A language model has to understand many things at the same time when it reads a sentence: word meanings, grammar, context, and world knowledge. Consider the sentence “The bank is by the river”. The model must simultaneously consider:
- bank as a financial institution
- bank as a bench
- bank as a riverbank
- the grammatical role of the word
- the surrounding context from earlier sentences
The model evaluates all of these possibilities in parallel. However, they must ultimately be compressed into a single internal representation. It intensifies. Larger models know more and process more signals at the same time, but they still rely on the same single internal representation.
To understand this limitation, it is important to distinguish between embeddings and channels. An embedding is the initial vector representation of a token when it enters the model. A channel, by contrast, is the high-dimensional vector space through which these embeddings flow as the model processes them. As attention, feed-forward layers, and residual connections are applied, embeddings are continuously transformed and merged with context and intermediate reasoning results.
Concretely:
- each token is represented as a vector with hundreds or thousands of values
- each Transformer layer transforms this vector
- residual connections add the previous vector to the new one
The channel becomes a bottleneck, because the vector sizes increase, but not infinitely. Newer architectures such as the DeepSeek paper https://arxiv.org/pdf/2512.24880v2 attempt to address this by introducing multiple internal paths. Instead of mixing everything immediately, different kinds of information can flow in parallel for longer. However, parallelism alone is not sufficient. Without constraints, models tend to overuse some paths and neglect others, leading to imbalance and instability.
The key improvement is organized parallelism. By enforcing balanced usage of channels, all types of information get a fair chance to contribute before being combined. This allows models to preserve multiple interpretations longer and merge them more deliberately. Organized channels are sometimes confused with Mixture of Experts (MoE) architectures, but they solve different problems.
Mixture of Experts splits the network into multiple expert sub-networks. A router selects which experts process each token, activating only a subset at a time. MoE primarily improves scalability and specialization.
- MoE answers: “Which part of the network should process this token?”. MoE reduces competition for compute. Some call it Sparse (few active).
- Channels answer: “How can multiple interpretations coexist inside the model without interfering?”. Channels reduce competition between meanings. Some call it Dense (all active).
The central insight is clear: progress in language models does not come only from more parameters, more data, or more compute. It comes from structuring how information flows internally.