PaperActive

X-AuT: Progressive Audio-Encoder Compression for Speech LLMs with Cross-Scale Distillation

quality59

Updated 1 h ago · first seen 11 Sept 2026

paper_01M294WYDTQJF309BA9AX08EJW

Published: 10 Sept 2026; T2 · 5 h ago
arXiv: 2609.11412; T2 · 5 h ago

Abstract

Reducing audio-encoder depth lowers the inference cost of speech large language models, but removing complete blocks perturbs the embeddings consumed by the decoder and can cause deletion and premature end-of-sequence errors. We introduce X-AuT, a progressive framework that selects layer combinations through short behavioral probes and restores the pruned model through representation alignment, cross-scale distillation, scheduled student-policy supervision, and LoRA finetuning. The language-model backbone remains frozen, while attention LoRA adapters and the tied output embedding adapt during distillation. Training uses the highest-agreement tier from a transcript-consistency pipeline, followed by source reweighting during finetuning. On ten public Chinese--English benchmarks, compressing Qwen3-ASR-0.6B from 18 to 16 audio-encoder layers reduces macro-average error from 5.61% to 5.27%. The 14-layer model reaches 5.75% with 20.7% fewer audio-tower parameters. Under the matched recipe, the 1.7B teacher yields 5.55% mean error, compared with 8.45% for self-distillation, and progressive 18rightarrow14 pruning outperforms direct pruning (5.75% vs. 6.73%). These single-run results establish two practical operating points and show that the accuracy effects vary across benchmarks. Project website: https://xpeng-ai.github.io/x-aut

Authors 10

Haojun Zhang, Yi Zou, Min Chen, Qize Yu, Lianrui Fan, Xini Ding, Hao Li, Shuchang Zhou, Xianming Liu, Shiyu Huang

Specification

Official page: arxiv.org/abs/2609.11412
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 5 h agomedium
arXiv id: 2609.11412
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 5 h agomedium
Github repo: XPENG-AI/X-AuT
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 5 h agomedium
Hf paper url: https://huggingface.co/papers/2609.11412
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 5 h agomedium
Github stars: 7
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 1 h agomedium
Hf comments: 2
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 1 h agomedium
Upvotes: 13
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 1 h agomedium
PDF: arxiv.org/pdf/2609.11412
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 5 h agomedium
Published: 10 Sept 2026
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 5 h agomedium

Each value shows its source, tier and observation time. Conflicting claims are kept side by side and flagged — never averaged. How AI Atlas records facts →

Provenance

Attributed facts

Source tiers

T211

Freshest observation

1 h ago

Conflicts

None

As of

Rewind the record: see this entity's attributes exactly as AI Atlas knew them on a given day.

Claim history · Upvotes

1 claims · 1 propertiesShow all properties

Upvotesmetric.upvotes1

Claim history for Upvotes
Value	Valid from → to	Status	Source	Confidence	Extractor
13	11 Sept 2026 21:10 UTC → current	current	Hugging Face Hub (public pages, model cards, papers)T2	medium	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

Source documents
Source	Document	Type	Tier	Last observed	Snapshots
Hugging Face Hub (public pages, model cards, papers)	huggingface.co/papers	listing	T2· Quality secondary	1 h ago	4

Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.