From Semantics to Readout: Mechanistic Understanding of Audio Tokens after Fine-Tuning for Temporal Audio Grounding
Abstract
Large audio-language models (LALMs) convey acoustic evidence to language decoders through native audio tokens, yet the internal roles of these tokens remain poorly understood. Using temporal audio grounding as a diagnostic setting, we examine how language-model fine-tuning affects the layerwise semantics, decoder accessibility, and temporal output alignment of native audio-token states through four complementary analyses: query-conditioned token semantics, calibrated token readout, temporal-window probes, and residual-delta erasure during generation. Alongside substantial improvements in temporal localization, semantic analysis of Qwen2.5-Omni shows that latent evidence for queried events is already present before fine-tuning and that the audio tokens most strongly aligned with the queried event appear at similar temporal positions before and after fine-tuning. After fine-tuning, event-related information in audio tokens becomes more accessible to the decoder, especially in early and middle layers, and a cross-checkpoint control shows that this improvement arises primarily from decoder adaptation. Temporal probes show that the base checkpoint already contains recoverable information about annotated windows and that fine-tuning mainly improves alignment with each checkpoint's own predicted temporal support. Residual-delta erasure further shows that removing audio-token updates within predicted windows harms timestamp generation more than removing the same number of randomly selected updates. The same broad improvements in decoder readability and prediction alignment also appear in Qwen2-Audio. Together, these results support a semantics-to-readout account in which grounding fine-tuning helps the decoder read existing event evidence and connect it more reliably to temporal outputs.
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Research summary
This paper provides a rigorous mechanistic analysis of how fine-tuning enhances temporal grounding in Large Audio-Language Models, demonstrating that improvements stem primarily from enhanced decoder accessibility to latent event evidence rather than the creation of new semantic representations.
Methodology
The paper proposes a comprehensive mechanistic interpretability framework for Large Audio-Language Models (LALMs), specifically focusing on the "semantics-to-readout" hypothesis. The methodology is rigorous and multi-faceted, employing four distinct diagnostic tools: (1) Query-Conditioned Token Semantics to measure alignment between audio tokens and text event phrases; (2) Calibrated Token Readout using hidden-state insertion (patching) to assess decoder accessibility; (3) Output-Facing Temporal Diagnostics using linear probes on both annotated and predicted windows; and (4) Residual-Delta Erasure to test the functional necessity of specific token updates during generation. This combination of probing, patching, and causal intervention provides a robust separation of representation quality from decoder utility. The approach is well-suited for the domain, addressing the "black box" nature of multimodal alignment in LALMs.
Results & evaluation
The experiments are conducted on two prominent LALMs, Qwen2.5-Omni and Qwen2-Audio, fine-tuned on a newly constructed dataset, AudioGrounding-QA, derived from the AudioGrounding corpus. The evaluation demonstrates substantial improvements in temporal grounding metrics (mIoU, F1) post-fine-tuning. The core findings are supported by detailed layer-wise analyses. Key results include the discovery that latent event evidence exists in base models but is not effectively read out, and that fine-tuning primarily enhances decoder accessibility (particularly in early/middle layers) rather than creating new semantic representations. The cross-checkpoint controls and residual erasure results strongly support the authors' claims about the mechanism of improvement. The replication on Qwen2-Audio adds significant weight to the generalizability of the findings.
Reproducibility
The paper provides detailed descriptions of the diagnostic pipelines, including the construction of the semantic bank, the specific prompts for readout, and the implementation of linear probes and erasure interventions. The dataset construction is clearly explained, and the fine-tuning setup (LoRA on LM blocks only) is specified. While the full code is not explicitly linked in the text provided, the methodological detail is sufficient for reproduction by experts in the field. The use of standard metrics (mIoU, F1) and established embedding models (SBERT) further aids reproducibility.
Limitations
The study focuses primarily on temporal grounding, a specific task, which may limit the generalizability of the "semantics-to-readout" account to other multimodal tasks (e.g., audio captioning or open-ended dialogue). The analysis is largely confined to Qwen-based architectures; while Qwen2-Audio is used for replication, the broader landscape of LALMs (e.g., Whisper-based or other proprietary models) is not explored. Additionally, the "calibrated readout" relies on SBERT embeddings, which may not perfectly capture the semantic nuances required for complex event reasoning, potentially underestimating or overestimating readout gains. The residual erasure is limited to layer-level interventions, which might miss more complex, distributed circuits.
Broader impact
This work contributes significantly to the field of mechanistic interpretability for multimodal models. By demystifying how fine-tuning affects internal representations, it provides actionable insights for improving LALM training strategies, particularly for tasks requiring precise temporal or spatial grounding. It challenges the assumption that fine-tuning creates new knowledge from scratch, suggesting instead that it optimizes access to pre-existing latent structures. This has implications for efficient fine-tuning, model compression, and the development of more reliable multimodal AI systems. This paper provides a rigorous mechanistic analysis of how fine-tuning enhances temporal grounding in Large Audio-Language Models, demonstrating that improvements stem primarily from enhanced decoder accessibility to latent event evidence rather than the creation of new semantic representations.
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