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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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CIGAsk trains a multi-turn GRPO policy with an outcome reward, per-clarification Counterfactual Information Gain computed by a frozen reference model, and a terminal Asymmetric Ambiguity Bonus keyed to the gold ambiguity label. · CiteArk

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

Not assessedNo independent reproduction scheduledMethodcigask-method-001

CIGAsk trains a multi-turn GRPO policy with an outcome reward, per-clarification Counterfactual Information Gain computed by a frozen reference model, and a terminal Asymmetric Ambiguity Bonus keyed to the gold ambiguity label.

Source: paper:PDF pp. 3-5, Section 3, especially Equations 2-8; PDF p. 11, Table 7

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Research Plan identity

Claim version: sha256:4bf00ec80ce4b7bdc3078d428b72a1680c9197589bc0b003769c18c8105a681d
Planned experiments: 6
Evidence objects: 0

This is method context rather than a separately reported empirical result. Its equations and controls are carried into every training objective; correctness is assessed through the bound empirical packages rather than by inventing a method-only scalar.

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