Large Signal Model software

Komorebi Large Signal Model

Projector-Resolved Large Algebraic Signal Model · 2.3.0

PR-LASM is an executable, non-LLM algebraic signal model. It separates a reconstructible coordinate archive from statistical state, learns coefficients and weights over a finite typed operator grammar, and provides explicit memory and planning on controlled interactive systems.

This release also exposes a standalone real-valued signal interface with polyphase transfer blocks, causal streaming FIR, complete packet refinement, and versioned source-signal storage. These signal operations do not require an ARC runtime or a hosted model.

Status: Beta / work in progress. The scoped core release has recorded verification on Linux / Python 3.13.5. Original Komorebi code is available under the MIT license in LICENSE; third-party terms remain as stated in THIRD_PARTY_NOTICES.md. Research and performance claims retain their recorded finite scope.

Start here

Install in a separate environment. Do not co-install the separate ARC3 agent package komorebi-arc3-offline, which owns the same compatibility namespace.

py -3.13 -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install -e ".[test]"
python -m komorebi_pr_lasm self-check
python -m pytest -q -m "not local_suite"

The dependency installation above may need internet access. An offline install can use the supplied wheel with --no-index --no-deps when compatible NumPy and SciPy are already installed. The package itself does not download a model.

python -m pip install --no-index --no-deps .\dist\komorebi_pr_lasm-2.3.0-py3-none-any.whl
python -m komorebi_pr_lasm self-check

In the full handoff, dist is beside the source directory. Adjust the wheel path to that location. The source-only archive does not contain dist.

A real signal, not a quantized image

import numpy as np
from komorebi_pr_lasm import analyze_signal, PolyphaseFIR, StreamingFIR

x = np.sin(np.arange(257) * 0.13)
analysis = analyze_signal(x, modulus=4)
print(analysis.reconstruction_residuals())

h = np.array([0.25, 0.5, 0.25])
bank = PolyphaseFIR(h, modulus=4)
y = bank.apply(x)
assert np.allclose(y, np.convolve(x, h))

stream = StreamingFIR(h)
y_stream = np.concatenate([
    stream.process(x[:31]), stream.process(x[31:]), stream.flush()
])
assert np.allclose(y, y_stream)

For two-dimensional data, MultiAtlasAnalyzer retains the original shape. Categorical color coordinates are present only for exact integers within the configured palette. Continuous data is neither rounded nor clipped to create an allegedly exact color representation.

What is learned

Atlas transforms, seeded projections and operator families are fixed mechanisms. Bayesian regression learns numerical coefficients from observed transitions. Validation data determines weights over the supplied operator candidates. The final regressions use all supplied training records. Replay memory stores observed transitions separately. The system does not autonomously invent an unbounded operator grammar, and this archive contains no pretrained foundation model checkpoint.

The standalone 1-D signal interface and the frame-based world model are related components, not a claim that a general time-series forecasting or audio agent has already been trained. The release guide describes that integration boundary.

Fresh verification

These are finite controls, not counts of independent theorems. All source tests were run with warnings treated as errors. The fixture checks are static and stubbed integration tests, not execution of official games. Windows and Python 3.12 CI are configured but were not executed in this container.

Worked examples

Eight examples are exercised in the fresh evidence bundle: exact image reconstruction, adaptive refinement, continuous signals, explicit polyphase FIR, streaming, source-signal archives, learned operator identification, and the finite integer Euler bridge. Run them from the repository after installation.

python examples\continuous_signal.py
python examples\polyphase_taps.py
python examples\streaming_signal.py
python examples\operator_identification.py
python examples\euler_signal_bridge.py

Commands

python -m komorebi_pr_lasm --version
python -m komorebi_pr_lasm self-check
python -m komorebi_pr_lasm analyze signal.npy --output results\analysis.json
python -m komorebi_pr_lasm benchmark --output results\benchmark
python scripts\stress_release.py --output results\stress
python scripts\build_offline.py --output dist

analyze accepts real 1-D or 2-D .npy and comma-delimited .csv files. Archives use a separate typed Python API. save_signal_archive saves source coordinates and analysis settings, not fitted world-model or agent state.

Documentation and evidence

The current operational manual is the release guide. The migration notes explain deliberate corrections to previously accepted invalid inputs. The audit, claim boundary, and reproduction instructions distinguish fresh results from historical evidence. The historical capstone is retained unchanged. It is not a newly revalidated manuscript of every 2.3.0 feature.

The full handoff preserves the recovered signal reconstruction and Euler companions separately. The older ARC2 diagnostic package is a lineage reference, not this release's ARC solver, and its score is not transferred to this model.

License

Copyright Komorebi P. The inherited license reserves all rights. This source handoff is not yet an open-source license grant. Third-party components retain their own licenses. Nothing has been remotely published.