README.md

metadata

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.

Training data, paper artifacts, and fixed proposals are in 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:

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.