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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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For the displayed incorrect response, local A_t^(0) penalizes tokens only weakly related to the actual reasoning error, whereas A_t^(0.99) concentrates stronger negative credit on the erroneous formula derivation without excessively penalizing the final end-of-sequence token. · CiteArk

Beyond Token-Local Imitation: Reward-Compatible Temporal Credit Assignment for On-Policy Distillation

Not assessedPlan blockedFindingclaim-token-credit-incorrect-example

For the displayed incorrect response, local A_t^(0) penalizes tokens only weakly related to the actual reasoning error, whereas A_t^(0.99) concentrates stronger negative credit on the erroneous formula derivation without excessively penalizing the final end-of-sequence token.

Source: paper:PDF pp. 8–9, Section 4.4 and Figure 5(b); PDF pp. 17 and 20, Appendix F.2 and Figure 7

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Research Plan identity

Claim version: sha256:f1052de0b10de311501f5928daadcf9ff8dae4fb651064ddac5d50ea3bdcf7cb
Planned experiments: 1
Evidence objects: 0

The exact illustrated response is retained in the ablation objective as qualitative source context; no scalar target is invented.

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