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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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CoAgent is classified as partially covering externally mediated A7: reordering prevents some conflicts, registered inverses enable repair, and irreversible effects are gated, but this is an achievability condition rather than an unconditional guarantee. · CiteArk

When Tool Calls Succeed but Workflows Fail: Anomalies at the Agent–Tool Boundary

Not assessedPlan blockedFindingclaim-runtime-coagent

CoAgent is classified as partially covering externally mediated A7: reordering prevents some conflicts, registered inverses enable repair, and irreversible effects are gated, but this is an achievability condition rather than an unconditional guarantee.

Source: paper:PDF p. 7, Table 3 and accompanying paragraph; Markdown blocks p7-b65-p7-b66

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:a897b966d4abdd3e2320e0f92609a29aa2e9856667c6ac7aadb4ac3072115caf
Planned experiments: 0
Evidence objects: 0

The partial A7 classification is an assumption-sensitive qualitative comparison of reordering, inverses, and irreversible-effect gating. It requires the cited CoAgent source and an explicit evidence rubric; a numeric proxy would not answer the claim.

Machine-readable Claim

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