SEAR: Segment-Evidence-Aware Routing for Weak-to-Strong Multilingual Speech MCQ
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Computer Science > Computation and Language
arXiv:2609.11355 (cs)
[Submitted on 10 Sep 2026]
Title:SEAR: Segment-Evidence-Aware Routing for Weak-to-Strong Multilingual Speech MCQ
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Abstract:This paper describes our system for Task~2 of the second Multilingual Conversational Speech Language Model (MLC-SLM) Challenge. We adapt Qwen3-Omni-30B-A3B-Instruct with a segment-evidence-aware data and post-training pipeline. A language model converts timestamped ASR into coherent event spans, which are expanded by a boundary margin and cropped from the original recording. We then synthesize complementary semantic MCQs with Qwen3.6-27B and acoustic MCQs with Gemini~3.1 Flash-Lite, followed by structural, grounding, answer-consistency, and target-model trainability checks, yielding 359,825 verified MCQs across 21 language and accent variants. A text-only probe partitions the data into weak, text-answerable items used for supervised fine-tuning and strong, audio-dependent items used for reinforcement learning with Group Sequence Policy Optimization (GSPO), stabilized by debiased advantages, sequence-level importance correction, and dynamic filtering. Our system obtains 90.92% accuracy on the final official evaluation set.
| Subjects: | Computation and Language (cs.CL); Sound (cs.SD) |
| Cite as: | arXiv:2609.11355 [cs.CL] |
| (or arXiv:2609.11355v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.11355
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled SEAR: Segment-Evidence-Aware Routing for Weak-to-Strong Multilingual Speech MCQ, by Huy Hoang Le and Long-Bao Nguyen and Minh Tri Dao
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