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This work presents the first systematic study of inverse relation directionality in LLMs, using a benchmark consisting of 5,457 instances spanning 27 distinct inverse relation labels and reveals systematic asymmetries in inverse relation classification across LLMs.
These results indicate that while CoT-augmented LLMs achieve strong performance on deductive reasoning tasks up to five hops, solver augmentation remains valuable for deeper multihop deduction and for applications requiring robust and verifiable reasoning.
The results show that Romanian incurs a 3 to 5 percentage point drop relative to English in prompt-only settings, that few-shot prompting provides marginal gains over zero-shot, and that QLoRA fine-tuning improves macro F1-Score by more than 22 percentage points in both languages while reducing the cross-lingual gap from 3.3 to 1.4pp.