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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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Higher teacher entropy corresponds to strictly lower gold-token probability, attenuates the gold-token gradient relative to NTP (reaching ~0.5x NTP for Q5 facts), and produces larger downstream factual recall deficits. · CiteArk

Knowledge Distillation During Mid-Training Favors Reasoning over Factual Recall

Not assessedPlan blockedFindingclaim_fig5_gradient_attenuation_factual_supervision

Higher teacher entropy corresponds to strictly lower gold-token probability, attenuates the gold-token gradient relative to NTP (reaching ~0.5x NTP for Q5 facts), and produces larger downstream factual recall deficits.

Source: source_paper:Figure 5, Page 7

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:b906d92c05d8887211920b2f4e755d71d91814a7c25116c59aeb1ed602f51f6a
Planned experiments: 0
Evidence objects: 0

Plot-only empirical finding without extractable discrete tabular coordinates; requires full intermediate training checkpoint trajectory or continuous sweep data not provided in the paper or repository.

Machine-readable Claim

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