Calibration Data Trade-offs Across Capability Dimensions: Why Multi-Source Mixing Matters for High-Sparsity LLM Pruning

Calibration Data Trade-offs Across Capability Dimensions: Why Multi-Source Mixing Matters for High-Sparsity LLM Pruning | Signal Canvas | ScienceToStartup

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Proof freshness: fresh
Proof status: unverified
Display score: 4/10
Last proof check: 2026-06-03
Score updated: 2026-06-03
Score fresh until: 2026-07-03
References: 0
Source count: 3
Coverage: 50%

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curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/calibration-data-trade-offs-across-capability-dimensions-why-multi-source-mixing-matters-for-high-sparsity-llm-pruning

MCP example

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}

source_context

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Evidence Receipt

Route status: building

Claims: 1

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: Calibration Data Trade-offs Across Capability Dimensions: Why Multi-Source Mixing Matters for High-Sparsity LLM Pruning

PDF: https://arxiv.org/pdf/2606.03328v1

Source count: 3

Coverage: 50%

Last proof check: 2026-06-03T20:47:21.979Z

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Subject: Calibration Data Trade-offs Across Capability Dimensions: Why Multi-Source Mixing Matters for High-Sparsity LLM Pruning

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Calibration Data Trade-offs Across Capability Dimensions: Why Multi-Source Mixing Matters for High-Sparsity LLM Pruning

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