ReSpinQuant: Efficient Layer-Wise LLM Quantization via Subspace Residual Rotation Approximation explores ReSpinQuant is an efficient layer-wise LLM quantization framework that achieves state-of-the-art performance by reconciling high expressivity with minimal inference overhead through offline activation rotation fusion.. Commercial viability score: 7/10 in LLM Quantization.
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Canonical route: /paper/respinquant-efficient-layer-wise-llm-quantization-via-subspace-residual-rotation-approximation
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Agent Handoff
Canonical ID respinquant-efficient-layer-wise-llm-quantization-via-subspace-residual-rotation-approximation | Route /paper/respinquant-efficient-layer-wise-llm-quantization-via-subspace-residual-rotation-approximation
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/respinquant-efficient-layer-wise-llm-quantization-via-subspace-residual-rotation-approximationMCP example
{
"tool": "get_paper",
"arguments": {
"arxiv_id": "2604.11080"
}
}source_context
{
"surface": "paper",
"mode": "paper",
"query": "ReSpinQuant: Efficient Layer-Wise LLM Quantization via Subspace Residual Rotation Approximation",
"normalized_query": "2604.11080",
"route": "/paper/respinquant-efficient-layer-wise-llm-quantization-via-subspace-residual-rotation-approximation",
"paper_ref": "respinquant-efficient-layer-wise-llm-quantization-via-subspace-residual-rotation-approximation",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Constellation, claims, and market context stay visible on the paper proof page even when commercialization rails are held back for incomplete proof receipts.
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