---
license: mit
language: en
tags:
  - scientific-discovery
  - research-ideas
  - pytorch
---
# Alien coherence and availability models

These are the two core inference models for *The Alien Space of Science: Sampling Coherent but Cognitively Unavailable Research Directions*. Run them with the [public code](https://github.com/alejandrohdez00/alien-space-of-science).

Training data, paper artifacts, and fixed proposals are in [alien-data](https://huggingface.co/datasets/alejandrohdez/alien-data).

## Contents

- `coherence_model/`: autoregressive transformer, 274 tokens, hidden size 768, six layers, twelve attention heads, maximum sequence length 32. Includes `model.pt` and its token mapping.
- `availability_model/`: dual encoder, hidden size 512, six transformer layers, eight heads, FFN size 3072. Includes `model.pt`, its token mapping, the 39,791 × 512 author embedding bank, ID-to-row mapping, and training configuration. Attention key biases are constrained to zero.

The inference files total approximately 358 MB.

## Use

Download the directories into `models/` in the code repository, install its requirements, and run:

```bash
hf download alejandrohdez/alien-models --include "*_model/*" --local-dir models
python alien.py sample --top-k 20 --output-dir results/demo
```

CPU and CUDA are supported. Sampling does not use an LLM API. Reconstruction is a separate optional command using the user's chosen LiteLLM provider.

Coherence is negative mean atom-token NLL over all set permutations, excluding EOS. Availability is the median of the top ten author cosine similarities. Fusion is `(1-beta)*z(coherence) + beta*z(-availability)` over the candidate population. Each checkpoint has its own token mapping; preserve the files together.

## Training and limitations

Coherence uses 136,827 training and 15,203 validation records from 15,203 papers. Availability uses 43,077,647 usable training and 4,786,404 validation subset/complement records; the training bank contains 39,791 authors from a corpus with 46,329 author atom profiles. Availability validation is by example, not by held-out author.

The models recombine the fixed 273-atom LLM-research catalogue. Availability measures corpus-relative proximity to author repertoires; it is not proof of originality, feasibility, or future scientific value. Decoder outputs are proposals to assess. CPU and GPU calculations can differ numerically.

Source: the authors' paper corpus and model training runs. File sizes and checksums are in `manifest.json`. The model weights and repository code are released under the MIT license; see `LICENSE`.