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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 Asymmetric Ambiguity Bonus requires a per-instance gold ambiguity label during training, which may be unavailable in unannotated corpora. · CiteArk

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

Not assessedNo independent reproduction scheduledLimitationlimitation-ambiguity-labels-001

The Asymmetric Ambiguity Bonus requires a per-instance gold ambiguity label during training, which may be unavailable in unannotated corpora.

Source: paper:PDF p. 8, Conclusion and 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:c8d0fa295d22df093c711ab7e17d2e4a500c89010cbbf3fce616225bacb0099a
Planned experiments: 0
Evidence objects: 0

This is a direct method/input requirement from the equations: every planned reconstruction requires the benchmark ambiguity label during training; no separate result is claimed.

Machine-readable Claim

Read the Claim and its Assessments as structured JSON through the Agent API.

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