Beyond Transformers: A Neuro-Symbolic and Quantum Hybrid Architecture Toward Artificial General Intelligence
Why this work is in the frame
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Bibliographic record
Abstract
Transformer architectures have revolutionized artificial intelligence, yet they face fundamental limitations that may prevent achieving artificial general intelligence (AGI): quadratic computational complexity, inability to learn continuously, lack of true temporal processing, and unsustainable energy requirements. We propose a novel theoretical framework-the Brain-Quantum-Symbolic (BQS) architecture-that transcends these limitations by unifying brain-inspired spiking neural networks with quantum computing principles and symbolic reasoning. Our framework introduces three key innovations: (1) a unified state representation combining spike-based temporal dynamics with quantum superposition, enabling processing of multiple hypotheses simultaneously; (2) a learning paradigm integrating spike-timing-dependent plasticity (STDP) with quantum interference feedback, achieving local, online learning without catastrophic forgetting; and (3) a symbolic knowledge base that grounds abstract reasoning in neural dynamics. We prove that our framework achieves O(k log n) complexity compared to transformers' O(n²), requires √n fewer samples for k-order reasoning tasks, and theoretically supports continuous learning while maintaining energy efficiency comparable to biological systems. We establish mathematical foundations showing how cognitive interference patterns enhance pattern recognition, temporal superposition enables multi-scale processing, and neuromorphic entanglement creates non-local dependencies essential for AGI. We present a comprehensive evaluation plan demonstrating that BQS reduces catastrophic forgetting by 40% on Split-CIFAR100 while achieving 3× energy efficiency compared to transformer baselines. This work provides both theoretical foundations and practical pathways for post-transformer architectures, establishing requirements for achieving AGI through convergence of biological, quantum, and symbolic computing principles.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it