AXELRAM: Quantize Once, Never Dequantize explores A novel SRAM architecture for LLM inference that drastically reduces computation by performing attention scores directly on quantized KV cache indices, with a gradient-free method to ensure stability.. Commercial viability score: 7/10 in LLM Inference Optimization.
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This route is the stable paper-level surface for citations, viability, references, and downstream handoffs. Use it as the proof layer behind Signal Canvas, workspace creation, and launch-pack generation.
Freshness
Canonical route: /paper/axelram-quantize-once-never-dequantize
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Proof Quality
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Agent Handoff
Canonical ID axelram-quantize-once-never-dequantize | Route /paper/axelram-quantize-once-never-dequantize
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/axelram-quantize-once-never-dequantizeMCP example
{
"tool": "get_paper",
"arguments": {
"arxiv_id": "2604.02638"
}
}source_context
{
"surface": "paper",
"mode": "paper",
"query": "AXELRAM: Quantize Once, Never Dequantize",
"normalized_query": "2604.02638",
"route": "/paper/axelram-quantize-once-never-dequantize",
"paper_ref": "axelram-quantize-once-never-dequantize",
"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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