What is recorded here?
Adapted gated bigram/trigram lookup features into multiple attention-value layers with decorrelated layer-specific hashes, combining them with training and architecture choices for fixed-budget small-language-model training. Reported result; not independently verified by WRS. Ranked using a provisional grade and reference estimate for the exact claimed result.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Ten-seed five-minute single-B200 tests, not a large-model scaling guarantee or proof of independent rediscovery. Hash embeddings, Muon and baseline nanochat components are inherited. Search initially used H100; hardware transfer and repeated benchmark feedback constrain comparisons. No training or no-leakage audit was replayed.
Read the original work
- Recursive original nanochat_autoresearch artifact documentation
- First Steps Toward Automated AI Research
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
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People and systems
Recursive The recorded author list is incomplete.
Automated AI research system · Recursive · UNKNOWN
Source-reported experimental proposal, implementation generation and evaluator-guided refinement; the exact underlying model identifier is unknown. Source-reported attribution; provider attestation is unavailable.
Research locations and affiliations
Location not yet established
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English catalogue narrative
Recursive: agent-produced layer-decorrelated n-gram value-path training recipes
Adapted gated bigram/trigram lookup features into multiple attention-value layers with decorrelated layer-specific hashes, combining them with training and architecture choices for fixed-budget small-language-model training. Reported result; not independently verified by WRS. Ranked using a provisional grade and reference estimate for the exact claimed result.
Scientific scope
Ten-seed five-minute single-B200 tests, not a large-model scaling guarantee or proof of independent rediscovery. Hash embeddings, Muon and baseline nanochat components are inherited. Search initially used H100; hardware transfer and repeated benchmark feedback constrain comparisons. No training or no-leakage audit was replayed.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Recursive
Automated AI research system / Recursive / UNKNOWN: Source-reported experimental proposal, implementation generation and evaluator-guided refinement; the exact underlying model identifier is unknown (source reported).