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Agentic AI systems can confidently generate plausible but wrong scientific results, even when given domain-specific context, highlighting a critical challenge for their integration into research workflows.
Forget prompt engineering: agent-based LLM data augmentation preserves labels better and boosts ABSA performance, especially for smaller models.