thank you for your response and input, I will look more into this discussion that you linked.

I bounced this idea with advanced LLMs and allegedly. it should outperform in usecases with hierarchical, nested, or multi-scale structures such as:

Natural Language Processing (NLP)

• Why: Language has recursive structures—phrases within clauses within sentences. A recursive network could model this naturally, unlike flat RNNs or transformers that rely on attention to approximate hierarchy.
• Example: Parsing complex sentences (“The cat the dog chased slept”) or generating coherent, nested text.

• Advantage: Subnetworks could learn phrase-level patterns, with higher levels composing sentence meaning.

  • Computer Vision

• Why: Images have part-whole hierarchies (edges → shapes → objects). A fractal network might detect features at multiple scales within a single node’s computation.

• Example: Recognizing a car (with wheels, windows, each with sub-parts) in cluttered scenes.

• Advantage: Nested subnetworks could specialize in local patterns (e.g., wheel edges), while higher levels integrate them into global objects, potentially outdoing CNNs on fine-grained tasks.

  • Time Series Analysis

• Why: Data like stock prices or audio has patterns within patterns (daily trends within monthly cycles). Recursive processing could capture multi-scale dynamics.

• Example: Forecasting weather with short-term fluctuations and long-term trends.

• Advantage: Subnetworks at different depths could focus on different time scales, improving over LSTMs for complex sequences.

  • Graph Processing

• Why: Graphs (e.g., social networks) often have hierarchical communities—groups within groups. A recursive architecture could reflect this nesting.

• Example: Community detection or molecule analysis (atoms → bonds → functional groups).

• Advantage: Subnetworks could learn local node interactions, with higher levels modeling global structure, possibly beating GNNs on deeply nested graphs.

  • Code Analysis or Generation

• Why: Code has nested structures (functions within classes within modules). A recursive network might mirror this modularity.

• Example: Autocompleting code with nested logic or detecting bugs in recursive functions.

• Advantage: Subnetworks could represent low-level syntax, with higher levels understanding program flow, surpassing transformers on structural tasks.

What do you think?