| --- |
| license: mit |
| library_name: pytorch |
| tags: |
| - artificial-life |
| - evolution |
| - thegrid |
| - genome |
| - mutation-operator |
| datasets: |
| - flaukowski/thegrid-colony-one-transitions |
| --- |
| |
| # gridlm: a mutation screen and generator learned from a thegrid fossil record |
|
|
| [thegrid](https://github.com/EckmanTechLLC/thegrid) (Matt Eckman, MIT) is an artificial-life lab. |
| Its organisms are small programs on an evolvable instruction set, and they compete for real CPU, |
| memory and heat on the host. Every birth goes into a fossil record. |
|
|
| thegrid's own findings (`tools/analyse/LESSONS.md`) include this one: **model-authored mutation |
| founded nothing.** 4,554 genomes written by a general LLM from a prompt produced 4,554 births and |
| no lineage. Over the same period, blind segment transfer compounded at 1.3x. |
|
|
| gridlm asks whether a model trained on **what actually reproduced** does better than one prompted |
| with the instruction set. The idea is borrowed from DNA language models such as Hugging Face's |
| Carbon-X: give each unit of the sequence its own token and learn from outcomes. Here the unit is |
| an opcode, and the outcomes come from the fossil record. |
|
|
| Two models, about 0.8M parameters each: |
|
|
| - **screen** (`screen.pt`): an encoder over `[CLS] parent [SEP] child`. It outputs P(the child |
| genome is born at least twice in its epoch), meaning it copied itself and founded something. |
| - **generator** (`generator.pt`): a causal LM over `[BOS] parent [SEP] child [EOS]`, trained only |
| on children that reproduced. It writes a child for a given parent. |
|
|
| There is also **`gridlm_operator.py`**, a drop-in operator for thegrid's `odin` mutator. It reads |
| the same `request.json` / `proposal.json` queue that thegrid's `tools/operator` answers. For each |
| request it samples 8 children, the screen picks the best, and it proposes that one. It runs on |
| CPU in milliseconds. The colony validates every proposal with its own parser, so a proposal it |
| cannot use costs one birth, never the colony. |
| |
| ## Data |
| |
| The fossil record of **colony-one**, the thegrid colony that kannaka-grid runs on its own host, |
| read 2026-10-27 (dataset: `flaukowski/thegrid-colony-one-transitions`): |
| |
| - 1,573,008 rows: one per (epoch, child genome), with its earliest parent and exact self-copies excluded. |
| - Label: the child was born at least twice in its epoch. The positive rate is 24.8%. |
| - 614 epochs. **This is an older thegrid version and a starvation economy** (613 of 614 epochs ended |
| in extinction). It is not the eviction or class worlds Matt runs today. |
| |
| Deliberately excluded from the inputs: |
| - `mutation_type`: thegrid records a mutation origin only when its child was born, so its presence |
| leaks the label. |
| - The parent's birth count for the epoch: it includes births after the child appeared. |
| |
| The inputs are the two instruction sequences and nothing else. |
| |
| ## Results (held out in time) |
| |
| Split by epoch: train epochs 1–565 (1.27M rows), validation 566–593, test 594–614 (152k rows). |
| The "novel" columns use only test children whose genome never appears in training (145k). |
| |
| | model | AUROC | avg precision | precision in top 10% | AUROC, novel | top 10%, novel | |
| |---|---|---|---|---|---| |
| | base rate | | 0.248 | 0.248 | | 0.240 | |
| | rule: has alloc+copy+fork | 0.526 | 0.267 | 0.283 | 0.528 | 0.265 | |
| | logistic regression, 15 hand features | 0.609 | 0.327 | 0.384 | 0.605 | 0.362 | |
| | **gridlm screen** | **0.667** | **0.385** | **0.452** | **0.659** | **0.418** | |
| | gridlm generator, log p(child \| parent) | 0.582 | 0.296 | 0.321 | 0.586 | 0.320 | |
|
|
| **What this says:** the outcome of a single mutation is mostly noise, which the weak baselines |
| show. But the screen ranks children well above chance and well above hand features. The 10% it |
| likes best reproduce at 1.8x the base rate (1.7x on genomes it has never seen). |
|
|
| **What it does not say:** whether the generator's children found lineages in a live colony. That |
| needs a colony. |
|
|
| The generator's 1,000 samples on held-out parents: |
| - 100% are valid length; |
| - 83.2% contain alloc+copy+fork; |
| - 2.3% are identical to the parent. |
|
|
| The screen rates them higher than the real test mutants: mean P 0.525 against 0.469. That is |
| one model judging another and is no evidence of fitness. |
|
|
| The samples look like what LESSONS says evolution does there. They include tandem duplications |
| of an earning motif and duplicated `fork copy alloc` blocks. Some are bad, for example |
| substitutions that remove `fork`. |
|
|
| ## Limits |
|
|
| - **One colony, one ISA version, one economy.** Instruction names colony-one never used are |
| unknown tokens, and the model's taste is a starvation world's taste. It should be retrained on |
| the target fleet's own record. `extract.py` and `train.py` do that end to end from any thegrid |
| `history.sqlite3`. |
| - **No replicate colonies.** thegrid's own rule is "never run one of anything you intend to |
| learn from". The time split is held out, but it is one colony. The real test is several |
| replicate colonies with this operator against the same colonies on blind variation, compared on |
| lineages founded per proposal. |
| - **LESSONS 11–12 say the economy decides where a world ends up**, not the mutation operator. |
| A better mutator may only reach stasis faster. That is worth measuring, not assuming away. |
|
|
| ## Use |
|
|
| ```bash |
| pip install torch numpy |
| # one proposal |
| python gridlm_operator.py --model-dir . --once-genome "harvest harvest alloc copy ifnotdone jmpb fork scan move" |
| # serve one or more colonies' odin queues |
| python gridlm_operator.py --model-dir . --queue /path/to/colony/operator |
| # retrain on your own fossil record |
| python extract.py /path/to/history.sqlite3 transitions.jsonl.gz |
| python train.py transitions.jsonl.gz out --device cuda |
| ``` |
|
|
| Trained on one qBraid RTX 4090 in about 7 minutes (12.8 credits, about $0.13). |
|
|
| ## Credits |
|
|
| Built by Kannaka (an AI) with Nick Flach, on top of Matt Eckman's thegrid. thegrid's instruction |
| set, fossil record format and operator protocol are his. The model, code and card are MIT. |
|
|
