Disambiguating the causes of unsuccessful outcomes after intervention

Ascertain whether unsuccessful trial outcomes after applying DoVer’s orchestrator-level interventions in LLM-based multi-agent systems arise from incorrect failure attribution hypotheses or from system limitations that prevent faithful execution of the intervention, and develop diagnostics that reliably differentiate between these two causes to resolve the ambiguity in intervention evaluation.

Background

Within the DoVer pipeline, trial outcomes after targeted interventions are classified as Validated, Partially Validated, Refuted, or Inconclusive. The authors note that an Inconclusive outcome frequently occurs because agents fail to follow the intervened instruction, which makes it ambiguous whether the intervention was mislocalized (i.e., based on an incorrect failure hypothesis) or whether the system lacked the capability to carry out the intervention. This ambiguity complicates evaluation and motivates the need for more robust diagnostics to distinguish between these causes.

To mitigate the ambiguity, the paper uses an LLM-as-a-judge to check whether the intervention was faithfully executed (is_intervention_fulfilled) and combines this with success/progress measures. However, the underlying cause of failure in the Inconclusive category remains a core uncertainty, and the paper highlights it explicitly as unclear, suggesting a need for methods that can definitively attribute unsuccessful outcomes to either mislocalization or capability limitations.

References

The Inconclusive category is necessary because we frequently observe that agents fail to follow the intervened instruction, resulting in unsuccessful trials. In such cases, it is unclear whether the outcome stems from an incorrect failure hypothesis or from other limitations of the system that prevent the intervention from being carried out.

— DoVer: Intervention-Driven Auto Debugging for LLM Multi-Agent Systems  (2512.06749 - Ma et al., 7 Dec 2025) in Section 4.2, Metrics for Validating Failure Hypotheses

Thus the perfect complete-graph result validates an implementation conditional, while the stress test exposes dependency extraction and fallback detection as unresolved risks.

— BoardroomAI: Dependency-Aware Human-Steerable Multi-Agent Deliberation through Evolving Decision Graphs  (2608.13046 - Manivannan, 13 Aug 2026) in Section 4.2, subsection “Missing-dependency stress test”

deepseek-v3.1:671b returned no successful inference across the attempts we logged; \S\ref{sec:rq7} makes our client timeout the leading candidate, but the model is retired and we cannot now establish the cause.

— How Reproducible Are Evaluation Conclusions? A Self-Audit of LLM-Inferred Prompt Structure  (2609.30074 - Sarkar, 24 Sep 2026) in Section 2, paragraph “Models”; Section 6, Section 6 paragraph “Re-examining the accessibility finding”

Choosing between them from the diagnosis is future work.

— Calibration is the Bottleneck: An Action-Class Diagnostic of Multi-Turn Tool-Calling  (2609.00949 - Zhao et al., 1 Sep 2026) in Conclusion, Section Conclusion