---
license: mit
tags:
- loss-functions
- research-record
- geometric-deep-learning
---

# The Loss Manifest — companion repository

Companion to the article **"The Loss Manifest: A Field History of Objective
Functions, and What a Machine Can Actually Be Asked to Compute"**
(`article_loss_manifest.md` in this repo; also published on the author's blog).

155 objective functions, regularizers, gauges, and prohibitions from a
multi-year geometric deep learning program — each rated 1-10 under a fixed,
recomputable rubric, each carrying its mathematics, its implementation home,
and its verdict with receipts. Failures ship alongside successes: the
retractions and prohibitions are first-class rows, because each is the
evidence for a standing law.

## Contents
- `article_loss_manifest.md` — the article (canonical copy).
- `LOSS_MANIFEST.md` — the full rated registry, human-readable.
- `loss_manifest.json` — the machine-readable sidecar; every rating is
  recomputable from its six sub-scores + the lookup table + the nine rules.
- `code/loss_forms.py` — the composable loss library (4 differencing
  primitives, accumulation formats A0-A8, the campaign candidates, the
  force-gated forbidden controls, self-smoke).
- `code/compartment_smoke.py` — the 22-test formula-smoke battery, including
  the two calibrated pre-spend gates (conditioning + collinearity).
- `code/fac_bed.py`, `code/deviant_bed.py`, `code/geobasin_bed.py` — the
  campaign beds (cosh-Bregman/FAC matrix; the deviant roster matrix; the
  recovered CE-replacement geometric arm under full controls).
- `code/ar_differentiation_bed.py`, `code/geolip_vitals.py`,
  `code/loss_view.py` — the certified byte bed, the shared read-only gauge
  harness, and the registry viewer/linter.
- `runs/` — the raw run ledgers (JSONL) behind every trained verdict in the
  article's era-six tables.

## Quick start
```bash
pip install torch  # cu-enabled build recommended
python code/loss_forms.py          # library self-smoke
python code/compartment_smoke.py   # the 22-test battery + gates
python code/loss_view.py card --json loss_manifest.json
```
Beds default their data root to `$GEOLIP_DATA` (or `./data`) and download
wikitext bytes on first use.

## The program's lines (evidence trails)
[Qwen3.5 adapter line](https://huggingface.co/AbstractPhil/geolip-aleph-qwen-3.5-0.8b-instruct) ·
[Qwen2.5 line](https://huggingface.co/AbstractPhil/geolip-aleph-qwen) ·
[diffusion line](https://huggingface.co/AbstractPhil/aleph-diffusion-adapters) ·
[differentiation line](https://huggingface.co/AbstractPhil/geolip-aleph-differentiation) ·
[amoe-lora](https://github.com/AbstractEyes/amoe-lora) ·
[classification line](https://github.com/AbstractEyes/geolip-aleph-classification) ·
[geolip-svae](https://github.com/AbstractEyes/geolip-svae) ·
[geofractal](https://github.com/AbstractEyes/geofractal)

Field reports: [ft1](https://huggingface.co/blog/AbstractPhil/aleph-autoregressive-differentiation-ft1) ·
[ft2](https://huggingface.co/AbstractPhil/geolip-aleph-qwen/blob/main/article_ft2.md) ·
[ft3](https://huggingface.co/AbstractPhil/geolip-aleph-qwen-3.5-0.8b-instruct/blob/main/article_ft3.md)

Internal citations in the registry (file:line anchors into the program's
research record) are preserved verbatim for provenance integrity.