We present H-RAG, our submission to SemEval-2026 Task 8 (MTRAGEval), addressing both Task A (Retrieval) and Task C (Generation with Retrieved Passages). Task A evaluates standalone retrieval quality, while Task C assesses end-to-end retrieval-augmented generation (RAG) in multi-turn conversational settings, requiring both accurate answer generation and faithful grounding in retrieved evidence. Our approach implements a hierarchical parent–child RAG pipeline that separates fine-grained child-level retrieval from parent-level context reconstruction during generation. Documents are segmented into overlapping sentence-based child chunks, while full documents are preserved as parent units to provide coherent context. weighting, and embedding-based similarity rescoring over child chunks. Retrieved evidence is aggregated at the parent level and supplied to an instruction-tuned language model for response generation. H-RAG achieves an nDCG@5 score of 0.4271 on Task A and a harmonic mean score of 0.3241 on Task C (RBagg: 0.2488, RLF: 0.2703, RBllm: 0.6508), underscoring the importance of retrieval configuration and parent-level aggregation in multi-turn RAG performance.

When LLMs are given contradictory passages, the generated response may appear decisive while masking how internal signals behave under conflicting evidence. Does it favor one passage, blend both, or ignore both? We study model behavior under inter-context contradiction by introducing a white-box tracing framework that extracts internal model signals based on hidden-state shifts, counterfactual attribution via passage removal, and token-level uncertainty during generation under two single-passage prompts and one dual-passage prompt with conflicting evidence. We evaluate four open-weight LLMs from the LLaMA (8B and 70B), OLMo, and Qwen on WikiContradict, a human-annotated QA benchmark with contradictory passages. Each dual-passage answer is labeled as committing to passage A or passage B, blending both, or fabricating an answer unsupported by either passage. Comparing these labels with the extracted signals shows that larger models reduce fabrication, but hidden-state and probability-level signals can disagree with the final answer. Moreover, fabricated answers often remain high-confidence, showing that confidence alone is unreliable for identifying ungrounded behavior under contradictory evidence. We further validate on ConflictBank, a larger misinformation-conflict QA dataset, showing consistent disagreement patterns across models. Finally, we test whether pre-generation white-box signals are informative beyond interpretation by training classifiers to predict output behavior. Classifiers using white-box signals outperform probability baselines, showing these signals contain diagnostic information about model behavior under contradictory evidence beyond log probabilities.

How far have we come in mitigating performance disparities across genders in multilingual speech recognition? We compare the impact on gender disparity of different fine-tuning algorithms for automated speech recognition across model sizes, languages and gender. We look at both performance-focused and fairness-promoting algorithms. Across languages, we see slightly better performance for female speakers for larger models regardless of the fine-tuning algorithm. The best trade-off between performance and parity is found using adapter fusion. Fairness-promoting fine-tuning algorithms (Group-DRO and Spectral Decoupling) hurt performance compared to adapter fusion with only slightly better performance parity. LoRA increases disparities slightly. Fairness-mitigating fine-tuning techniques led to slightly higher variance in performance across languages, with the exception of adapter fusion.