Proof pending. This topic has not reached the minimum paper threshold yet.
State-space models (SSMs) offer efficient sequence modeling but lag behind Transformers on benchmarks that require in-context retrieval. Prior work links this gap to a small set of attention heads, te...
Diffusion Transformers (DiTs) incur prohibitive computational costs due to the quadratic scaling of self-attention. Existing pruning methods fail to simultaneously satisfy differentiability, efficienc...
Linear attention methods offer Transformers $O(N)$ complexity but typically underperform standard softmax attention. We identify two fundamental limitations affecting these approaches: the restriction...
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Canonical route: /topics
Agent Handoff
Canonical ID efficient-transformers | Route /topic/efficient-transformers
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/topic/efficient-transformersMCP example
{
"tool": "search_papers",
"arguments": {
"query": "Efficient Transformers",
"cluster": "Efficient Transformers"
}
}source_context
{
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"mode": "topic",
"query": "Efficient Transformers",
"normalized_query": "efficient-transformers",
"route": "/topic/efficient-transformers",
"paper_ref": null,
"topic_slug": "efficient-transformers",
"benchmark_ref": null,
"dataset_ref": null
}Use This Via API or MCP
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