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How neural networks work
How neural networks work inside: interpretability, learning theory and the limits of what models can do.
- Bahdanau proposed a method for passing context in 2014
Dmitry Bahdanau from the University of Montreal developed the attention mechanism in 2014, which became the foundation for modern language models and transformers.
- PyTorch experiments fail to extract encrypted neural networks
By implementing encrypted neural networks within PyTorch weights, the authors prove that arbitrary computations remain hidden from fine-tuning and gradient-based search under cryptographic assumptions.
- ALHR reads only 30 keys per query
The ALHR method employs hierarchical routing to lower VRAM usage from 57 to 422 MB, achieving 92.1% top-1 accuracy at 1024 tokens with NlogN inference scaling.
- Five-outcome measurement violates explicit classical bound
The study demonstrates that quantum Bayesian retrodiction forecasts become incompatible with any classical Bayesian explanation when using a five-outcome measurement, whereas measurements with at most four outcomes admit a classical realization.
- Categorical analysis explains how LLMs operate on state space W
The paper presents a formal categorical framework analyzing how LLMs transform content into truth-evaluated propositions, arguing that models circumvent rather than solve the symbol grounding problem.
