What is recorded here?
Released an agent-produced small-transformer recipe combining frequency-aware sparse bigram/trigram features, causal induction features, document-position buckets and learned multilayer readout under a fixed single-GPU training budget. 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
One source-reported 300-second single-B200 experiment, about 348 seconds including setup/evaluation and 169 GB memory. This is not a universal efficiency gain or large-model scaling result. Muon/AdamW, attention and embedding techniques are inherited. WRS has not replayed the training or independently established generalization.
Read the original work
- Hyra nanochat autoresearch: original fixed-budget recipe and recorded result
- Hyra Results: original artifact index and source-reported agent provenance
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
Hyra Team The recorded author list is incomplete.
Hyra · Tencent · UNKNOWN
Source-reported model-recipe exploration and implementation generation; 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
Hyra Team: agent-produced fixed-budget language-model recipe with sparse n-gram and induction features
Released an agent-produced small-transformer recipe combining frequency-aware sparse bigram/trigram features, causal induction features, document-position buckets and learned multilayer readout under a fixed single-GPU training budget. Reported result; not independently verified by WRS. Ranked using a provisional grade and reference estimate for the exact claimed result.
Scientific scope
One source-reported 300-second single-B200 experiment, about 348 seconds including setup/evaluation and 169 GB memory. This is not a universal efficiency gain or large-model scaling result. Muon/AdamW, attention and embedding techniques are inherited. WRS has not replayed the training or independently established generalization.
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
Hyra Team
Hyra / Tencent / UNKNOWN: Source-reported model-recipe exploration and implementation generation; the exact underlying model identifier is unknown (source reported).