CERPT Korean Stage 1
CERPT (Causal Evidence Recursive Program Transformer) is a research
architecture that keeps typed intermediate state in a workspace and updates
that state through explicit operators and evidence checks.
This checkpoint is a small Korean Stage 1 experiment. It is trained from
scratch on Korean math, logic, and multi-hop reasoning examples converted to
four cycles:
EXTRACT → SIMULATE/BIND → CHECK → WRITE_RESULT
Status
This is not a general-purpose LLM. It is a research checkpoint for testing
whether a small CERPT model can learn Korean task solving and structured
reasoning traces. It should not be used as an open-domain assistant or for
high-stakes decisions.
Training data
The local conversion and split details are documented in
docs/KOREAN_STAGE1_DATA.md. Dataset providers and licenses must be credited
when redistributing derived artifacts.
Architecture
- PyTorch and Hugging Face compatible
CERPTForConditionalGeneration
- 4 recursive workspace cycles
- 6 typed operators:
EXTRACT, BIND, COMPARE, SIMULATE, CHECK,
WRITE_RESULT
- Korean tokenizer trained from the Stage 1 text
Limitations
- No general web or code pretraining
- No reliable image/video understanding training in this checkpoint
- Structured output quality and Korean open-domain ability are not established
- Reported loss is a training diagnostic, not a general capability score
