Proof pending. This topic has not reached the minimum paper threshold yet.
Topic-specific paper and score movement from the daily diff ledger.
Transformers owe much of their empirical success in natural language processing to the self-attention blocks. Recent perspectives interpret attention blocks as interacting particle systems, whose mean...
Standard negative log-likelihood (NLL) for Supervised Fine-Tuning (SFT) applies uniform token-level weighting. This rigidity creates a two-fold failure mode: (i) overemphasizing low-probability target...
Converting pretrained attention modules such as grouped-query attention (GQA) into multi-head latent attention (MLA) can improve expressivity without increasing KV-cache cost, making it attractive for...
We study efficient reasoning under tight compute. We ask how to make structured, correct decisions without increasing test time cost. We add two training only components to small and medium Transforme...
Freshness
Canonical route: /topics
Agent Handoff
Canonical ID nlp-optimization | Route /topic/nlp-optimization
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/topic/nlp-optimizationMCP example
{
"tool": "search_papers",
"arguments": {
"query": "NLP Optimization",
"cluster": "NLP Optimization"
}
}source_context
{
"surface": "topic",
"mode": "topic",
"query": "NLP Optimization",
"normalized_query": "nlp-optimization",
"route": "/topic/nlp-optimization",
"paper_ref": null,
"topic_slug": "nlp-optimization",
"benchmark_ref": null,
"dataset_ref": null
}Use This Via API or MCP
Topic pages bundle paper counts, viability trends, author concentration, and top questions into one canonical surface your agents can reference before they open Signal Canvas or create a workspace.