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AI is dramatically lowering the cost of reading papers, writing code, and running experiments. But as research output explodes, telling which results can be trusted keeps getting harder. CiteArk connects the claims in scientific papers with their experimental conditions, execution process, result evidence, and verification records, so researchers and agents can verify claims, reuse experimental results, and build follow-up research on them instead of repeating work. We hope these trustworthy research records will grow into a scientific foundation that researchers, labs, and agents can all build on together.

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The measured speedups belong to uniform FIXED-K, which preserves uniformly shaped expert GEMMs; per-token dynamic k is harder to serve efficiently, and the paper reports no matched dynamic-rule throughput results. · CiteArk

You Only Need 2/3 of the Chosen Experts: An Empirical Study of Dynamic Expert Pruning in Fine-Grained MoE LLMs

Not assessedNo independent reproduction scheduledLimitationclaim-serving-comparison-limitation

The measured speedups belong to uniform FIXED-K, which preserves uniformly shaped expert GEMMs; per-token dynamic k is harder to serve efficiently, and the paper reports no matched dynamic-rule throughput results.

Source: fixed-paper:PDF pp. 7, 12, and 24, Sections 4.3 and 7.3 and Appendix G

Reported and observed measurements

No structured measurement is attached to this Claim.

Assessments (0)

No immutable Assessment has been published for this Claim yet.

Runs (0)

No execution has been linked to this Claim yet.

Research Plan identity

Claim version: sha256:89ceb4affa707906a71614fb97bab7b4824750c895181e9edca8dd466459435e
Planned experiments: 0
Evidence objects: 0

This record is source context, a protocol definition, or an interpretive limitation rather than an independently testable empirical result. It remains attached to the relevant experiment objectives and comparison constraints but is not assigned a separate reproduction verdict.

Machine-readable Claim

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