION

AKBASCORE PROMETHEUS extends the Silicon Hippocampus research line from verified physical numerical memory in a frozen 72-billion-parameter transformer to a multi-stage scientific synthesis workflow built on the same memory mechanism.

Three real academic publications are independently encoded into persistent physical cartridges.

The frozen transformer generates separate reflections, constructs a cross-document textual synthesis, and encodes the resulting material into a new MASTER cartridge.

The MASTER cartridge is stored on SSD, verified, reloaded, injected into the transformer at CUT3, and used to answer academic questions through native frozen-model computation.

The accompanying experimental record documents physical integrity, numerical reconstruction, output reproducibility, GPU measurements, and selected memory-dependent behavioral comparisons.

The research contribution is a concrete, inspectable implementation connecting persistent numerical memory with the internal computational pathway of a frozen transformer and a controlled scientific synthesis workflow.

The next research frontier is not merely storing more information. It is determining how reliably such a system can select evidence, distinguish correct from incorrect memories, test competing explanations, detect its own errors, and adapt its reasoning under new conditions.

AKBASCORE PROMETHEUS provides an experimentally documented foundation for investigating those questions.

Research lineage:

https://doi.org/10.5281/zenodo.23381336

https://doi.org/10.5281/zenodo.23418927

https://doi.org/10.5281/zenodo.23434686

Repository:

https://github.com/ceceli33/titan-cognitive-core-v3

Copyright © 2026 Mustafa Akbaş. All rights reserved except as expressly licensed under the applicable operative license and law.