This paper describes our first-place submission to Task B (generation with reference passages) of the SemEval-2026 Task 8 MTRAGEval shared task on multi-turn retrieval-augmented generation. We propose a heterogeneous ensemble of seven LLMs organised into two groups with distinct prompting strategies, and use a GPT-4o-mini judge to select the best candidate response for each instance. Our system ranked first among 26 teams, achieving a conditioned harmonic mean score of 0.78 and substantially outperforming the strongest organiser baseline (0.64). Ablation experiments show that diversity across model families, scales, and prompting strategies is critical: the ensemble consistently outperforms any individual model. We also include Meno-Lite-0.1, a 7B domain-adapted model with a favourable cost–performance trade-off, and present an analysis of MTRAGEval that highlights annotation limitations and directions for benchmark improvement.

The DataBench shared task in the SemEval-2025 competition aims to tackle the problem of QA from data in tables. Given the diversity of the structure of tables, there are different approaches to retrieving the answer. Although Retrieval-Augmented Generation (RAG) is a viable solution, extracting relevant information from tables remains challenging. In addition, the table can be prohibitively large for direct integration into the LLM context. In this paper, we address QA over tabular data first by identifying relevant columns that might contain the answers, then the LLM generates answers by providing the context of the relevant columns, and finally, the LLM refines its answers. This approach secured us 7th place in the DataBench lite category.

This work presents a speech-to-text system “Pisets” for scientists and journalists which is based on a three-component architecture aimed at improving speech recognition accuracy while minimizing errors and hallucinations associated with the Whisper model. The architecture comprises primary recognition using Wav2Vec2, false positive filtering via the Audio Spectrogram Transformer (AST), and final speech recognition through Whisper. The implementation of curriculum learning methods and the utilization of diverse Russian-language speech corpora significantly enhanced the system’s effectiveness. Additionally, advanced uncertainty modeling techniques were introduced, contributing to further improvements in transcription quality. The proposed approaches ensure robust transcribing of long audio data across various acoustic conditions compared to WhisperX and the usual Whisper model. The source code of “Pisets” system is publicly available at GitHub: https://github.com/bond005/pisets.