Low-resource Indic machine translation (MT) remains challenging due to limited parallel corpora, linguistic diversity, and contextual ambiguities that often result in inaccurate translations and hallucinations. This study proposes AgenticRAG-GRPO, an RL-inspired retrieval-augmented translation framework that combines semantic retrieval with GRPO-inspired reward-guided candidate selection for context-aware English↔Indic MT. The framework dynamically retrieves relevant translation contexts and ranks candidate translations using a reward function based on SacreBLEU and BERT score. Experiments were conducted on the WMT 2026 Low-Resource IndicMT Shared Task for English↔Manipuri (Mni) and English↔Bodo (Brx) using IndicTrans2, NLLB-200 Distilled 600M, and Sarvam-Translate. The proposed study achieved s performance, obtaining BLEU scores up to 32.36, METEOR up to 67.01, BERTScore up to 95.04, and COMET up to 80.51 across the languages, while improving semantic faithfulness and reducing hallucinations through retrieval-guided generation. The framework also maintained inference efficiency without requiring task-specific model fine-tuning. These results demonstrate that AgenticRAG-GRPO provides an effective and scalable solution for low-resource English-Indic MT.

Clinical reasoning over electronic health records (EHRs) involves heterogeneous operations, including text interpretation, numerical computation, temporal filtering, and guideline-based aggregation. However, many existing LLM-based approaches still cast these heterogeneous operations as a single end-to-end generation process, obscuring their different reliability requirements and making intermediate failures difficult to inspect. We therefore propose a framework based on operation-mechanism alignment that represents clinical reasoning as a directed acyclic graph of typed operations, where each node is assigned to the execution mechanism best suited to its reliability requirements. The framework also preserves structured evidence provenance for intermediate results. Across six clinician-annotated binary decision tasks, the framework outperforms direct prompting, single-step retrieval-augmented prompting, and chain-of-thought baselines, supporting operation-mechanism alignment as a practical design principle for reliable clinical reasoning over EHRs.

Divergence is a key aspect of translation between two languages. Divergence occurs when structurally similar sentences of the source language do not translate into sentences that are similar in structures in the target language. Divergence assumes special significance in the domain of Example-Based Machine Translation (EBMT). An EBMT system generates translation of a given sentence by retrieving similar past translation examples from its example base and then adapting them suitably to meet the current translation requirements. Divergence imposes a great challenge to the success of EBMT. The present work provides a technique for identification of divergence without going into the semantic details of the underlying sentences. This identification helps in partitioning the example database into divergence / non-divergence categories, which in turn should facilitate efficient retrieval and adaptation in an EBMT system.