Paper and design correspondence

The package is based on Mapping Networks by Lord Sen and Shyamapada Mukherjee. The paper proposes replacing direct optimization in a large target parameter space with optimization of a compact latent vector.

Paper mechanism

The paper’s basic path is:

trainable latent vector
        -> fixed, modulated mapping network
        -> flattened target parameter descriptor
        -> reshaped target weights
        -> target network prediction

The mapping weights are fixed after orthogonal initialization. For the paper’s additive modulation, a mapping weight associated with latent coordinate z_i is adjusted by w_ij <- w_ij + alpha * z_i. Gradients optimize the latent vector rather than the target model.

The paper presents two training arrangements:

• Single Latent Vector Training (SLVT) maps one latent vector to all target parameters. It is simple but its fixed projection can become prohibitively large.

• Layer-wise Training (LWT) assigns smaller latent vectors and mappings to separate layers. This is the package’s planned default because it has a more practical memory profile.

The proposed Mapping Loss combines task, stability, smoothness, and alignment terms. Those losses will be implemented in a later milestone after the mapper and generator contracts are stable.

Package interpretation

The paper describes flattening, partitioning, and reshaping a generated vector each iteration. The package compiles that positional contract once as a ParameterSpec, then exposes named tensors as a ParameterTree. Names such as features.0.weight remain attached to tensors across subsystem boundaries.

Target execution is stateless. Generated parameters are supplied to torch.func.functional_call; the target module’s parameter objects are never replaced or assigned. Persistent buffers are cloned for each call so training-mode BatchNorm cannot mutate the wrapped target.

The complete design adds several engineering extensions around the paper:

• Layer-wise generation is the default, with SLVT retained as a baseline.

• Grouped generation will cover custom parameter partitions and shared fine-tuning modulation.

• Mapper, modulation, generator, and loss implementations use explicit interfaces and registries.

• Checkpoints will store latent and reconstruction state, not ephemeral generated target weights.

• Scientific claims are not treated as software guarantees. Tests establish tensor semantics, gradient flow, immutability, and reproducibility rather than universal model quality.

Current limitations

The implemented runtime rejects tied/shared target parameters and PyTorch parametrizations. Both features carry semantic constraints that a generator must preserve; silently expanding aliases or bypassing a parametrization would produce a different model. Support can be added later through an explicit alias/transformation-aware parameter specification.

Only the parameter runtime and stateless target wrapper are implemented today. The ergonomic training API shown in the package objective is planned, not yet available.