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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 paper's effect-history model separates external events from the runtime's observations, attributes attempts, compensations, and effects to workflows and branches, and evaluates effect safety against workflow resolution at the higher level. · CiteArk

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

Not assessedNo independent reproduction scheduledMethodclaim-effect-history-model

The paper's effect-history model separates external events from the runtime's observations, attributes attempts, compensations, and effects to workflows and branches, and evaluates effect safety against workflow resolution at the higher level.

Source: paper:PDF pp. 2-3, Section 2; Markdown blocks p2-b19 and p3-b21-p3-b26

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: sha256:94494d3e0eac9e1d2c3030e8c1af92e84e22bb9cd11ff669893f6508c6458b56
Planned experiments: 0
Evidence objects: 0

This is non-empirical method context defining the model and the distinction between world events and runtime observations; it supplies interpretation for later claims but has no independently reported empirical observable.

Machine-readable Claim

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