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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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Every evaluated benchmark is in English, so multilingual clarification behavior is untested. · CiteArk

When and How Should an Agent Clarify? CIGAsk: Teaching LLMs to Clarify via Counterfactual Information Gain

Not assessedNo independent reproduction scheduledLimitationlimitation-language-001

Every evaluated benchmark is in English, so multilingual clarification behavior is untested.

Source: paper:PDF p. 8, Limitations

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: sha256:4e5bb4c97c62dbd344832281fd69763dab84f7642ad6ce84739a33e9d67b9584
Planned experiments: 0
Evidence objects: 0

The English-only scope is descriptive of the evaluated datasets; multilingual behavior is unreported and is not added as a new paper-bound experiment.

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