LayerRAG-Bench: A Cross-Layer Reliability Benchmark for Agentic Retrieval-Augmented Generation
Quick Answer
LayerRAG-Bench introduces a cross-layer reliability benchmark for agentic retrieval-augmented generation systems, comprising 240 tasks across 8 domains and 9 models from OpenAI, Anthropic, and Gemini.
Quick Take
The study reveals that schema normalization significantly improves schema-drift success but fails to address issues like stale evidence and wrong-session context, underscoring the need for layer-specific evaluation in reliability interventions.
Key Points
- LayerRAG-Bench includes 240 tasks and 9 fault scenarios across 8 enterprise domains.
- Schema normalization improved schema-drift success from 0.000 to 0.913.
- Issues like stale evidence and wrong-session context remain unaddressed by schema normalization.
- Groundedness-only evaluation leads to significant false positives under specific conditions.
- The study advocates for layer-specific evaluations in reliability interventions.
Paper Resources
Source Excerpt
Agentic systems can produce answers that appear grounded while failing at the evidence, tool-contract, authorization, or session-state layer. We introduce LayerRAG-Bench, a controlled cross-layer reliability benchmark with 8 enterprise domains, 240 tasks, 9 fault scenarios, 2 contract modes, and 38,880 live task-level records across nine models from OpenAI, Anthropic, and Gemini. Schema normalization raises schema-drift success from 0. 000 to 0. 913, but stale eviden
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