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On Temporal Binding in Large Audio Language Models
Paul Primus, Gerhard Widmer
TL;DR
The paper investigates how large audio language models bind sound events to temporal positions and which representations support temporal reasoning. Using mechanistic interpretability across three open-source models, it finds that intermediate event-name representations carry coarse temporal information and affect before/after beliefs, while precise timestamp shifts are not consistent.
Problem
It remains unclear how LALMs internally bind temporal positions to events and which hidden activations are involved in temporal reasoning, despite progress on coarse temporal benchmarks.
Source: §1 · p. 1Method
The authors use activation swapping and representation analysis in AF-Next, MOSS-Audio, and Qwen3-Omni to locate and intervene on event-bound temporal representations.
Source: §1 · p. 1, §3.1 · p. 2Results
Across the three models, intermediate event-name representations carry coarse temporal information and steering them shifts before/after beliefs, but does not consistently shift predicted event timestamps.
Source: §6 · p. 4, §5.2 · p. 4, §5.3 · p. 4Takeaways & Limitations
The findings suggest that coarse temporal reasoning and precise metric event localization rely on distinct mechanisms.
Source: §6 · p. 4, §5.3 · p. 4The analysis covers three models, a small set of query formulations, and relatively simple scenes with isolated query events and no strong overlaps.
Source: §6 · p. 4
Abstract
from arXiv · showhide
Reasoning about temporal structure of audio recordings requires Large Audio Language Models (LALMs) to associate sound events with their temporal position. Understanding the underlying mechanisms is a first step toward diagnosing failures and identifying model components that may need improvement. Using mechanistic interpretability, we investigate how temporal information is represented and bound to sound events in three open-source LALMs. We find that across all three, event-specific location becomes concentrated in event name representations at intermediate modality integration layers. These representations encode coarse event position along a low-dimensional, curved relative time trajectory. Steering event name representations along this trajectory systematically shifts before/after beliefs, providing evidence that these representations contribute to coarse temporal reasoning. In contrast, the same interventions do not reliably shift predicted onset timestamps, suggesting that coarse temporal reasoning and precise metric event localization rely on distinct mechanisms.
1. INTRODUCTION
The paper asks how LALMs bind temporal positions to sound events and which activations support temporal reasoning. It studies three open-source models with mechanistic interpretability methods to examine event-bound representations and their roles in coarse reasoning and precise localization.
1. INTRODUCTION
- The paper investigates how LALMs internally bind temporal positions to events and which hidden activations are involved in temporal reasoning.The authors frame this as a step toward locating sources of temporal-reasoning failures and identifying model components that may need improvement.Source: §1 · p. 1
- Using activation swapping and representation analysis, the study examines AF-Next, MOSS-Audio, and Qwen3-Omni for event-bound temporal representations and their causal roles.The investigation asks whether such representations support both coarse temporal reasoning and precise event localization.Source: §1 · p. 1
- The authors report a common mechanism for coarse temporal reasoning across the studied models, while precise localization appears to rely on a separate mechanism.This distinction is the paper’s stated overall result.Source: §1 · p. 1
2. EXPERIMENTAL SETUP
The experiments use synthetic sound scenes and natural-audio subsets to study temporal reasoning and event timing. Activation swapping compares model beliefs when activations are replaced using audio with reversed event order.
2. EXPERIMENTAL SETUP
- Synthetic scenes combine ESC-50 foreground events with FreeSound backgrounds, with foreground clips mixed at approximately −5 to 0 dB SNR.The development and test sets are disjoint, and background classes detected by an audio tagger are excluded from insertion.Source: §2 · p. 1
- RealDESED subsets provide 15–30 s natural recordings with temporal annotations for 15 sound-event classes.Recordings are selected for clearly ordered event pairs or identifiable events with limited temporal overlap.Source: §2 · p. 1
- Activation swapping replaces residual-stream activations with those from the same text query paired with audio whose event order is reversed.The resulting belief shift indicates the intervention’s effect; values near 1 indicate a strong shift and values near 0 little effect.Source: Figure 1 · p. 2
3. TEMPORAL INFORMATION IS BOUND TO EVENT NAME REPRESENTATIONS
The section examines where temporal information enters textual representations, how well event timing can be decoded from them, and whether they contribute to temporal reasoning. It finds that intermediate event-name representations carry coarse, event-specific temporal information.
3. TEMPORAL INFORMATION IS BOUND TO EVENT NAME REPRESENTATIONS
- The study uses activation swapping to locate temporal information in the text residual stream, then tests how precisely event position can be decoded from the representations.For decoding, a one-hidden-layer MLP predicts query and unmentioned-event midpoints from token activations, with five-fold evaluation across event classes.Source: §3 · p. 2, §3.2 · p. 2
3.1. Intermediate Layers Bind Temporal Information to Event Name Tokens
- In intermediate layers, swapping event-name tokens produces a substantial belief shift as audio influence decreases, indicating that temporal information becomes bound to these representations.At later layers, full-text interventions dominate, and the final token eventually has the strongest effect among individual tokens.Source: §3.1 · p. 2
3.2. Coarse Temporal Position Is Decodable from Event Name Representations
- The queried event midpoint is decoded most accurately from its own event-name representation, reaching roughly 3.5–4 s MAE at intermediate layers.Decoding the unmentioned event’s time from that representation or the queried event’s time from the first prompt token yields consistently higher errors; the decodability peak overlaps the modality-integration region and generalizes across sound classes.Source: §3.2 · p. 2, Figure 2 · p. 3
4. GEOMETRY OF EVENT-BOUND TEMPORAL ENCODING
The section defines temporal IDs from class-centered event-name activations and characterizes them as a low-dimensional, curved code for relative event position.
4.1. Extracting Temporal IDs
- Temporal IDs average class-centered event-name activations within 2.5 s midpoint bins, removing event-identity variation; bins with insufficient support are discarded.Source: §4.1 · p. 3
4.2. Temporal IDs Lie on a Low-Dimensional Curved Trajectory
- 83.9%, 86.4%, and 86.5% of temporal-ID variance is explained by the first two PCs, tracing a curved trajectory with early and late endpoints strongly anti-aligned.ID magnitude falls toward the recording midpoint and rises toward its boundaries.Source: §4.2 · p. 3, Figure 3 · p. 3
- Multivariate R² rises from 0.19 to 0.43 for AF-Next, 0.31 to 0.53 for MOSS-Audio, and 0.30 to 0.52 for Qwen3-Omni when fitting relative rather than absolute time.Source: §4.2 · p. 3
5. EVENT-BOUND TEMPORAL ENCODING CAUSALLY MEDIATES COARSE REASONING
The section tests whether steering event-name temporal IDs toward trajectory endpoints shifts coarse before/after beliefs and precise onset predictions.
5.2. Steering Before/After Beliefs
- 82%, 84.2%, and 70.7% of opposing predictions flipped under steering for AF-Next, MOSS-Audio, and Qwen3-Omni, respectively; aligned judgments were preserved in 99.5% of cases.Source: Figure 4 · p. 4, §5.2 · p. 4
5.1. Temporal ID Steering
- The intervention interpolates the queried event's temporal ID, steers it toward a trajectory endpoint, and adds that direction to event-name tokens.α controls intervention strength, and ρ_L is the norm of the selected token activations.Source: §5.1 · pp. 3–4
5.3. Steering Precise Sound Event Detection
- Steering did not induce consistent timestep displacement; AF-Next and MOSS-Audio responded asymmetrically, while Qwen3-Omni showed weaker bidirectional shifts toward the target.For AF-Next, backward steering largely reset predictions to 0 s.Source: Figure 5 · p. 4, §5.3 · p. 4
6. DISCUSSION AND CONCLUSION
Across three investigated LALMs, event-specific temporal information binds to event-name representations in intermediate integration layers, supporting coarse temporal reasoning. The analysis distinguishes this from precise localization and is limited to three models, few query formulations, and simple acoustic scenes.
6. DISCUSSION AND CONCLUSION
- Across three investigated LALMs, event-specific temporal information becomes bound to event-name representations in intermediate modality integration layers.Activation swapping and before/after steering provide evidence that this information is involved in temporal reasoning.Source: §6 · p. 4
- Nonlinear decoding predicts event midpoints with an MAE of around 3.5–4 s, while steering does not consistently shift predicted onsets.The results suggest event-bound temporal IDs support coarse temporal reasoning, while precise localization relies on additional mechanisms.Source: §6 · p. 4
- The analysis covers three models, a small set of query formulations, and relatively simple scenes with isolated query events and no strong overlaps.Future work should examine more challenging acoustic conditions and mechanisms for fine-grained localization.Source: §6 · p. 4