arXiv:2606.09878v1 Announce Type: new Abstract: Standard benchmarks report aggregate accuracy, but practitioners need to know which specific capabilities a model lacks. We introduce FailureScope, a behavioral-diagnosis method that clusters evaluation probes by their cross-model pass/fail patterns (leave-one-model-out, LOMO), and show it yields stable, interpretable failure taxonomies across three regimes usually studied separately: single-turn benchmarks, multi-turn dialogue, and adversarial agent attacks. On 2,664 single-turn tasks across 18 models, taxonomy-conditioned sampling reaches Kenda
The rapid advancement of large language models (LLMs) and their deployment in various applications necessitates robust methods for identifying and addressing their limitations.
Understanding specific model weaknesses beyond aggregate accuracy is crucial for developing reliable and safer AI systems, impacting their widespread adoption and trust.
This research provides a structured, interpretable framework for diagnosing LLM failures, moving beyond simple performance metrics to behavioral-level insights across different operational regimes.
- · AI developers
- · AI safety researchers
- · Companies deploying LLMs
- · Companies with poorly diagnostic evaluation pipelines
- · Undifferentiated LLMs
Improved debugging and fine-tuning of large language models, leading to more robust AI.
Accelerated development of specialized LLMs tailored to specific tasks by precisely addressing identified weaknesses.
Enhanced AI 'immune systems' where models can self-diagnose and potentially self-correct behavioral flaws.
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Read at arXiv cs.LG