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        <datestamp>2026-09-29T09:56:01Z</datestamp>
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        <dc:title>Attribution Is Scored on a Finished Trace: What Failure-Attribution Benchmarks Measure, What Cascade Containment Would Need, and Why Neither Result Bounds the Other</dc:title>
        <dc:creator>Pranay Mahendrakar</dc:creator>
        <dc:subject>multi-agent systems</dc:subject>
        <dc:subject>failure attribution</dc:subject>
        <dc:subject>error propagation</dc:subject>
        <dc:subject>cascade containment</dc:subject>
        <dc:subject>root cause analysis</dc:subject>
        <dc:subject>benchmark construction</dc:subject>
        <dc:subject>fault localization</dc:subject>
        <dc:description>(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0.
A family of recent proposals aims to stop errors from spreading through LLM-based multi-agent systems: genealogy-graph governance over message dependencies, cross-channel causal monitoring, propagation-aware remediation of contaminated state, infection-aware safeguarding, and corrector placement on the communication graph. Most of these mechanisms have to decide where a spreading error came from before they can decide what to cut. A separate and largely disjoint literature measures exactly that: automated failure attribution, the task of naming the agent and the step responsible for a failed run. Its reported accuracies at step granularity are low, and the obvious inference is that containment is built on a primitive that does not work. This paper argues that the inference is not available, in either direction, and sets out why. The attribution benchmarks score a method on a completed trace whose failure is already known to have occurred and whose ground truth is a single decisive step; a containment mechanism must act on a prefix, without knowing that a failure will occur, and its output feeds an intervention rather than a developer. Recent benchmark work reports that how much of the trace a method is shown, and whether the ground truth is allowed to be multi-valued, both move the measured accuracy substantially, which means the pessimistic numbers are not a stable property of the task. The paper also separates the containment proposals by what they localise and notes one that localises nothing at all; and it observes that the most widely used taxonomy of why these systems fail defines its first category as failures that occur during execution but reflect pre-execution design choices, for which the step a method points at is a symptom by construction. It closes with the measurements that would make the question decidable, and with the older fault-localisation literature that already ran this experiment once.
The literature search, drafting and citation verification for this paper were carried out with AI assistance under the author&apos;s direction. Every citation was machine-verified against the arXiv API and Crossref before inclusion, and every quantitative claim was read back against the cited source&apos;s own text, either its abstract or, where a claim is drawn from a paper&apos;s body, the located passage. The author is responsible for the final text and for all claims made in it.</dc:description>
        <dc:publisher>Life of Research</dc:publisher>
        <dc:date>2026-09-10</dc:date>
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        <dc:identifier>https://lifeofresearch.com/articles/63</dc:identifier>
        <dc:identifier>https://doi.org/10.5281/zenodo.23022351</dc:identifier>
        <dc:language>eng</dc:language>
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