Speech Retrieval-Augmented Generation (Speech RAG) systems are increasingly used in real-world settings where spoken queries vary across speakers, emotions, and acoustic conditions. However, current evaluations primarily focus on clean-condition retrieval accuracy, leaving unclear whether these systems remain semantically invariant under paralinguistic variation. We introduce ParaSpeechRAG, a counterfactual robustness benchmark that isolates non-semantic acoustic factors while preserving lexical content. The benchmark spans controlled perturbations across speaker identity, emotion and prosody, additive noise, speaking rate, and codec compression, enabling systematic evaluation of semantic invariance in speech retrieval. We evaluate three retriever families: ASR-based cascades, speech-text multimodal retrievers (CLASP), and general-purpose audio-language models (GLAP). Results reveal a consistent robustness–accuracy trade-off: multimodal retrievers achieve higher performance under clean conditions but are substantially more sensitive to paralinguistic and acoustic perturbations, particularly emotion and noise. In contrast, ASR-based cascades are more robust to these variations due to their text-centric bottleneck, though with reduced expressive capacity. These findings highlight an overlooked dimension of instability in Speech RAG systems and underscore the need for robustness-aware evaluation beyond clean-condition benchmarks.

We present Acessilia, an agentic document-parsing architecture developed for the Dr.DocBench track of the DocInsights 2026 shared task. Independent structural agents use Docling and MinerU to generate alternative canonical document trees. A Tree Differ aligns these representations using textual and spatial evidence, identifying discrepancies that are resolved by an adjudicator agent leveraging a vision-language model and visual evidence. The architecture supports hierarchical adjudication of specialised subtrees, including tables and mathematical expressions, while a deterministic serialiser converts the reconciled tree into the benchmark format. On the 509-page hidden test set, the final integrated pipeline achieves an official EvalAI overall score of 70.37, compared with 58.09 for the MinerU-only baseline, an absolute improvement of 12.28 points. Reading order shows the largest gain, increasing from 53.44 to 72.30, while text accuracy reaches 74.67 and table TEDS reaches 58.31. Formula CDM remains near zero at 0.22. These results demonstrate the effectiveness of combining complementary parser outputs through a shared canonical representation, structural alignment, and adjudication, while highlighting the need for stronger mathematical recognition and specialised processing.