We present a domain-grounded benchmark and evaluation framework for tool-aware plan generation in contact-center analytics, where answering a business-insights query requires decomposing it into executable steps over structured tools (Text2SQL over Snowflake), unstructured tools (RAG over transcripts), and LLM-based synthesis, with explicit depends_on relations for safe parallel execution. Our contributions are threefold: (i) a reference-based plan evaluation framework with two complementary views—a metric-wise evaluator spanning seven dimensions (e.g., tool–prompt alignment, query adherence) and a one-shot evaluator that compares a candidate plan against a reference plan; (ii) a lineage-driven data curation methodology that uses an iterative evaluator→optimizer loop to refine initial plans into high-quality plan lineages while reducing manual effort; and (iii) a large-scale study of 14 LLMs across model families and sizes on their ability to generate step-by-step, executable, tool-assigned plans, evaluated with and without lineage in the prompt. Empirically, LLMs continue to struggle on compound queries and on plans longer than four steps; the highest aggregate metric-wise score is 84.8 (Claude-3-7-Sonnet), while the strongest one-shot A+ rate (Extremely Good or Very Good) is only 49.75% (o3-mini). Lineage yields mixed overall gains but improves several strong models and often helps step executability. Overall, our results expose persistent weaknesses in tool understanding—especially tool–prompt alignment and tool-usage completeness—and show that shorter, simpler plans remain markedly easier. The benchmark, evaluation framework, and findings provide a practical path for assessing and improving agentic planning with tools in enterprise question-answering settings. An anonymized dataset with human-annotated reference plans, plan lineages, and per-planner outputs for all 14 planners is available at the anonymous repository linked in the paper.

In the contemporary epoch of multilingual education, learning idioms provides a fascinating gateway towards creativity, cultural values, historical context, and diverse perspectives inherent to various linguistic traditions. This paper showcases the navigation of retaining figurative and cultural semantics in low-resource Southeast Asian languages such as Hindi, Bengali, and Thai, where culturally rich idioms pose significant obstacles for computational modelling and cross-linguistic transfer due to their deep metaphorical complexity. To tackle such complexity, we present Varnika (वर्णिका) , a reconstructed multimodal idiom corpus comprising 3,533 multilingual idioms, enriched with seven idiomatic tones aligned with both textual and visual representations. Additionally, to infer informative idiomatic understanding, we introduce a Hybrid Mixture-of-Experts (HybridMoE) framework that embeds multiple idiomatic expert opinions while mitigating expert sparsity by integrating outputs from both selected and unselected experts through controlled hybridisation, further augmented with Idiomatic Property Signals via masked multimodal embeddings. To analyse the performance across multiple dimensions, we propose the IDIO-TONE and Idiomatic Validation Score, a three-stage evaluation pipeline measuring (i) literal translation fidelity, (ii) visual- semantic alignment, and (iii) idiomatic meaning retention. Empirical evaluations highlight that HybridMoE achieves 5–6% performance gains across advanced vision language models, demonstrating improved representation of figurative language and culturally embedded meaning in multilingual multimodal settings. Resources are available at (https://github.com/sarmistha-D/Hybrid_MOE).