AI Research
SAAG: Structured Agent Assessment and Grounding
Exact-match evaluation of agent-calling obscures qualitatively different failure modes: a model may select the right function yet hallucinate argument values, or satisfy a schema while choosing a agent for the wrong reason. Existing benchmarks collapse these distinctions into a s...
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ArXiv CS.AI
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ArXiv CS.AI
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This research addresses a critical gap in AI agent evaluation by distinguishing between different types of failures, moving beyond simple binary scores to enable more precise debugging and improvement. The proposed SAAG framework offers a structured, multi-stage diagnostic process that not only identifies specific errors but also facilitates iterative self-correction, leading to more reliable agent performance.
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