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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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Pretraining does not monotonically improve distinguishing truth from plausibility: performance on surprising truths and common misconceptions mode-hops in both OLMo3 and Apertus. · CiteArk

Generalization Dynamics of LM Pre-training

Not assessedPlan blockedFindingtruth-mode-hopping

Pretraining does not monotonically improve distinguishing truth from plausibility: performance on surprising truths and common misconceptions mode-hops in both OLMo3 and Apertus.

Source: paper:§3.3, Figure 5, p.4; Appendix G, Figure 21, p.17

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Assessments (0)

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Runs (0)

No execution has been linked to this Claim yet.

Research Plan identity

Claim version: sha256:8f54ee3e590b190269477230736e51e84253ac5636239a5d1ae96519a86d3c5e
Planned experiments: 1
Evidence objects: 0

The proposed observation comparison is retained, but a deterministic decision rule is unresolved.

Machine-readable Claim

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