A new workflow for multilingual agent benchmark adaptation using functional, cultural, and difficulty alignments improves non-English agent success rates by up to 32.7% over simple machine translation, indicating substantial benchmark-induced measurement error in prior multilingual evaluations.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
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AgentShield uses layered deception traps in LLM agent tool interfaces to detect indirect prompt injection compromises with 90.7-100% success on commercial models, zero false positives, and cross-lingual transfer without retraining.
citing papers explorer
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GAIA-v2-LILT: Multilingual Adaptation of Agent Benchmark beyond Translation
A new workflow for multilingual agent benchmark adaptation using functional, cultural, and difficulty alignments improves non-English agent success rates by up to 32.7% over simple machine translation, indicating substantial benchmark-induced measurement error in prior multilingual evaluations.
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AgentShield: Deception-based Compromise Detection for Tool-using LLM Agents
AgentShield uses layered deception traps in LLM agent tool interfaces to detect indirect prompt injection compromises with 90.7-100% success on commercial models, zero false positives, and cross-lingual transfer without retraining.