Existing textual-gradient prompt optimisers rely on a single Large Language Model (LLM) to diagnose failures, propose edits, and judge the results, conflating critique and refinement into one model’s judgement. We introduce DialPO (Dialectical Prompt Optimisation), a multi-agent framework that replaces the single- model feedback loop with a structured de- bate: an Advocate defends what the prompt does well, a Critic attacks failures, cross- examination filters their arguments adversar- ially, a data-isolated Synthesis model drafts the revised prompt from surviving arguments alone, and a regression gate rejects edits that degrade batch accuracy. We provide a theo- retical framework identifying three precondi- tions under which multi-agent debate improves the feedback-reasoning bottleneck of textual- gradient methods, and show that DialPO in- stantiates them by construction. Across 12 benchmarks spanning classification, reasoning, summarisation, and question answering, Di- alPO achieves the highest scores across various Target models, winning or tying a majority of datasets in head-to-head comparisons against each baseline while using 8–18× fewer LLM calls than beam-search optimisers. Compo- nent ablations across five Target models reveal that DialPO’s advantage scales inversely with Target-model strength, clarifying when dialec- tical debate helps prompt optimisation.

Semantic Textual Relatedness (STR) will provide insight into the limitations of existing models and support ongoing work on semantic representations. Track A in Shared Task-1, provides pairs of sentences with semantic relatedness scores for 9 languages out of which 7 are low-resources. These languages are from four different language families. We developed models for 8 languages (except for Amharic) in Track A, using Sentence Transformers (SBERT) architecture, and fine-tuned them with multilingual and monolingual pre-trained language models (PLM). Our models for English (eng), Algerian Arabic (arq), andKinyarwanda (kin) languages were ranked 12, 5, and 8 respectively. Our submissions are ranked 5th among 40 submissions in Track A with an average Spearman correlation score of 0.74. However, we observed that the usage of monolingual PLMs did not guarantee better than multilingual PLMs in Marathi (mar), and Telugu (tel) languages in our case.