minmaxrnc 0.1.1

MinMax Recurrent Neural Cascade

MinMax Recurrent Neural Cascades

A parallelisable recurrent sequence model built on the MinMax operator — expressively powerful, efficiently implementable, and provably not affected by vanishing or exploding gradient.

Resources:

• Paper for the formal description and analyses.
• Repository including architecture reference in docs/model.md.

Key properties

• Perfect memory. MinMax neurons can store and retain information arbitrarily long (formal expressivity: all group-free functions).

• Parallel training. All hidden states across a sequence of length T are computed simultaneously in O(log T) depth, with no sequential bottleneck.

• Efficient inference. Runs as a true RNN: O(1) compute and O(D) memory per token, making it practical for long-context streaming generation.

• Stable recurrence. The MinMax operator is bounded and its gradients cannot vanish or explode through the state path.

The model

Each layer contains three sub-modules applied with pre-norm and residual connections:

1. MinMax Neuron — the recurrent cell, updating a hidden state x_{t+1} = max(min(r_t, x_t), s_t) element-wise in parallel via a prefix scan.
2. Convolution — one-step causal mixing.
3. Feed-forward network — feature mixing (gated or standard MLP).

Installation

pip install minmaxrnc

PyTorch (≥ 2.0) is required. For GPU support, follow the PyTorch installation guide before installing this package.

Quick start

Sequence backbone

import torch
from minmax import MinMaxRNC, MinMaxRNCConfig

model = MinMaxRNC(MinMaxRNCConfig.medium())   # d_model=512

u = torch.randn(batch_size, seq_len, 512)

# Parallel over the full sequence (training)
y = model(u, unroll_steps=seq_len)            # (B, T, 512)

# Carry state across calls (streaming inference)
y, state = model(u, unroll_steps, return_state=True)
y_next   = model(u_next, unroll_steps, state=state)

Language model

import torch
from minmax import MinMaxRNC_LM, MinMaxRNCLMConfig, MinMaxRNCConfig

model = MinMaxRNC_LM(
    vocab_size = 50257,
    cfg = MinMaxRNCLMConfig(backbone=MinMaxRNCConfig.medium()),
)

tokens = torch.randint(0, 50257, (batch_size, seq_len))
logits = model(tokens, unroll_steps=seq_len)      # (B, T, vocab_size)

# Autoregressive generation
logits, state = model(tokens[:, :1], unroll_steps=seq_len-1, return_state=True)
for _ in range(max_new_tokens):
    next_tok = logits[:, -1].argmax(-1, keepdim=True)
    logits, state = model(next_tok, unroll_steps=1, state=state, return_state=True)

Custom configuration

from minmax import MinMaxRNC, MinMaxRNCConfig

cfg = MinMaxRNCConfig(
    d_model          = 768,
    n_layers         = 12,
    d_state          = 192,       # hidden-state dimension per neuron
    norm             = 'rmsnorm',
    ffn_type         = 'gated',
    ffn_act_fn       = 'swish',   # → SwiGLU
    output_gate      = True,
    use_postlayers_ffn = True,
)
model = MinMaxRNC(cfg)

Preset sizes

Preset	d_model	n_layers	d_state	Parameters (backbone)	Parameters (LM, GPT-2 vocab)
small	90	2	40	~0.1 M	~4.6 M
medium	512	8	512	~16.6 M	~42.4 M
large	728	12	1456	~75.9 M	~112.5 M

Running the tests

pytest

How to cite

@misc{ronca2026minmaxpaper,
      title={{MinMax} Recurrent Neural Cascades},
      author={Alessandro Ronca},
      year={2026},
      eprint={2605.06384},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2605.06384},
}
@software{ronca2026minmaxcode,
  author  = {Alessandro Ronca},
  title   = {{MinMax} Recurrent Neural Cascades},
  year    = {2026},
  url     = {https://github.com/minmaxrnc/model},
  version = {0.1.0},
}

License

This project is source-available under the PolyForm Noncommercial License 1.0.0.

You may use, copy, modify, and distribute this software only for non-commercial purposes under the terms of that license.

Commercial use is not permitted without a separate commercial license from the copyright holder.

For commercial licensing, contact:

Alessandro Ronca [e-mail]

Third-party dependencies

This project depends on third-party software, including Python and PyTorch. These dependencies are licensed separately by their respective copyright holders.

See THIRD_PARTY_NOTICES.md for details.