Rethinking Model Efficiency: Multi-Agent Inference with Large Models explores A multi-agent inference framework that leverages large models with short responses by transferring reasoning tokens from smaller models to improve efficiency and performance.. 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.
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Canonical route: /paper/rethinking-model-efficiency-multi-agent-inference-with-large-models
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Agent Handoff
Canonical ID rethinking-model-efficiency-multi-agent-inference-with-large-models | Route /paper/rethinking-model-efficiency-multi-agent-inference-with-large-models
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/rethinking-model-efficiency-multi-agent-inference-with-large-modelsMCP example
{
"tool": "get_paper",
"arguments": {
"arxiv_id": "2604.04929"
}
}source_context
{
"surface": "paper",
"mode": "paper",
"query": "Rethinking Model Efficiency: Multi-Agent Inference with Large Models",
"normalized_query": "2604.04929",
"route": "/paper/rethinking-model-efficiency-multi-agent-inference-with-large-models",
"paper_ref": "rethinking-model-efficiency-multi-agent-inference-with-large-models",
"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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Dimensions overall score 7.0
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