PSSA: A non-transformer language model written from scratch in Rust
PSSA: a plastic state-space architecture PSSA is a small language model that is not a transformer. It reads text one token at a time through a recurrent state-space layer, keeps a bank of episodic memories it can look things up in, and rewrites part of its own weights while it runs. It is written in Rust from scratch, with no PyTorch, no TensorFlow, and no ML framework of any kind underneath it.
- ▪PSSA: a plastic state-space architecture PSSA is a small language model that is not a transformer.
- ▪It reads text one token at a time through a recurrent state-space layer, keeps a bank of episodic memories it can look things up in, and rewrites part of its own weights while it runs.
- ▪It is written in Rust from scratch, with no PyTorch, no TensorFlow, and no ML framework of any kind underneath it.
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Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/Sparticle62ops/pssa |
| Publication time | Wed, 30 Sep 2026 03:19:54 +0000 |
| Retrieval time | 2026-09-30T04:02:33.766Z |
| Last seen | 2026-09-30T04:02:33.766Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | LxO_Zks5pkAR · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
PSSA: a plastic state-space architecture PSSA is a small language model that is not a transformer. It reads text one token at a time through a recurrent state-space layer, keeps a bank of episodic memories it can look things up in, and rewrites part of its own weights while it runs. It is written in Rust from scratch, with no PyTorch, no TensorFlow, and no ML framework of any kind underneath it. At matched parameters and on the same corpus, it learns faster than a transformer and generates text about twelve times quicker on the same CPU. How it differs from a transformer A transformer scores every pair of tokens in the context, so its cost per step grows with the square of the sequence length and the whole context is re-read at every step.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.