aiqbench analysis
The paper proposes "implicit failures," local errors that multi-agent systems correct or absorb without affecting the final result, as a process-level signal beyond task success. It reports these occur in 54% of successful and 75% of failed traces across 1,828 runs, and it adds an RL variant, IF-GRPO, with modest accuracy gains plus trade-offs between performance and IF cost. For readers building agent systems, the useful takeaway is that final-outcome metrics can hide how errors propagate, though the evaluation is benchmark-scoped and the gains are described as "some."
MASIF: Implicit Failures for Process Observability in Multi-Agent Systems
arXiv:2610.25852v1 Announce Type: new Abstract: Multi-agent systems (MAS) can complete a task despite local execution errors that are corrected, bypassed, or carried forward without individually determining its outcome. We call these implicit failures (IFs) and study them as signals for process observability beyond final task outcome. Across 1,828 traces from nine MAS-benchmark configurations, IFs occur in 54.3% of successful and 75.2% of failed traces. We develop taxonomies of error types and subsequent absorption mechanisms, supported by human and cross-model annotation audits. Counterfactual replay further probes how repairing an IF changes the final outcome. To examine the practical value of IFs, we evaluate IF-based best-of-N trace selection against existing process reward models, and introduce a novel RL algorithm, IF-GRPO, that uses IF costs to refine preferences among successful trajectories in GRPO. Experiments show that IF-GRPO exhibits some gains in accuracy and process metrics. Importantly, it exposes trade-offs between task performance, IF count and cost, and token-normalized IF density. Overall, our results show that IFs provide complementary process observability into how MAS reach their outcomes, including error recovery, propagation, and IF-based process differences that final task success alone does not expose.
Abstract:Multi-agent systems (MAS) can complete a task despite local execution errors that are corrected, bypassed, or carried forward without individually determining its outcome. We call these implicit failures (IFs) and study them as signals for process observability beyond final task outcome. Across 1,828 traces from nine MAS-benchmark configurations, IFs occur in 54.3% of successful and 75.2% of failed traces. We develop taxonomies of error types and subsequent absorption mechanisms, supported by human and cross-model annotation audits. Counterfactual replay further probes how repairing an IF changes the final outcome. To examine the practical value of IFs, we evaluate IF-based best-of-N trace selection against existing process reward models, and introduce a novel RL algorithm, IF-GRPO, that uses IF costs to refine preferences among successful trajectories in GRPO. Experiments show that IF-GRPO exhibits some gains in accuracy and process metrics. Importantly, it exposes trade-offs between task performance, IF count and cost, and token-normalized IF density. Overall, our results show that IFs provide complementary process observability into how MAS reach their outcomes, including error recovery, propagation, and IF-based process differences that final task success alone does not expose.
Artificial Intelligence (cs.AI)
arXiv:2610.25852 [cs.AI]
(or arXiv:2610.25852v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.25852 Focus to learn more arXiv-issued DOI via DataCite
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