STAR-Bench: Probing Deep Spatio-Temporal Reasoning as Audio 4D Intelligence
Authors: Zihan Liu
Zhikang Niu
Qiuyang Xiao
Zhisheng Zheng
Ruoqi Yuan
Yuhang Zang
Yuhang Cao
Xiaoyi Dong
Jianze Liang
Xie Chen
Leilei Sun
Dahua Lin
Jiaqi Wang
Venue: ICLR 2026 Poster (2026)
Keywords: audio understanding
spatio-temporal reasoning
4D Intelligence
Primary area: datasets and benchmarks

Abstract: Despite rapid progress in Multi-modal Large Language Models and Large Audio-Language Models, existing audio benchmarks largely test semantics that can be recovered from text captions, masking deficits in fine-grained perceptual reasoning.
We formalize audio 4D intelligence that is defined as reasoning over sound dynamics in time and 3D space, and introduce STAR-Bench to measure it. STAR-Bench combines a Foundational Acoustic Perception setting (six attributes under absolute and relative  regimes) with a Holistic Spatio-Temporal Reasoning setting that includes segment reordering for continuous and discrete processes and spatial tasks spanning static localization, multi-source relations, and dynamic trajectories. Our data curation pipeline uses two methods to ensure high-quality samples. For foundational tasks, we use procedurally synthesized and physics-simulated audio. For holistic data, we follow a four-stage process that includes human annotation and final selection based on human performance. Unlike prior benchmarks where caption-only answering reduces accuracy slightly, STAR-Bench induces far larger drops (-31.5\% temporal, -35.2\% spatial), evidencing its focus on linguistically hard-to-describe cues. Evaluating 19 models reveals substantial gaps compared with humans and a capability hierarchy: closed-source models are bottlenecked by fine-grained perception, while open-source models lag across perception, knowledge, and reasoning. Our STAR-Bench provides critical insights and a clear path forward for developing future models with a more robust understanding of the physical world.

Published as a conference paper at ICLR 2026
STAR-BENCH: PROBING DEEP SPATIO-TEMPORAL REASONING AS AUDIO 4D INTELLIGENCE
Zihan Liu Zhikang Nin Qiuyang Xiao Zhisheng Zheng", Ruoqi Yuan', Yuhang Zang Yuhang Cao², Xiaoyi Dong" Jianze Liang", Xie Chen Leilei Sun', Dahua Lin Jiaqi Wang 2.5mm
Beihang University, Shanghai AT Laboratory, Shanghai Jiao Tong University,
"The Chinese University of Hong Kong. Shanghai Innovation Institute
liuz 1 haw@haaa. cn, wangguhanggeellb..or....
Code: hctpas//gtthhhi..cc////mmmmmm//r
Benchmark: http:://mteemun.gggtau.oooo/o//
Homepage:
ABSTRACT
Despite rapid progress in Multi-modal Large Language Models and Large Audio- Language Models, existing audio benchmarks largely test semantics that can be recovered from text captions, masking deficits in fine-grained perceptual reason- ing. We formalize audio 4D intelligence that is defined as reasoning over sound dynamics in time and 3D space, and introduce STAR-Bench to measure it. STAR- Bench combines a Foundational Acoustic Perception setting (six attributes under absolute and relative regimes) with Holistic Spatio-Temporal Reasoning setting that includes segment reordering for continuous and discrete processes and spatial tasks spanning static localization, multi-source relations, and dynamic trajectories. Our data curation pipeline uses two methods to ensure high-quality samples. For foundational tasks, we use procedurally synthesized and physics-simulated audio. For holistic data, we follow a four-stage process that includes human annotation and final selection based on human performance. Unlike prior benchmarks where caption-only answering reduces accuracy slightly,
induces far larger drops (-31.5% temporal, -35.2% spatial), evidencing its focus on linguistically hard-to-describe cues. Evaluating 19 models reveals substantial gaps compared with humans and a capability hierarchy: closed-source models are bottlenecked by fine fine-grained perception, while open-source models lag across perception, knowl- edge, and reasoning. Our STAR- Bench provides critical insights a clear path forward for developing future models with more robust understanding of the physical world.
INTRODUCTION
As fundamental modality of human perception, audio serves a pivotal role communication, aes- thetic appreciation, and situational awareness, complementing the limitations of visual perception. With the rise of Multimodal Large Language Models (MLLMs) (Comanici et al. [025 Achiam
al. 2023) and especially Large Audio-Language Models (LALMs) (Chu et al. 2024 Goel al.
2025
these models have shown impressive capabilities in understanding audio, representing a cru-
cial step toward diverse applications such as embodied intelligence (Paul et al. 2022).
To drive progress, a series of audio benchmarks has been introduced (Yang et al. 2024 Sakshi et al. 2025), covering traditional tasks like Automatic Speech Recognition (ASR) and sound event classi- fication. While some recent efforts are beginning to emphasize reasoning abilities (Ma et al.) 2025 Kumar et al., 2025), we observe that existing benchmarks predominantly focus on coarse-grained semantic content, which is audio information that can be distilled into textual descriptions with min- imal loss. As shown the left part of Fig. [ we first use Gemini 2.5 Pro (Comanici et al., 2025) to generate detailed audio captions for samples in recent representative audio benchmarks MMAU (test-mini) (Sakshi et al. (2025) and MMAR (Ma et al., 2025). We then prompt the model to answer questions based only these audio captions, and its performance drops by only 5.9% and 9.0%,

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Published as a conference paper at ICLR 2026
Three Core Abilities Required for ST AR-Bench
Linguistically Hard-to-Deseribe
88,00
Audio Cucs
Accurccy (%0
82:20
77.80
$0.70
73-70
Multi-step Reasoning
21,
71,70
58,50
Missing any ability results in incorrect
43,60
4D
World Kinwwledge (Physscc
Common Sense)
MMAR
Temsoora (ours)
Spatial (ours)
Fine-grained
Perception
Human Audio Gemini-2.5
Audio cuption Gemini-2.5
Figure I: (Left): A comparison between humans and the Gemini 2.5 Pro with and without audio captions various audio benchmarks. Our STAR-Bench evaluates linguistically hard-to-describe audio cues. See Appendix [B.] for audio caption details. (Right): The three core abilities required solve tasks in the STAR-Benee benchmark.
respectively, compared to when it processes the raw audio. This result suggests that existing bench- marks primarily evaluate audio information that is easily representable by text. However, human auditory intelligence is not limited to this coarse- grassee understanding. For example, humans can intuitively judge the water level in container from the dynamic changes in the pouring sound, even without being able to precisely articulate the underlying acoustic features. Similarly, we can infer the trajectory and distance of a vehicle approaching from behind to ensure our safety. These abilities are rooted in deep reasoning of audio cues that are difficult to represent linguistically.
To capture this human-like audio competence, propose new paradigm, called audio 4D intelli- gence. This defined as the ability to perform deep reasoning over the dynamics of sound sources in time (1D) and three-dimensional space (3D), grounded in understanding of the physical world. Mastering 4D audio intelligence crucial for various applications. In embodied Al and robotics, for instance, agents must integrate fine- -eeiggaaied auditory cues to interact naturally with their surroundings, such as using sound to infer the trajectory of an object or to monitor the sub- tle operations of machine. To systematically evaluate this paradigm and bridge the gap between current audio benchmarks and real-world auditory intelligence, we introduce the Spatio-Tempora Temporal Audio Reasoning STARRBENCH benchmark.
STAR-BENCH is designed through hierarchical task structure with two levels. At the Foun- dational Acoustic Perception level, we conduct fine-grained, quantitative evaluation of six core audio attributes (pitch, loudness, duration, azimuth, elevation, distance) across both absolute percep- tion ranges and relative discrimination sensitivity. We also introduce a Holistic opatio-Temporal Reasoning level that evaluates an audio model's ability to infer both event order and 3D scene structure. Temporal reasoning is tested via segment reordering that spans continuous processes and discrete event scripts, while spatial reasoning covers static localization, multi- relations, and dynamic trajectory tracking. As shown in the right part of Fig. [ every question in our holistic tasks designed to probe synthesis of three core pillars, such multi-step reasoning. A failure in any one of these pillars will lead to an incorrect response. Our data curation pipeline couples procedu- rally synthesized, fully parameterized audio for foundational perception with large-scale real- world corpora for holistic reasoning. For the latter, we use a four-stage process including human annota- tion and final selection by human performance to ensure the high quality of benchmark samples.
Our comprehensive evaluation of 19 models (16 open-source and 3 closed-source) reveals clear capability hierarchy between the two groups. Leading closed-source models like Gemini 2.5 Pro excel in knowledge and reasoning, shifting their primary bottleneck to the more difficult challenge of fine- mne-ggaieed perception. In contrast, open-source models exhibit fundamental weaknesses across all three core capabilities. Through our detailed error analysis and ablation studies, we highlight several key insights for the future development of open-source audio models: 1) Enhancing dense audio captioning. Open-source models struggle to produce dense, fine-grained captions, which limits their perceptual sensitivity and ability to extract embedded knowledge. Bridging this gap is crucial first step. 2) Improving multi-audio reasoning. Open-source models lag significantly in comparing, integrating, and grounding information across multiple audio clips. 3) Moving beyond channel-averaged audio preprocessing. The common practice of averaging multi-channel audio
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Table 1: A comparative overview of our benchmark against other representative audio benchmarks.
(/: Fully supported, O Partially supported or limited amount, X: Not supported)
Quantitative Attribute Evaluation
Fally Human- Annotated
Fally
Temporal
Spatial
Robust Evaluation
Multi- Audio
Benchmark
Deep Reasoning
Deep Reasoning
Expert Verified
AIR-Bench 45
X
x
X
x
MMAU 10
x
Dynamic-S SUPERR Phase-2
16
X
X
x
c
c
MMAR 27
X
K
c
MMAU-Pr
X
x
/
/
STAR-BENCH
into mono signal is major bottleneck for spatial reasoning. Developing architectures that natively process multi-channel cues is essential for unlocking genuine spatial awareness.
Our contributions summarized (1) We formalize audio 4D intelligence, and empirically show that prior benchmarks largely probe text-representable semantics, motivating a shift toward fine-grained, non-linguistic auditory cues. (2) We introduce the STAR-BENCH with foundational acoustic perception and holistic spatio-temporal reasoning tasks, together with rigorous curation pipeline with expert validation. (3) We provide a comprehensive evaluation of 19 LALMs/OLMs. Our analyses and standardized protocols establish strong baselines and testbeds for future research.
2
RELATED WORK
The recent progress of Large Audio-Language Models (LALMs)(Kong et al. 2024 Chu et al. 2024 Wu et al.) 2025 Xiaomi 2025) and Omni-Language Models (OL et al.) 2025 Yao al. 2024: AI al. 2025) has significantly advanced audio understanding. At the same time, has spurred the development of numerous benchmarks to comprehensively evaluate their capabil- ities. Earlier benchmarks Wang et al. 2024 Yang et al., 2024) mainly focused on semantic-level understanding tasks (transcription, captioning, and simple question answering), and recent bench- marks(Sakshi et al., [2025] Ma et al. 2025 Kumar et al. 2025) have begun to investigate logical audio reasoning tasks.
However, existing benchmarks largely overlook audio 4D intelligence. Although some advanced benchmarks do touch upon spatio-temporal aspects, their coverage remains limited in both scale and depth. While MMAU Sakshi et al. (2025). MMAU-Pro Kumar et al. (2025) and MMAR Ma et al. 2025) contain temporal questions, they mainly involve identifying the timing or ordering of events (e.g., when a sound occurs, which event comes first). These are primarily perceptual-layer tasks. By contrast, our "temporal deep reasoning" tasks require understanding physical principles or causal dynamics across segments (e.g., inferring how a process evolves over time or how one event implies another), which cannot be solved by local timing cues alone. In addition, the spatial tasks in MMAR and MMAU-Pr are often restricted to single-source localization, and many items do not necessitate meaningful use of stereo cues (c.g., simple arriving vs. departing judgments). In contrast, STAR- BENCH introduces hierarchical design covering three sub-tasks in complex scenes and explicitly emphasizes stereo-cue-basee based reasoning.
A comparative overview of STAR-BENCH and prior benchmarks is presented in Tab. I STAR- BENCH evaluates deep spatio-temporal reasoning through tasks that go beyond surface-level per- ception and instead require applying physical causal knowledge, performing multi-step reasoning in complex real-world scenarios, and integrating information across multiple clips or events. STAR- BENCH rests hicrarchical and comprehensive task design. In addition, rigorous data curation pipeline ensures high-quality samples, and robust evaluation strengthens the reliability.
3 STAR-BENCH
Understanding dynamic sound sources in both time (ID) and three-dimensionall space (3D) is crucial skill for MLLMs to comprehend the physical world. To address this need, our benchmark, STAR-BENCH is designed to comprehensively evaluate this 4D intelligence in the audio domain. As illustrated in Fig. 2] our evaluation has two complementary sub-tasks: (1) Foundational Acoustic Perception (Sec. 3.1 which uses procedurally synthesized audio quantitatively profile model's
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Absolute Porception Range
Relative Discrimination Sensitivity hard -aasy à
Foundation
audingram (gtch & hoolnow))
sputial sector localization
pitch
londness
duration
azimuth
edevation
distance
Which sound has a higher pitch?
Which sound louueer?
Which sound
they the sume arimuth?
Which audio hos the higher elevation angle?
which audio farther away?
is longer?
Hz
dB
(1 + A) %
Temporal Reasoning
Spatial Reasoning
Question T Template: You are specialized sound event ondering expert. Pinase listen to the following three audio clips labelad clip cbp 2, and cliy. 3, and determine the most natural chronological order which these sounds would typically occur in the real world.
[binaural audio]
Native /Withoud special processing)
(azadio] QQuestion[(ptiinnn
2yChanmel wase
clip E: [segul clip 2: [seg]] dip 3: (seg_k)
Audbo [chamnel)] J Audio 2: [chamnelz] You are given binaural recioding: Audin is the left-car chaunel and Atdio 2
Continuous Processes
Discrete Event Sequences
the right-our chancel. [Question] | (Oytions]
Object Spatial Motion
Tool & Appliance Operation
Single-source Static Localization Multi-source Spatial Relation
opprosciiss and orives cnelyy.
Question: [beaardd usiio) is the pf the thher sound?
Questione:
Who
Holistic
funber Jeh, Chira or the boy from bndia"
Optiona:
Opeions:
do Dheetlyaaad
she The buy from Chin
des Lell vale sighe
bay from
Buth peeition
[Pag]]
[eg.l....
(1-1pp (1 dipgi dips (ong
@ dip (ig
dep leg 0 cip
Answrr BBo Left side
Asswer dão The from Chira
Anewer chodlp do
Amswer dho t dip to dip
Knowledge Dogpler (ffet hperc
Knowledger Skonkerinnl speniing procolore
Dynamic Trajectory Tracking
In-Situ Stato Evolution
Dally Scene Scripts
Event-Triggered Consequences
Question: [binsard
(Parigga warr g
[ABte quectig yeareernn and eways)
[eaaligg balls, and shatninge gihn fustl))
right lek, canoe nasdee sin. frems lightt
Optiona:
R... le.s......
[ogal U
<b Seep comiing
<A> Turn riphs, then lott
dip
.llt dlp 4pp3--dpp
<Do Keep nurning right
Knowledge: Dymanic
Knowledge
Komwladge:
Answer Tum left them right
Figure 2: Data examples from STAR-BENCH: (1) the foundational perception task (upper) and (2) the holistic spatio-temporal reasoning task, which includes both temporal reasoning (bottom left) and spatial reasoning (bottom right). Zoom in for the best view.
basic perceptual abilities under controlled conditions, and (2) Holistic (Sec.3.2) which uses real-world audio to evaluate more complex reasoning in dynamic and authen- tic scenarios. We also elaborate our data curation pipeline in the Sec. 3.3
Reasoning
3.1 FOUNDATIONAL ACOUSTIC PERCEPTION
The Foundational Acoustic Perception task is motivated by the need for a robust, quantitative eval- uation of the core perceptual abilities that underpin 4D audio intelligence. A model's capacity for complex reasoning about dynamic audio scenes in the physical world is directly dependent on its ability to accurately perceive fundamental acoustic properties. Our foundational acoustic perception task systematically probes a model's understanding of three critical auditory attributes: Loudness, Pitch, Duration, and the three spatial dimensions: Azimuth, Elevation, and Distance. Just solid understanding of grammar is required for writing a complex narrative, a model must be able to accurately perceive these core attributes before it can reason about the dynamic, spatial relationships of sound sources in the physical world. Without a firm grasp of these foundational elements, a model cannot accurately interpret complex, real-world acoustic scenes, which require understanding how sounds change over time and move through space.
We employ a targeted synthesis strategy to generate precise evaluation samples in a controlled en- vironment for the foundational perception task. For non-spatial attributes (Loudness, Pitch, Dura- tion), we synthesize pure sine waves by directly specifying their parameters. For spatial attributes (Azimuth, Elevation, Distance), use the Pyroomacousties (Scheibler et al.) 2018)) physics-based simulation engine to render acoustic scenes. The targeted synthesis strategy allows us to investigate model's audio perceptual abilities under the following two sub-tasks:
1) Absolute Perception Range, which defines the sensory limits of MLLMs for acoustic attributes. For pitch and loudness, we adapt the design of human audiometry tests to create an "audiogram" for the MLLMs. Specifically, we synthesize sine waves with frequencies ranging from 125 Hz to 8000 Hz and loudness levels from - to 110 dB HI. and require the model to identify if clear beep is
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in the first second part of audio clip, or it's not there at all. For spatial attributes, design interval localization tasks that require the model to identify a sound's azimuth within one of four 90 quadrants (from 0% to 360"), its elevation relative to ear-level (above, or below, from -90 to 90"), and its distance category (near, medium, or far, within a 0 10m range). Tab. presents detailed examples of these absolute perception range tasks. Through these precise tasks, establish the absolute limits of what the model can hear, which is crucial for developing AI systems that can safely and effectively interact with the physical world.
2) Relative Discrimination Sensitivity, which investigates how well a model can detect small changes in acoustic attributes. The ability detect small changes allows a model to make nuanced judgments, like determining if a sound is getting louder or pitch is rising, Analogous to measuring the human Just Noticeable Difference (JND), the relative discrimination task presents the model with an audio clip containing two sounds and requires it to compare them based on specific attribute. We meticulously designed four to six distinct difficulty levels for each of the six attributes, as de- tailed in Tab. 3 Level I serves as a control group to test for random guessing, presenting identical sounds (A=0) for non-spatial attributes and a sub-threshold difference for spatial ones. Subsequent levels then introduce progressively larger differences, ranging from subtle variations perceptible to humans to more significant, real-world changes. By analyzing the model's performance across these different levels of stimulus differences, we can quantitatively assess its discrimination sensitivity for each attribute.
3.2 HOLISTIC SPATIO-TEMPORALL REASONING
Building the model's fundamental audio perceptual abilities (Sec. 3.1). we further introduce holistic temporal reasoning (Sec. B32.1] and spatial reasoning (Sec. (3.2.)). which are designed to systematically evaluate model's reasoning ability that is required for audio 4D intelligence.
3.2. 1
TEMPORAL REASONING TASKS
The core of temporal reasoning lies in understanding the intrinsic logic of event sequences, encom- passing physical causality, functional procedures, social conventions. To evaluate this capability, we design novel Audio Segment Reordering setting. Specifically, we curate collection of au- dio events characterized by strong sequential uniqueness. semantic clarity, and logical universality. Each event is segmented into three clips, which are then shuffled as inputs to the model. The models are required to restore the original temporal sequence based solely on the audio content. Our tem- poral reasoning tasks are organized into two meta-categories (continuous processes, discrete event sequences) and five subcategories based on their core logical principles.
The continuous processes assess model's ability to track the subtle, continuous evolution of acous- tic features within a single, uninterrupted acoustic event. The object spatial motion subcategory reconstructs the spatio-temporal trajectory of moving sources (e.g.. passing cars, airplanes) by inter- preting key acoustic cues, such as the Doppler effect (frequency shifts indicating relative velocity) and the inverse-square law (loudness changes indicating distance). Besides, the in-situ state lution subcategory assesses a model's ability to track the intrinsic evolution of a stationary object's state, a process governed by predictable trend patterns. These trend patterns arise from various un- derlying principles, including: Fluid & Pneumatic Dynamics, where the sound is governed by prin- ciples of turbulence, resonance, and pressure changes (e.g., toilet flushing, water being poured); Thermodynanic Processes, involving irreversible state changes driven by heat (e.g., water boiling, food frying); Energy Decay, a process governed by resonant decay and frictional damping after a single excitation (e.g., a bell's chime, an explosion's echo); and complex Biological Rhythms that reflect an evolving physiological or emotional state.
The discrete event sequences category requires the model to understand the logical and temporal relationships between multiple, distinct acoustic events, which are governed by function, conven- tion, causality. The tool & appliance operation sub-category follows the standardized operating procedure for tools and appliances (c.g., a microwave, a power drill), where the sequence is correct when it follows the tool's designed function. The daily scene scripts sub-category applies com- monsense and contextual script knowledge to follow the conventional sequence actions in daily activity (e.g., brushing teeth, drinking water). The event-triggered consequences sub-category ap-
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reasoning Published as a conference paper at ICLR 2026
plies causal reasoning to infer that trigger event (e.g., firework explosion) will be followed by an automatic and irreversible outcome, whether physical (glass shattering) or social (a crowd cheering).
3.2.2 SPATIAL REASONING TASKS
Humans effortlessly perceive complex 3D auditory scenes (e.g., hearing a voice from behind, follow- ing an approaching car, or locating multiple speakers). Such an ability is fundamental for egocen- tric interaction and embodied Al systems, for instance, robots that navigate and interact with their surroundings. However, existing benchmarks focus primarily on the localization of static sound sources, whereas real-world scenarios demand reasoning that integrates both spatial and temporal cues. To address this gap, we organize the spatial reasoning task into three subcategories.
The single-souree static localization evaluates the model's ability to identify the direction of tar- get sound source among multiple statie sources (e.g., judging whether sound comes from the left or right). It assesses the basic spatial perception capability of the model and provides the foundation for more advanced reasoning. The multi-source spatial relation requires the model determine the relative spatial relationships among multiple simultaneous sound sources (e.g., comparing the placement of two speakers to decide which one is further to the right). Beyond localizing each source individually, the model must infer their spatial placement and choose the appropriate rela- tional description from multiple candidates. The dynamic trajectory tracking introduces moving sound sources, which require the model to go beyond basic spatial perception to dynamically model apatio-temporal relations for reasoning about complex movement trajectories (e.g.. tracking a pass- ing car moving from left to right). This task extends spatial reasoning into the temporal domain and is more faithful to the complexity of real-world acoustic scenarios.
However, evaluating existing LALMs on multi-channel spatial tasks is chal- lenging. The common practice of these models is to average multi-channel au- dio into mono signal, resulting in the loss of substantial spatial informa- tion. We conduct a simple experiment as shown in Fig. 3 We construct 20 pseudo-stereo signals by assigning the original audio to the left channel and its additive inverse to the right. While human listeners could easily perform sound event classification on these sig- nals, the models consistently failed due to signal cancellation during the mono conversion. The result confirms their lack of explicit support for genuine stereo audio processing. To provide a comprehensive assessment, we adopt two complementary strategies. The first is the native input setting, where the model directly processes stereo audio using its default pipeline. This allows us to probe its intrinsic ability exploit spatial cues. The second is the channel-wise input setting, where the left and right channels are presented separately with explicit textual instructions, as shown in the ablation study to examine whether current models have any spatial capability when the binaural information is preserved at the input.
channe-averaged waveform (meno)
Left -w--mm
Additive
Right -wwmmmm
Invvrsss
Question: What sound do you hear this audio?
Accuracy
<Ber Rird chirping
Random
25%
human
Human
100%
Options:
Gemini
BAT
100%
Dog barking <Rs chirping
frandow guezs)
<
x
Gemini 2.5 pmo
20%
<Cs Cat meowing
Gp
-Ds No sound / SSlencc
preview
<DD> No sound SSlonce
Qwen Orani
<Bs Hird chirping
Figure 3: Audio preprocessing in existing models results in the loss of dual-channel information.
bottom right of Fig. [] This configuration serves
3.3 DATA CURATION PIPELINE
Our data curation pipeline integrates procedural synthesis with real-world data collection to ensure both comprehensive coverage and ecological validity. Fig. shows the distribution and statistics All audio for the foundational perception task is synthesized using precise parameterization or the Pyroomacoustics (Scheibler et al. 2018) physics-based simulator, providing complete control over acoustic parameters. Domain experts rigorously validate the task difficulty levels, which are then calibrated through human testing. For the holistic
of our
task, the curation process comprises four key stages (see Fig. 5)
1) Taxonomy Construction and Data Sourcing: We build hierarchical task taxonomy through collaborative process involving domain experts and the Gemini 2.5 Pro (Comanici et al., (205)). This framework guides the sourcing of candidate data from large-scale, real-world audio libraries: Clotho

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Statisties
Number
Total Questions
2,353
Frew Spulie
Singlesaranee
Abuclatz Pescepthas Ragge
Cartinass Priceses
Foundation Perception
Questions
961
hyl
Calegory
Stenaton
Reaasive
Attributes
Berrittttio
Teal Appllance Oponiun
Dysamic Traestery
Muult-erama wxerre Spertil
Temporal Reavoning
Senstrrayy
Duiy Surpes
Category
Spanial Reazoning
Questions
502
Foundation perception tasks
Temporal reasoning tasks
Spatial reasoning tasks
Category
Avg.
Audio Langeh
sec
(a) Data distribution across the foundation perception, temporal reasoning, and spatial reasoning three tasks.
(b) Statistics.
Figure 4: (a) The data distribution of STAR- R-RABCCH across three main tasks. (b) Data statistics of our benchmark, including the total number of questions for each task and their sub-categories, and the average audio length for reasoning tasks.
Basic Rules
spot
Frree- pulidtion
MLLMM
Taxonomy
Datn
Al-Assisted
Human Amnotation
Quality
Validation via
Construction
Sourcing
Autorated Fillering
Control
Humun Performance Evaluation
Figure 5: The four-stage data annotation pipeline for constructing our STAR-BENCH.
Drossos et al.
2019) and FSD50K (Fonseca et al. 2022) for temporal reasoning, and STARSS23
Shimada et al. 2023). along with audio sourced from the internet for spatial reasoning.
2) AI-Assisted Automated Filtering: This process employs an efficient three-stage funnel. First, discard unsuitable samples based on basic properties like duration and energy. Next, an LLM (e.g.. DeepSeek-V3 (Liu et al., (024a)) performs an initial screening based on textual metadata, providing justifications for its decisions. Finally, powerful multimodal model (e.g., Gemini 2.5 Pro (Comanici et al., 2025)) analyzes the audio, metadata, and the LLM's outputs. The final step yields judgment, a quality score, and preliminary classification, further filtering irrelevant samples. The detailed prompts used to query the LLMs are provided in Appendix B
3) Human Annotation and Quality Control:
We recruit and train 10 undergraduate annotators to label the data using professional platform. During this process, Al-generated information is provided as an auxiliary reference. To ensure high-quality labels, we implement stringent two-round review process: the first round involves inter-annotator cross-validation until consensus is reached, while the second consists of random spot-checks by three domain experts. More details are provided in Appendix B.3.2
4) Final Validation via Human Performance Evaluation: ensure all items in the benchmark are fair, unambiguous, and solvable by humans, implement final validation stage. In this phase, domain experts act as examinees and solve our tasks. Only items that are independently and correctly solved by at least two-thirds of the experts retained. Our rigorous protocol ensures that all problems in our benchmark are well-posed and reliably solvable by human experts.
4 EVALUATION
Benchmarking Models. Our evaluation covers 19 models (16 open-source and closed-source models). The open-source models span three categories: (1) Large Audio Language Models de- signed for universal audio-text understanding, including SALMONN (Tang et al. 2024). Qwen2- Audio Instruct (Chu et al.) 2024). Audio Flamingo 3 (Goel et al. 2025) with its "think" variant, DeSTA2.5-Aude (Lu et al. 2025), Kimi-Audio (KimiTeam et al. 2025), Step-Audio-
(Wu
et al. 2025), MidashengLM DDineel et al.) 2025), and Kiaomi-MiMo-Aus
(Xiaomi 2025) with
its "think' variant; (2) a specialized model for spatial audio, BAT (Zheng et al. 2024): and (3) Omni
Language Models with fully multimodal support, including Qwen-2.5-4 2.5-0mni (Xu et al. 2025). Phi4-
MM (Abouelenin et al.) 2025), Gemma-3ne Cemma-3n-E4B-i (Team et al. 2025). and Ming-Line Lite-Omni-l.55
AI et al. 2025). We also include three leading closed-source models: Gemini 2.5 Pro (Comanici
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Evaluation results of various models on STAR-BENCH. The best performance is high- lighted in bold, and the second-best ones are underlined. MA (Macro Accuracy) denotes the un- weighted mean of class-wise accuracies, while OA (Overall Accuracy) denotes the proportion of correctly answered instances. All reported values are AA (Average Accuracy across multiple runs) only; for ACR (All-Correct Rate), see Appendix D
Table 2:
Models
Foundationa Perception
Temporal Reasoning
Spotial Reasening
MA
Rango
Semoitivity
MA
Continuous
Diserete
OA
Loralization
Relation
Trajectory
Randon Guess
23.75
26.35
25.33
14,29
14,29
14.29
33.35
33.33
35.35
33.33
24.32
Human
79.42
74.55
75.60
90.12
$5.51
85.00
70000
$0.00
77.00
73.72
79.11
SALMONN 18
1:38
27.32
25.45
26.22
14.55
13.30
14.15
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et al. 2025) (updated June 2025), Gemini 2.5 Flash (updated June 2025), and -or----uud---rvviiieee AAciiam et al. 2023) (version 2025-06-03).
Robust Evaluation. All questions in ETAR-BENCIL are presented as multiple-choiee questions and evaluated using classification accuracy, with correctness determined via string matching of option labels or their full text. To ensure robustness, we evaluate each question multiple times under minor prompt perturbations, a strategy detailed in Appendix [ This approach yields two key metries: Average Accuracy (AA), the mean accuracy across all runs, and All-Correct Rate (ACR), the proportion of questions answered correctly model reliability. Due to space limitations, we primarily report AA in the main text, while complete experimental results are available in Appendix D
every run, which serves as stronger indicator of
4.1 MAIN RESULT ANALYSIS
We present a comprehensive evaluation on STAR- as shown in Tab. 2 Due to the space limit, detailed results on each task are provided in Appendix D Our key findings are as follows:
presents a considerable challenge for existing mod- els. Human evaluators achieve high accuracy across all task categories (e.g.. 75.6% perception, 88.0% on temporal, and 73.7% on spatial tasks), whereas all tested models fall well below this base- line. Most open-source models perform close random guessing, and even the best dlosed-source model. Gemini 2.5 Pro, reaches only 49.59% average accuracy. In addition, model predictions on STAR-BENCH exhibit low reliability, as evidenced by the pronounced gap between their Average Accuracy (AA) and All-Correct Rate (ACR) scores. A detailed discussion of this issue is provided in Appendix E. 1 Although the underlying audio data for the temporal tasks (e.g.. FSD50K, Clotho) is commonly used for model pre-training, our novel task formulation of temporal reasoning de- liberately departs from conventional audio QA formats. This design allows for a more thorough evaluation of the integrated capabilities of current models. Meanwhile, this design also serves diagnostic lens on the limitations of current training pipelines. The poor performance across models suggests that existing training paradigms often centered on clip-level tagging. QA, captioning
STAR-BENCH is Challenging
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over linguistically salient cues (e.g., using FSD50K for sound event recognition) and do not equip models with the abilities needed for audio 4D intelligence.
A Clear Performance Gap between Closed-Source and Open-Source Models On the founda- tional perception and temporal tasks, Gemini 2.5 Pro establishes a commanding lead among all models. On spatial tasks, however, nearly all models, both closed- and open-source, perform poorly. As indicated by the prior experiment (Fig. 3) this is likely because most models (except BAT) dis- card multi-channel information during preprocessing, thereby losing key acoustic cues needed for spatial reasoning. Among closed-source models, Gemini 2.5 Pro surpasses Gemini 2.5 Flash, sug- gesting that stronger reasoning capabilities deliver substantial gains. In contrast, open-source models show the opposite pattern: the "think" modes of Audio Flamingo 3 and Kiaomi-MiMooAuddi per- form worse than their no-thinking counterparts, implying that without sufficiently solid perceptual and knowledge foundations, reasoning can be ineffective or even detrimental.
4.2 DISCUSSION: WIIY DO EXISTING MODELS STRUGGLE ON
To better understand the underlying causes of the poor performance of existing models, we conduct detailed error analysis along with a series of ablation studies. Due to space limitation, the ablation study on spatial reasoning is provided in Appendix E.2
Error Analysis. We conduct manual error analysis on 200 failed predictions sampled equally from temporal and spatial tasks of three representative models (Gemini 2.5 Pro, GPT-4o-audio, and (ren-2.5-mmni) For temporal tasks, our analysis reveals clear capability hierarchy across the models. The open-source Qwen-2.5-Omni shows major deficiencies in all three core abilities: its perception is coarse-grained and unable to capture subtle inter-segment distinctions, and a sub- stantial knowledge gap (54%) leads to reasoning that often appears specious due to the absence of physical-world grounding. GPT-4o-audio demonstrates stronger knowledge, but still suffers from perceptual and reasoning limitations, along with low-level issues such misalignment between rea- soning and final answers. In contrast, Gemini 2.5 Pro excels in knowledge and reasoning, shifting its primary bottleneck to the more advanced challenge of fine-grained perception (84%). As shown in Fig. 7 Gemini 2.5 Pro is the only model to succeed by providing a remarkably detailed description of acoustic nuances. Our finding suggests that the advanced world knowledge is deeply embedded within detailed audio-text captioning. While open-source models largely remain at a coarse se- mantic level (e.g., sound event classification), our analysis highlights that enabling them generate fine-gran -rained acoustic descriptions is critical toward more robust reasoning. On the other hand, most models demonstrate lack of native spatial awareness in audio tasks, with weaknesses in perception, knowledge, and reasoning. Additionally, a prevalent type of error involves vision-centric hallucina- tions (e.g., ba.eb on the car's trajectory in the video..."). This may be attributable to the models' training on visual spatial tasks, leading them to misapply visual reasoning to auditory inputs.
Lack of Human-like Range and Sensitivity in Foundational Perception. To quantify the gap in perceptual range and sensitivity, we provide detailed visualizations of model performance our foundational perception tasks in Fig. g The first row of Fig. g presents audiograms that compare model coverage across the pitch- loudness space. Gemini 2.5 Pro achieves much broader coverage than the other two models, where greener regions indicate higher accuracy and the covered area reflects the perceptual range. In contrast, human listeners with normal hearing are expected to

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Published as a conference paper at ICLR 2026
capoze
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Figure 8: The range and sensitivity analysis in foun- Figure 9: The ablation study on temporal dational perception.
reasoning.
achieve near-full coverage, underscoring the gap between current models and human perceptual abilities terms of range. The second row Fig. further track the performance of both models and human subjects the three core acoustic attributes (pitch, loudness, and duration) difficulty decreases. The results reveal a stark performance gap between all models and the human baseline, particularly in the perception of fine-graie fine-grained loudness differences. A clear trend is visible even for the top-perfien pop-perforning Gemini 2.5 Pro: its accuracy, while competent on easier tasks, plummets perceptual granularity increases. This directly corroborates grained perception as its primary bottleneck. Notably, its performance on duration perception is an exception, showcasing temporal grounding capabilities superior to those of other models by accurately assessing audio segment lengths.
task
error analysis, identifying fine-
Ablation Study on Temporal Reasoning. To further pinpoint the specific limitations of temporal reasoning, we augment the baseline audio segment reordering task with two progressively easier settings: (1) + Global Caption, where single sentence describing the overall scene provided as contextual guide; and (2) + Uncut Audio, where the complete, unsegmented audio track is offered as a reference, reducing the task to straightforward process where the correct order can be determined simply by comparing and grounding each segment within the full audio. As shown in Fig, 9 Gemini 2.5 Pro's performance scales effectively with task simplification, culminating in near-perfect 99% accuracy in the Uncut Audio setting. In contrast, the open-source models show minimal to improvement across these settings. Their performance remains stagnant even when provided with the complete audio reference, despite the simplified nature of the task. This finding starkly exposes a core weakness in current open-source models: a fundamental inability to effectively compare, ground, and integrate information from multiple audio inputs.
CONCLUSION
We introduce STAR-BENCH -BNCHH, comprehensive benchmark for evaluating 4D audio intelligence over time and 3D space. We use rigorous human annotation, consensus review, and expert validation to ensure the high quality of data samples. STAR-BENCII establishes standardized tasks and protocols for studying 4D audio intelligence, offering actionable diagnostics for model developers. We expect STAR- Bench accelerate progress on advanced audio models and training with spatialized corpora, capabilities that are crucial for embodied agents.
ACKNOWLEDGMENT
This project is funded in part by Shanghai Artificial Intelligence Laboratory, Shanghai Innovation Institute, the Centre for Perceptual and Interactive Intelligence (CPII) Ltd under the Innovation and Technology Commission (ITC)'s InnoHK. Dahua Lin is a PI of CPI under the InnoHK.
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ETHICS STATEMENT
Our study primarily relies on datasets from open-source research communities and publiely avail- able online resources, as described in detail in the main text. These datasets do not involve private information, sensitive content, or material that could raise concerns related to safety, discrimina- tion, or harmful societal impact. All annotation and evaluation tasks were carried out by university volunteers who participated on voluntary basis. No human subjects were placed at risk, and no personally identifiable information was collected during the course of this research.
REPRODUCIBILITY STATEMENT
We provide a detailed description the construction process of our benchmark dataset and evalu- ation pipeline in the main text. To facilitate reproducibility, we will release the benchmark dataset as well as the evaluation code to the community. Clear documentation and step-by-step instructions are included to ensure that other researchers can replicate our experiments and verify the reported results.
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Beichen Zhang, Yuhong Liu, Xiaoyi Dong, Yuhang Zang. Pan Zhang, Haodong Duan, Yuhang Cao, Dahua Lin, and Jiaqi Wang. Booststep: Boosting mathematical capability large language models via improved single-step reasoning, 2025a.
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THE USE OF LARGE LANGUAGE MODELS
We used -emini--2.5-Pro to assist in expanding and consolidating the taxonomy of tasks in our benchmark. Both DeepSeek-V V3 and Gemini-2.5-Pro were utilized for the automated nee-serenning of candidate data. The final task definitions and data samples are verified by humans. We also used GPT-44 to generate some of the illustrative figures presented in the paper, and used GPT-5 polish the manuscript text. Only human-verified revisions are included in the final version.
A RELATED WORK
A.1
AUDIO LANGUAGE MODELS
With the advancements of large language models (LLMs) and multimodal language models (Yang
et al. 2025a
Jiang et al. 2024 Achiam et al. [023 Comanici et al. [2025] Cai et al. [2024] Touvron
et al.
2023: Liu et al.] 2024c 2025 Zhang et al. 2025b; QQ et al. 2025: Xing et al.; 2025b a
Ding et al. 2025 Wei et al. 2025a b Li et al.
2025; Zhang al. 2025a), recent research has
increasingly locused on integrating audio perception with LLMs to enhance audio understanding and reasoning. Existing methods can be broadly grouped into two categories: Large Audio Language Models(LALMM)) and Omni Language Models(OLMss)
Most LALMs combine a pre-trained audio encoder with an LLM backbone, where the two modal- ities are aligned via large-scale text-audio joint training. Notable models include LTU-AS (Gong eet al. 2023), SALMONN (Tang al., 2024), Midashengl.M (Dinkel et al.. (025), Audio Flamingo series (Ghosh et al. 20255 Goel et al. (2025), Qwen-Audio series (Chu et al. 2023 2024). Step-
Audio (Wu et al. (025) and Mimo-Audio (Xiaomi 2025). These models have achieved remarkable performance across wide range of audio understanding tasks, including automatic speech recog- nition(ASRR, spoken question answering(SpokennA)) and automated audio captioning(AAC)) In parallel, OLMs extend this paradigm to unify multimodal understanding with representative exam- ples such as Qwen-2.5-Omin (Xu et al. 2025), Ming-Ommn (AI et al. (2025) MiniCCMM (Yao et al.) 2024), Phi-4 (Abouelenin et al.) (025), GPT-4o (Achiam et al.) (023), and Gemini 2.5 (Comanici ctt al. 2025). Notably, they also achieve impressive performance on audio understanding and reason- ing. highlighting their potential to bridge multimodal perception and advanced audio intelligence.
A.2 AUDIO BENCHMARKS
Existing audio benchmarks illustrate the rapid progress of multimodal evaluation but also expose limitations. AudioBench (Wang et al., 2024) and AIR-Bench (Yang al. 2024) primarily focus on tasks such as automatic speech recognition (ASR). spoken question answering (SpokenQA). and audio captioning (AAC). These settings tend to reduce audio understanding to transcription or description, thereby neglecting the broader spectrum of acoustic reasoning. MMAU (Sakshi et al. 2025) and MMAR (Ma et al. 2025) further extend the evaluation scope. However, their results reveal an inherent weakness-L.Ie
equipped with audio captions can perform on par with advanced LALMs, suggesting that such benchmarks still probe little beyond language-level semanties.
Although some advanced benchmarks, such as MMAR (Ma et al. 2025) and MMAU-Pro (Kumar et al. 2025), do touch upon spatio-temporal aspects, their coverage remains limited in both scale and depth. For instance, their temporal analysis is typically reduced to identifying the timing or order of events occurring in the audio, while spatial analysis is often limited to localizing single sound source. In contrast, our benchmark systematically evaluates models' temporal and spatial deep reasoning capabilities within complex, real-worke physical contexts, requiring them to infer causal and dynamic relationships. Beyond audio benchmarks, multimodal benchmarks in video question answering (Cheng et al., 20255 Yang et al. (2025c) and embodied AI (Yang et al., (0255) have em- phasized temporal and spatial reasoning. However, these frameworks are predominantly grounded in the visual modality. where exploration of the audio modality remains comparatively limited. In real-world scenarios, audio understanding often depends integrating information across multiple sound streams and reasoning about subtle changes in intensity, phase, or frequency-capabilite that existing benchmarks scarcely capture.
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Table 3: Task examples of foundational acoustic perception.
Our benchmark aims to address these gaps by introducing tasks that require multi-audio input and cross-audio reasoning, such as comparing or integrating information across multiple sound inputs, as well as fine-grained spatio-temporal deep reasoning, such as tracking how acoustic patterns evolve with underlying physical changes. Rather than being limited to surface-level semantics, the benchmark is designed to assess whether models can leverage raw audio cues to perform physically grounded reasoning across spatial and temporal dimensions.
B DETAILS OF DATA ANNOTATION
In this section, we present the details of data annotation.
B.1 PROMPTS FOR AUDIO CAPTIONING
The prompt for Gemini 2.5 Pro audio captioning: "Please provide detailed description of the audio, including speech, music, environmental sounds, and any other noticeable elements. Be as specific as possible."
B.2 DETAIL INFORMATION FOR FOUNDATIONAL ACOUSTIC PERCEPTION
Tab. details the ranges and levels used for each acoustic attribute, alongside illustrative examples of our foundational acoustic perception tasks.
B.2.1 BINAURAL AUDIO SYNTHESIS
We generated binaural recordings for foundational perception tasks (azimuth, elevation, distance) in Pyroomacousties (Scheibler et al.) 2018) across three rectangular rooms-small (4.0x3.5x2.8 m), medium (8. (8.0x6.0x3 m), and large 15x machach with a frequency-indeeeeddnt wall absorp- tion coefficient of 0.25. Image-source reflections were modeled up order 10 at 44.1 kHz (matched to the HRTF sampling rate). For each room, we evaluated two listener positions (distinct Carte- sian coordinates) and oriented the head toward the +x axis. Binaural reception used co-located two-microphone array at the listener position with ear-specific directivity derived from a measured SOFA HRTF (MIT KEMAR, "normal pinna"; interpolation order 12, 1000 points), loaded via local SOFA reader and applied to the left/right channels.
For each condition (room x listener), sources were placed on sphere centered at the listener (radii 1-10 m; configurable azimuth/elevation). and ear-specific BRIRs were computed. Mono source signals were drawn from three curated audio clips ("alarm," "applause, "telephones"). downmixed if necessary. Rendering was performed by convolving each dry signal with the left/right BRIRs after an early/late mix to emphasize distance cues: we preserved the first 80 ms and attenuated the
1_pinnaa_n____ ninna.
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late tail by 0.5. We then applied global peak normalization across the batch to avoid clipping while preserving inter- inter-position level differences.
We discretized each attribute into fixed partitions control dataset balance.
Absolute azimuth: Eight angles {30", 60%, 120", 150", 210", 240", 300", 330") }. For each angle rendered all combinations of rooms 2 listener positions x 2 source clips, yielding 8 (3 x 2 x 2) = 96 utterances. Absolute elevation: Six angles --5", -45", 15 15: 45", 75%). Per angle we rendered 3 rooms 2 listener positions 2 source clips, for 6 (3x2x2)
72 utterances. Absolute distance: Radii from 1-10 m with a nonuniform allocation to emphasize near-field cues: for 1-7 m we generated 6 utterances per meter (42 total), and for 8-10
generated per meter
(9 total), giving 42 9 = 51 utterances per (room x listener) set.
Relative azimuth: Differences were multiples of 30": {30", 60%, 90", 120", 150", 180") (6 levels), totaling x 20 = 120 utterances. Relative elevation: Four difference angles {15', 90", 120%, } with 18, 17, 17, 12 utterances respectively (64 total). Relative distance: Four difference levels {1 - 2, 4- 5, 6 7, 8 -99 m with counts per level {12, 12, 12, 9}. totaling 45 utterances.
B.3 DETAILS THE CURATION PROCESS FOR REASONING TASKS
B.3.1 PROMPT USED FOR AI-ASSISTED FILTERING OF TEMPORAL TASK DATA
Fig. 10 and Fig. I present carefully designed prompts, which leverage Gemini 2.5 Pro to filter candidate data that meet the requirements of audio segment reordering. Briefly, we feed the audio, its metadata, and our task description, and ask Gemini 2.5 Pro decide, under strict criteria of strong sequence uniqueness, semantic clarity, and high logical universality, (i) whether the audio is suitable for reordering task, (ii) whether reflects continuous or discrete process, (iii) the reasoning behind its judgment, and (iv) quality score. We adopt a conservative filtering strategy, discarding only samples explicitly marked "not applicable". All remaining clips, along with the model's 's analysis, are then passed to professional annotators for verification and annotation. A prior LL.M-based filtering step follows similar procedure, but without audio input.
B.3.2 DETAILS OF HUMAN ANNOTATION AND QUALITY CONTROL
Following automated filtering, each candidate sample undergoes rigorous, multi-stage human notation and quality control process to ensure high data quality and annotation consistency. This process is follows:
(I) Systematic Training:
All annotators received detailed written guidelines and completed a trial annotation of 10 samples. These trials were meticulously reviewed by experts ensure unified understanding of the criteria.
(2) Inter-annotator Cross- :ross-validation:
(i) Initial Ammotation: A sample is first annotated by Annotator A. The annotation content includes:
Task compliance checks, segment boundary delineation, textual descriptions for sub-clips and the global audio, scene classification, and audio quality scoring.
For Temporal Reasoning:
For Spatial Reasoning: Selecting appropriate segments, task classification, and the generation question, the correct answer, and distractor options for the multiple-
choice format.
(ii) Review and Flagging: The annotated sample is then fully reviewed by Annotator B, who flags any inconsistencies with detailed comments and marks the sample as "failed".
(iii) Consensus through Negotiation: Annotators A and B then discuss all flagged issues to reach consensus and apply corrections. During the discussions, primary sources of ambiguity are as follows:
For Temporal Reasoning: (a) The reasonableness of the segmented clip boundaries. (b) The existence of multiple logically plausible orderings for the segmented clips. (c) Significant discrepancies in audio quality scores.(d) Adherence to formatting and content guidelines for the captions.
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For Spatial Reasoning: (a) Whether the spatial perception presented in the audio ambiguously aligns with the annotated answer. (b) Whether the constructed question- pair clearly necessitates the use of audio spatial cues for resolution. (c) Po- tential ambiguity in mapping the event name mentioned in the question to a specific sound in the audio. (d) The appropriate difficulty and plausibility of the distractor options.
(iv) Expert Arbivation: the cases where a consensus cannot be reached, the sample is escalated to expert panel for final decision. If the experts still cannot agree, the sample is discarded.
(3) Expert Spot-check: After passing cross-validation random 10% of samples undergo a final quality check by experts to ensure consistency and accuracy. Any discovered issues are then sent back for revision.
Role Setting:
You rigorous audio analysis expert, specializing identifying dynamic audio with explicit temporal legie conforming physical laws or strong causulity. Your task is to sereen suitable audio samples for high-standard "Audio Sequence Ordering Evaluation." Analysis should rely on the audio itself, with text as auxillary reference.
"Audio Clip Ordering" Evaluation
Qualifled audio segmented into three clips, shuffled,, and given the model, which reconstruct sequence using only sound. Candidate audio must meet the following strict standards:
1.Strong sequence uniquenes:: The events in the audio must present unique and clearly discernible temporal progression, with no possibility of alternative plausible orderings.
2.Semantic elarity: Events in the audio must be easily identifiable by sound alone.
3. Higigh logical universality: The event sequence should conform commonsense physical laws or strong causal relations, such that listeners from different backgrounds reach consistent understanding.
Note: Since the model
text, samples must be interpretable solely from sound.
# Audio Classification Standards:
1. Sortable Single Event:
Definition: Audio primarily represents a continuous event driven by single process, exhibiting significant and predictable temporal dynamics.
Possible categories include (but not limited to):
Spatial movement and distance variation. Physical processes in progression, Energy state decay. Blological activity dynamics.
The core judgment is whether the change governed by single continuous physical process, is significant, and commonly recognized as unambiguous.
Core judgment:
Examples:
The sound of pouring water. ball bouncing rest.
2. Sortable Multi-Event Eventt:
Definition: Audio contains two or more independent events, where the events exhibit a strong causal relationship such that "A inevitably leads B" or "A must precede B."
Core judgment: The core judgment is whether sub-events have distinet, separable acoustic features, follow a direct and widely recognized causal chain, together form concise, complete, unambiguous process.
Examples:
Opening bottle pouring water setting down the bottlc.
Cracking an - stirring - pouring into an oil pan.
Note: If event boundaries are unclear but the everall structure forms a continuous process, clussify as "Single Event." there are clearly distinct stages with evident logical links, classify as "Multi-Event..
#Special Exclusion Rules:
Filter out the following audio types, even they exhibit some "dynamic change." If they fail semantic clarity, sequence uniqueness, or logical universality, they must be labeled "Not Applicable."
Static or repetitive sounds.
Sounds with intensity variation without reflecting physical or typical action sequence.
Weak absent causality, dependent special background knowledge.
Ambiguous, subjective, interpretable multiple ways.
Amblent noise atmosphere shifts without structured events.
Dialogue or speech as main content.
Poor quality or indistinct segmentation.
Features High-Quality Samples:
Cleur semanties: Each action change is acousticully distinct and intuitively understandable.
Unambiguous sequenee: Temporal logie is indisputable, allowing only reconstruction order.
Narrativity and procesuualty: The audio presents a complete, coherent event chain consistent with human understanding and commonsense reasoning.
Figure 10: The prompt for our Al-assisted filtering process on temporal tasks.
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Input
You will recelve the following three components:
1.Text description: Labels, annotations, or metadata about audio.
2.P 2.Preliminary judgment based text: text-only hypothesis about audio's attributes. 3.Audio file.
Note: Always buse your classificution primarily the acoustic features of the audio itself, using text only supplementary context.
#Output Format (Strictly follow the four-line structure)
Classification label: Sortable Single Event Sortable Multi-Event Uncertain Not Applicable description: For "Sortable Multi-Event," the strong causal chain. For "Single Event," " describe the dynamic change. "Not Applicable", briefly summarize the content.
Event C. describe
Reasoning: Explain classification basis, focusing the three standards: semantic clarity, sequence uniqueness, and logical universality. Refer to specific acoustic features where possible.
Score: (Composite evaluation based on the three standards)
points: Events clear, sequence strong, logic robust, structure or narrative (24 events).
points: Sequence clear, physics/logic reasonable, but semantics somewhat limited (53 events).
-3 points: Some ambiguity, human listeners may also struggle to interpret
-2 points: Content vague, minimal sequential logic, not recommended for ordering.
point: Events unclear, structure absent, unusable for ordering tasks.
Examples:
Input:
Text description: "A ping-pong ball landing."
Preliminary judgment:
Audio file: Sharp bouncing sounds, gradually weakening, intervals shortening, finally stopping
Output:
Classification label: Sortable Single Event
Event description: Ping-pong ball bouncing, sound intensity
decay until stop. Reasoning: physical energy decny, unique order, limited semantics.
Score:
Input:
Text description: "People counting down for New Year, then celehrating with fireworks."
Preliminary judgment:
Audio file: 10-second countdown fireworks cheering
Output:
Classification label: Sortable Multi-Event
Event description: Countdown (10s) > Fireworks >> Cheering
Reasoning: Three distiinct events, unique order, clear logic, strong semantics.
Score:
Input:
Text description: "A segment of continuous machine rumble."
Preliminary judgment:
Audio file: Indistinct machine operating noise
Output:
Classification label: Applicable
Event description: Continuous
machine rumble without structural change.
Reasoning: Lacks identifiable dynamic events temporal structure, unsuitable for ordering evaluation.
Score:
Figure 11: The prompt for our Al-assisted filtering process temporal tasks.
C ROBUST EVALUATION
All questions in STAR-BENCH are presented as clear multiple-choice questions with well-formatted options. We adopt classification accuracy as the evaluation metric. To determine the correctness of response, we employ string matching to extract either the chosen option label (e.g. <A>) or the full text content of the option from the model's 's output.
Furthermore, we implement a robust evaluation strategy to ensure rigorous and reliable results. For perception and spatial tasks, we adopt the CircularEval method from MM-Bench (Liu et al. 2024bb) Specifically, each question is presented to the model N times (N is the number of options), with the option order cyclically rotated in each run to mitigate potential positional biases. For temporal tasks, conduct three runs per question with different temporal segment orders to evaluate the model's robustness to sequence variations. Note that due to the significant API costs, GPT-4o Audio was evaluated only once per question. This strategy yields two key metrics: Average Accuracy (AA), the
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mean accuracy across all evaluation runs, and All-Correct Rate (ACR), the proportion of questions answered correctly in every single run, which serves as a stronger indicator of model reliability.
For models that do not support multi-audio input (only Audio Flamingo 3 and its Think variant among the models we evaluated), we concatenate the audios with a 2-second silence and specify this in the prompt. In contrast, for models that support multiple audio inputs, feed them sequentially with textual indices.
To establish human performance baseline, conduct human evaluation randomly sam- pled subset of approximately 10% of the data from each task. This evaluation is performed by 10 university students, from whom we explicitly exclude anyone involved in data annotation with domain-specific expertise, thereby ensuring a general, non-expert perspective.
D BREAKDOWN RESULTS
In this section, we present detailed results for perception, temporal reasoning, and spatial reasoning on
as shown Tab. Tab. 5 and Tab, 6
E FURTHER ANALYSIS AND DISCUSSION
E. HIGH OUTPUT INSTABILITY AND CONCENTRATED PREDICTIONS
The reliability of model outputs our benchmark is notably low, as evidenced by the stark contrast between their Average Accuracy (AA) and All-Correct-Ranl (ACR) scores. Even the top-perforning model, Gemini 2.5 Pro, exhibits average drop of 25.01 percentage points from its AA to its ACR. This issue even more pronounced for the majority open-source models, which record an ACR near zero. This score indicates complete failure maintain consistent predictions under minor input perturbations. For these models, the instability often manifests as a tendency to concentrate predictions specific option, suggesting reliance superficial biases rather than genuine derstanding.
E.2 ABLATION STUDY ON SPATIAL REASONING.
As shown in Tab. 6 the results reveal a fundamental limitation of LALMS' in spatial perception. The native input inherently discards part of the multi-channel information during model preprocessing, which leads to significant loss spatial cues that are essential for fine-grained reasoning. On the other band, the channel-wise input explicitly presents each channel with textual instructions, mitigating some of the information loss. Despite this, most existing models are not trained to handle multi-audio inputs. As result, they consistently struggle align channel representations and fail to make reliable use of interaural differences. Overall, the pronounced gap between human and model performance highlights that spatial reasoning in audio remains unsolved challenge, underscoring the need for audio encoders that natively support multi-channel audio input.
F CASE STUDY
In this section, we present several case studies of error analysis, including temporal reasoning (Figs. to [7] and spatial reasoning (Fig. (8).
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Table 4: Results for the foundational perception task. Each cell reports AA/ ACR: Average Accu- racy (AA; overall accuracy across all runs) / All-Correct Rate (ACR; proportion of samples that are correct on every run). The best model in each category is shown in bold, and the second best is underlined.
Model
Size
Alsaltto
Helatie Eltriiittation Senethrly
Ms (S)
Runders Caans
75.00-3:1 10
22:00/1200 17
25,9113 X0
25.01 11:34
25.0011333 34
75:00:1135 36
35 30
71:31 13
25 10:01:3 76
24, 11:76
$1.56
$2.35
73.65
SALMONS
178
14.34-1300
25:3-00
16.20 (18)
35-33/100 (13/0
13.0411000
25 25:0011:00
38.54/0100 10
31.30/11 330
34.15/1000
12:3 (11/3
26.33/100
Andio Harnang taek
$1.35 1.50
135/10000
13.33
835 10
45.63 22500
09.38 160
61.11 4:31
1320-13.0 550
18.36 11.30
3156-1.33
Qveeb inooec
848
35300 40
22.50/0100 10
0866
13:75 11//0000
35531000 10.00
14:55 10:00
26:4-111 00
15000/1000
2181-1
23.88/0000
27 27.54:235
B4T
76
0000/003
2004 26.34
41.63
23.50 23.53
0001
000 0000
0.00 (15)
57.50155
009 0000
000/1100
12.77 12:57
Kind Andio
10
16.19
18.13
141770333
36.72 1350
35.00133
M
78
4035
25.3755.37
2800-13
45.25
4000)
12.00 13
23:5-7355
14.72
27
42.22/0.11
11.24/ 18:35
Gonana Ine4---- E48-4
7:58
15.00 0100
11.03
36.15
4556 1.66
23.13-13
25. 10.30
22 22.4311.15 1.5
Ming 1.
13.05
1607
43.07
13..1
01
10
18.50 51
06.96
16.17
05.00
17.30
40-46111
33 11:08-13 50
77 1.67
17 13:03/233
4301 14.09
1523/1155
22.7111:0
16-554470
33.96/0005
70
$5.20
53. 16:10-33
93.33
11.53/332
Garan 25 Thah
62.94/1911
12.50-10:00
18.6/0055
48.50/23.0
43.3
60.13/20.51
33,86/
9.17
25 13.
2432/440
Gonan: 25 the
8677 F2234
25 25
3.85 10.00
5118
15:35
63.58 52.50
55755
1533
7656 06.35
07.08
13.75
29 23.24-12 5.35
64.46
46/4
Table 5: Results for the temporal reasoning task. Each cell reports ACR: Average Accuracy (AA; overall accuracy across all runs) All-Correct Rate (ACR; proportion of samples that are correct on every run). The best model in each category is shown in bold, and the second best is underlined.
Madd
Size
Continuan Procesees
Discrete Event Seguences
OA (S)
Objjctt Sporial
Applioare Operation
Serppic
Torre-Tllgyred Conagganncss
Ranbon Cuess
12:000:00
1220.00 /0.00
14:99/0000
14.25/000
14.29
1420/0.00
Plannin
91.11
$5.89|
47 88
63.35
83 55
15.00
MONN
115
12/00.00
15.56
11
12.90 0//00/00
14.1//080
Andio Fhriings
6:10
31 3846/70.00
10.00 (1-41-7
366 11.00 96
2.22/1 111 11
8 8.53
8.57 /0.00
Anaii Fhentogo ttan
8:11
14:17 10/00
15/6//1.47
1146/0/00
DeSTAZ Andin
K30
18:88 10
95 10
19-55/1477
13.900055
1146/000
16 1MM00000
BAT
70
10000000
0/0 10:00
0100/100
0100 11.00
010/1111
0000/0000
Phi-MM
5,00
1322
16.4/0045
1100
3683/3/33
1535/01.00
1:25
2138/000
16.30
Saep- Ser---ete---mr 2-mii
70
18.13
18 18.22/1000
12 (1000
10.00/000
INA3/1000
15
17.51 1251/000
Lat 1
18.08
16.57
Qnce 2.5 Oveii
7B
17.00
0335/10 (0.55
19 .47
16 13.50
11:46-000
16 75
Xacosi MiMe-Aali
78
15.2 10.00
17:16-0055
20.99 2.52
2604 73.15
18 50
MMMo-Aall-Blld
MicCPPM---- 0-16
17.46/0.45
14.55/0..00
4PT Aadio
15.61
16.23
24
22.78
25.00
20,561335
35.07
30,00
Camini Pro
10
4372:
35.55
40.00
(10.50
Table 6: Results for the spatial reasoning task using native and channel-wise audio input. Each cell reports AA AA/AO ACR: Average Accuracy (AA; overall accuracy across all runs) / All-Correct Rate (ACR; proportion of samples that are correct on every run). The best model in each category is shown in bold, and the second best is underlined.
Sugle-Suucee Stutic Lecaliution
Mulii Souvee Spatial Relalisn
Dyuanic Trajecteyy Tricking
OA (S)
Native Inpur
Channelnie Input
Native Input
Channdowiee Ingut
Native Inper
Chaunel-viee Teput
Natire Inpst
Channei-wior Iaput
Recako Giacaa
7000
77.00
SALMONK
13B
26:15 15 33 18
25:62/ 3.15
25.61 5.42
33331
35594 0094
38 (L.56
29.02 2.10
28/55 /3,05
Plansago
9.455
2.12
38.01
44.35
tisk
5606
37.11 19.51
Qweed Andi botucc
3.45
21 32
0.36 1.77
24.86 3355
12 4.42
1509-004
11.06 2.55
20.78 5.76
8.6/2.50
Dx51 Audio
27 2.53
20.35 4.59
3481 19.33
41.30/ 19.47
$2.76 38
3208 21.20
20.15 231151595
20.5:11.55
BxI
0200
0.00
XM
33
29.211355
52.01 10.50
32000 (0400
Kari Nadio
18
27 06 3.55
15.49 3.53
35.04 15.04
22.42
4403 7.55
40.25 (2.512.40
23 (6.00
22.54/55 5.77
MiDulergiMM
18
4311
37.22/
63 23
42.77
23 30
45 21.20
46.29 20032
40 24 18.53
Step- Sap--aiie22- simi
31
1.58
25
15.04
29.75
32.34 16397
Ming Lic O
15:98
10.30
34.53 5.03
35.10 10.75
33 04
56
52
39.04 20.75
37 15 11.70
35.39 35.39/5.36
Owce:2 Ouzii
18
56.9/ 15
41.00/19.00 19.55
15.0011550 15.55
27 06 12.00
50
37.25 11555
05
MMMa-
Xuowii Aidootidetttikk
25.4
2.55
17.99/3.57
37.12 13.00
26.55 (4.1
18
20 02/0//3
3 18
37.52/2.0
43.36/11.50
39.53 12.39
36.56 26
/
34.73 1300
32.14 15.17
Geniri Faac
4679-2447
45/07 (10.0
46.5/11377
06 16.5/0.7
Contri 2 Poo
40447/11045
9-98/1100
48.0 75.00
15236/71115
45.88 15
45.50 04.0011.00
1077/17775
22

---

Published as a conference paper at ICLR 2026
[Brushing of teeth vigorously and then turning on the sink water:]
teeth brushing spit foam
the tap
spit
seg
seg 2]
seg
Q:
clip1:
[seg 2]
clip2: [seg_3 3] clip3: [seg_]
Answer: <E>: clip 3 - clip 1 - clip
Gemini 2.5 Pro
The most natural chronological order these sounds is <A>. Here's breakdown of the events:
Clip 1: The sound of solid objects, most likely ice cubes, being dropped into container like blender pitcher.
Clip 2: The sound of liquid being poured, which would typically follow adding the solid ingredients.
Clip 3: The sound of a blender running, which is the final step to mix the icc and the liquid together.
Therefore, the most logical sequence dropping in the ice (clip 1), pouring the liquid (clip 2), and then blending (clip 3).
Perception Error Reasoning Error The core error fundamental scene misidentification: the audio depicts teeth-brushing routine, making blended drink. All subsequent step-by-step analysis is therefore invalid.
Error analysis
GPT-40 Audio
Let's break down the sounds:
Clip sounds like spitting or rinsing into sink.
Clip sounds like water from a faucet, which typically happens before or after brushing. Clip sounds like someone brushing their teeth.
In natural choreography of events, you might first turn the water to wet the toothbrush rinse your mouth, then proceed to brushing your teeth, and finally, rinse or spit after brushing.
That would make the most natural order: Clip 2 (water on) -> Clip 3 (brushing) -> Clip (rinsing/spitting). So the answer is D: clip clip 3 -> clip 1.
Reasoning Error Perception Error The failure to isolate the spitting sound from the water noise led to incorrect event order.
Error analysis
Qwen-2.5 2r5en-2.5-Omii
To determine the most natural chronological order of these sounds, let's analyze each clip: Clip 1: A door being opened and closed. This typically happens the beginning of sequence of activities.
2. Clip 2: A toilet is being flushed. This usually follows the of the bathroom.
3. Clip 3: Someone is brushing their teeth. This is common activity that often happens
after using the bathroom.
Given this sequence, the most natural chronological order would be: Open the door (Clip 1)- Use the bathroom (Clip 2)- Brush teeth (Clip 3)
Therefore, the correct answer is: <A>; clip
clip clip
Perception Error Knowledge Gap Qwen-2.5-Q 5-5nns''ss perception is confined to individual clips, which prevents it from inferring sound events within the broader scene context. Consequently. seg and seg remain unassociated with seg causing the model to engage flawed spurious reasoning.
Error analysis
Figure 12: An error case from the temporal reasoning task.
23

---

Published as a conference paper at ICLR 2026
[A man fastens his seatbelt, then starts up the car.
pull the seatbelt
insert the buckle
get the keys
start the engine
[seg
ssgg 2]
[seg 3]
Q: clip1:
[seg_3] 3] clip2: |seg 1] clip3: [seg_2]
Answer: <D>: clip 2-xllpp 3 3-clip
Gemini 2.5 Pro
The most natural chronological order for these sounds is <F>, Here's breakdown of the events:
1. Clip 3: This is the sound of a car door closing, This is one of the first actions taken after getting into a car.
2. Clip 2: This the sound of a person settling into the car seat. You can hear clothes rustling and the seat creaking as they get comfortable. This typically happens after the door closed.
Clip 1: This the sound of keys being put into the ignition, followed by the car engine starting. This is the final step the sequence before driving away. Therefore, the most logical real-world sequence is closing the door (Clip 3), settling into the seat (Clip 2), and then starting the car (Clip 1).
Error analysis Perception Error The first half of seg is the sound of pulling the
seatbelt, and the second half is the sound of inserting the buckle.
GPT-40 Audio
X
Let me break down the sounds.
Clip 2 sounds like a zipper, maybe someone zipping up a bag or coat.
Clip 3 is the sound of a car engine starting, along with some mechanical sounds.
Clip sounds like a door closing.
natural sequence, you would expect someone to zip up their bag or jacket, then close a door after stepping out or in, and then finally start the car.
So the most natural chronological order would be: clip 2 (zipping) -> clip (door closing)
-> clip 3 (car starting).
That aligns with option F.
Perception Error Reasoning-Answer Misalignment Error analysis
<
should be
clip clip1 - clip2
Qwen- 2.5--m22
To determine the most natural chronological order of the sounds, lct's analyze each clip. Clip This clip contains the sound of car engine starting. This is typically the first action sequence of events when someone is about to drive.
Clip This clip contains the sound of seatbelt being fastened. This action usually happens after the car engine has started, as the driver passenger needs secure themselves before driving.
3. Clip 3: This clip contains the sound of a car door opening. This action usually happens before the car engine starts, as the driver or passenger needs to enter the car before starting the engine.
Given this analysis, the most natural chronological order is: Clip 3: Car door opening Clip 1: Car engine starting- Clip 2: Seatbelt being fastened Therefore, the correct answer Es:<E>; clip 3 -> clip ->> clip 2
Error analysis Perception Error Seg 3 is the sound of inserting the seatbelt buckle.
Figure 13: An error case from the temporal reasoning task.
24

---

Published as a conference paper at ICLR 2026
[Dial, connect,call]
dial the number
ringing tone
ringing tone
say hello
seg
seg 2]
[seg 3]
Q: clipl:
[seg 2]
clip2: [seg_ 3]
clip3: [seg
Answer: <E>: clip 3 3-clip 1-clip 2
Gemini 2.5 Pro
Pro
The most natural chronological order for these sounds is:
1. Clip 3: The sound of a phone number being dialed on a touch-tone keypad. This is the first action taken to initiate a call.
2. Clip 1: The sound of a phone ringing (the ringback tone). This is what the caller hears after dialing and while waiting for the recipient to pick up.
Clip 2: The sound of someone answering the phone with "Hello?"" This is the final step the sequenee, where the call is connected.
Therefore, the correct order is clip 3 -> clip clip 2. The correct option is <E>
Error analysis
correct
GPT-40 Audio
X
The most natural chronological order for these sounds would typically be:
<A>: clip -> clip -> clip 3
This because clip sounds like phone ringing, clip has someone answering the phone with "hello," " and elip is busy signal, which can occur after call ended or fails to connect.
Perception Error Seg_3 3 is the sound of pressing numbers, not busy signal.
Error analysis
Qwen-2.5-0mni
The most natural chronological order in which these sounds would typically occur in the real world is:
<A>; clip 1> clip -> clip
Explanation:
1. The first sound (clip 1) is telephone ringing, indicating that someone is calling.
2. The second sound (clip 2) is person answering the phone, saying "hello."
3. The third sound (clip 3) is a dial tone, which typically occurs after the call is connected and the call is being made.
Error analysis Reasoning Error Sog 3's explanation is problematic: the dial tone
occurs before the call is connected, not after.
Figure 14: An error case from the temporal reasoning task.
25

---

Published as a conference paper at ICLR 2026
[An ambulance approached and then moved away.]
seg
Q: clip1: [seg_ 3] clip2: [seg_ 1] clip3: [seg_2] 2] Answer: <D>; clip 2 2-clip 3 3-clip 1
Gemini 2.5 Pro
The most natural chronological order for these sound events <D>; clip clip clip
Here is breakdown of the events they would likely occur:
Clip 2: This clip captures the sound of emergency siren that is clearly approaching, followed by the abrupt, jarring sound of collision or a car horn blaring, signifying crash. This is the inciting incident.
2. Clip 3: This clip features a loud, steady siren. This represents the immediate aftermath of the crash, with the emergency vehicle now at the scene, its siren still active.
Clip I: This clip features the sound of a siren fading into the distance (the Doppler effect is noticeable as the pitch drops). The sound of the damaged car's homn continues blare but also seems to be part of more distant, ambient soundscape. This suggests time has passed, and the emergency vehicle now leaving the scene.
Error analysis correct
GPT-40 Audio
All thrce clips sound like sirens approaching, but there's noticcable difference in pitch and intensity that suggests changing distance. It's common for sirens to start from far away and then approach closer, raising the volume and changing the pitch. From what heard:
One clip likely represents more distant or quieter siren. Another one sounds like it's getting closer. The final one should be the closest and the loudest.
Putting that together in a natural order, the most logical sequence events in real life would the siren starting chronological order would be where the siren initially distant, moves closer, and finally the closest. Looking at the pitch and dynamics of the sirens: Clip sounds furthest away, clip seems closer than clip 2 but not the closest, and clip 3 seems the closest.
away and gradually approaching closer.Hence, the best
So, the most natural chronological order would be clip -> clip 1 -> clip T.That corresponds to option C.
Error analysis Perception Error Seg_3 decreases in volume.
Qwen 2.5--22.--mni
X
To determine the most natural chronological order of the sounds in the audio clips, let's analyze each clip:
Clip 1: An emergency vehicle siren is heard, indicating a situation requiring immediate attention.
Clip 2: Another emergency vehicle siren is heard, suggesting a second emergency
vehicle is approaching responding.
Clip 3: A third emergency vehicle siren is heard, indicating third emergency vehicle is also responding.
Given this sequence, the most natural chronological order would be: First, the first emergency vehicle arrives (Clip 1), Then, the second emergency vehicle arrives (Clip 2). Finally, the third emergency vehicle arrives (Clip 3). Therefore, the correct answer is: <A>: clip > clip 2 3
Knowledge Gap The descriptions disregard fundamental audio
Error analysis characteristics, making no attempt leverage pitch or volume variations infer movement, thereby rendering the sequence meaningless.
Figure 15: An error case from the temporal reasoning task.
26

---

Published as a conference paper at ICLR 2026
[A person opens jar, quickly gulps the water and then closes the jar.
unscrew the cap
drink
put the cap screw on the cap
[seg 1]
[seg
[seg 3]
Q: clipl: [seg_]] 1] clip2: |seg 3] clip3: [seg_2] 2]
Answer: <B>: clip 1->clip 3 c-clip 2
Gemini 2.5 Pro
The most natural chronological order for these sounds is <F> clip 1> clip clip 3.
Here is breakdown of the events in that order:
Clip 1: This clip features the sound of a container, possibly with powder solid inside, being shaken. This is followed by the sound of the container being opened (like cap being twisted off). This is common preparatory step, like shaking bottle of drink mix before opening it.
2. Clip 2: This clip begins with the distinct sound of liquid being poured from bottle. This would logically follow opening the container in clip 3. as one would pour a liquid (like water) into the mix. The clip also contains a spraying sound, but the pouring is the key transitional sound from clip 3.
Clip This clip
sound of someone drinking and swallowing the liquid. This is
the final action consumption after the drink been prepared.
Error analysis Perception Error Seg 3 the sound of closing the lid, not pouring.
GPT-40 Audio
X
Let's break down sounds logically.
Clip sounds like cubes being placed into glass. Clip sounds like liquid being poured into that glass. Clip 3 sounds like someone sipping drinking from the glass. In natural sequence of events, you would first put ice in the glass, then pour the liquid, and finally drink from it.
Therefore, the most natural chronological order is: clip (ice first), clip 2 (liquid poured next), and clip 3 (drinking the drink last).
That corresponds to option A: clip 1 - clip 2 > clip 3.
Error analysis Perception Error Descriptions of both seg and seg 3 are incorrect.
To determine the most natural chronological order of the sounds, let's analyze each clip: 1. Clip 1: Sounds like someone is stirring or mixing something in container, possibly glass or a cup.
Clip 2: Similar to Clip I, it also sounds like stirring or mixing, but the sound is slightly different, possibly indicating a different type of container or a different substance.
Clip 3: Sounds like somcone is drinking from a glass or a cup.
Given these observations, the most natural sequence would be:
Stirring or mixing (Clip 1)
Stirring or mixing (Clip 2)
3. Drinking (Clip 3)
Therefore, the most natural chronological order is: <A>; clip 1 -> clip 2 -> clip
Reasoning Error Simply placing the two mixing segments together lacks rational explamation.
Error analysis
Figure 16: An error case from the temporal reasoning task.
27

---

Published as a conference paper at ICLR 2026
[Water is poured into glass.]
[sog
[seg
[seg 3]
Q: clipl: [seg_3] 3] clip2: [seg 2] clip3: [seg_]] 1]
Answer: <E>: clip 3 3-clip 2-clip
Gemini 2.5 Pro
Based on the sounds in the audio clips, the most natural chronological order is:
Clip 2: This is the sound of carbonated beverage being poured vigorously into glass, characterized by the "glugging" sound air replaces the liquid in the bottle. This represents the main part of the pour.
2. Clip 1: This sounds like the continuation
finishing of the pour, perhaps "topping off"
the glass after the initial foam has settled. The sound is less vigorous than in clip 2.
3. Clip 3: This is the distinct sound of someone taking large gulp or swallowing the drink that has just been poured.
Therefore, the logical sequence of events is pouring the drink, finishing the pour, and then drinking it. The correct option is <C>
Error analysis Perception Error Seg is not the sound of gulping, but the crisp sound of water being poured into glass.
GPT-4o Audio
"The most natural chronological order in which these sounds would typically occur is: <>>: clip (blender sound) -> clip 3 (pouring liquid) -> clip 2 (sipping)
Perception Error All segments are sounds of pouring water; the perception of both seg_3 3 and seg 2 is entirely incorrect.
Error analysis
Qwen-2.5-Omni
To determine the most natural chronological order of these sound events, let's analyze each
clip:
Clip 1: Liquid being poured into container.
Clip 2: Liquid being poured into container.
Clip 3: Liquid being poured into container.
Given that all three clips involve the same action (pouring liquid into container), the most natural chronological order would be based on the sequence of events typically observed in real-world scenario.
If we assume that the containers are being filled in sequential manner, the most logical order would bc:
1. First container is filled.
2. Second container is filled.
Third container filled.
Therefore, most natural chronological order <A>: clip 1 -> clip 2>> clip
1>
Perception Error Reasoning Error It cannot perceive differences Error analysis between segments, and can only output coarse-grained captions, leading to an inability to reason correctly.
Figure 17: An error case from the temporal reasoning task.
28

---

Published as a conference paper at ICLR 2026
Q: What is the movement trajectory of the racing car?
Options: <A> From left to right <B> From right left <C Remains unchanged
Answer: <>>:
From right to left
native input
channel-wise
Gemini Pro
The correct answer is <A>: From left to right. Here's breakdown of why:
Based on the audio, the sound of the race car starts in the left channel, moves across the stereo field, and then fades in the right channel. This indicates the car is moving from left right past the recording point.
I. Audio (Left Ear): The sound of the race car starts, becomes very loud, and then fades away.
2. Audio 2 (Right Ear): The sound is initially quieter than the left It becomes loud as the car passes the center point and then remains louder than the left ear as the car moves away.
Therefore, the correct answer is <A>: From left to right.
Error analysis
By comparing the two channels, we can hear that the sound is first prominent in the left car (Audio 1) and then becomes prominent in right ear (Audio 2). This change volume and timing between the ears creates the auditory illusion of the traveling from the listener's left their right.
native: Perception Error the channels interfere with each other, causing the model's basic perception to fail channel-wise:
Error Knowledge Gap It indicates that the model lacks knowledge for processing spatial audio tasks, relying only on volume perceive spatial audio, which leads to incorrect answers.
Perception
Based on the binaural cues in the audio recording, the correct answer is B. From right to left.
ITD, ILD
GPT-40 Audio
Knowledge
The racing trajectory of a race car typically involves taking the optimal racing line through each corner maintain speed and minimize lap time. It often involves starting wide approach, cutting close to the apex of the turn, and then moving wide again the exit, to achieve the smoothest and fastest path. So. the trajectory changes dynamically depending on the turns and straights of the track.
Based the audio, the sound of the race moves from the left channel the right channel, which means the from the left to the right side. The correct option is <A>; From left to right.
is moving
the
Error analysis
Hallucination Knowledge Gap Perception Error
shown that the model lacks knowledge about spatial audio tasks, making unable carry out the correct reasoning process, which in turn leads to hallucinated outputs.
Swen-2.5-Ommn
The audio does not provide information about the direction of the race car's movement, so it is impossible determine the racing trajectory. Therefore, the correct answer is C: Remains unchanged.
The racing trajectory of race would be <>>; From left to right.
Error analysis
Perception Error Generation Collapse Hallucination
It indicates that during the binaural audio processing, the channels interfere with each other, causing the model's 's basic perception to fail and resulting in hallucinated outputs.
Figure 18: An error case from the spatial reasoning task.
29


Reviews:
[{"review_id": "GJroJbxknz", "reviewer": "Reviewer_M1pg", "summary": "A very topical benchmark for large audio language models, which adds basic audio perception modalities, and explores categories where linguistic descriptions are hard.", "questions": "How would you generalize the presentation of spatial audio to models, beyond Kemar? How would you accommodate ambisonics? It would have been good to provide some example sounds.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "A clear way of generating spatial audio and temporal reasoning benchmarks is described and implemented. The results show clear lack of perception of current LALMs on tasks requiring these skills. This is a valuable and distinct addition to the plethora of benchmarks coming out", "weaknesses": "The audio scenes were generated binaurally, but this restricts the range of applications of the benchmark to perhaps humanoid listeners. Also, the HRTF used was a Kemar one, which is somewhat limited; and the scene rendering was also a bit limited. Nonetheless a good beginning. The models are of course set up to fail in this testing.\n\nThe spatial audio tasks are relatively hopeless, and analysis of the models was not too much possible To an extent, I would have liked more detailed analysis of the temporal reasoning tasks. An ablation I would like to see is if the models perform better if the  with just single channel presentation of these events to the models.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "JfygIh8JHb", "reviewer": "Reviewer_5scV", "summary": "The paper introduces STAR-Bench which measures Audio 4D Intelligence (reasoning over sound dynamics in time and 3D space). They combine a Foundational Acoustic Perception setting with a Holistic Spatio-Temporal Reasoning setting that includes segment reordering for continuous and discrete processes and spatial tasks spanning static localization, multi-source relations, and dynamic trajectories. Unlike prior benchmarks where caption-only answering reduces accuracy slightly, STAR-Bench induces far larger drops (-31.5% temporal, -35.2% spatial), evidencing its focus on linguistically hard-to-describe cues. They also provide a comprehensive evaluation of 19 LALMs/OLMs.", "questions": "- Do you think gemini 2.5 pro captioning stage filters the data points to be more biased towards the current capabilities of the models? \n- How easy/difficult is it to hill climb on this benchmark for audio models?\n- Do the baselines actually support spatial audio?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "- Introduces and benchmarks multi-audio segment reordering and stereo spacial reasoning which has been ignored by the previous benchmarks\n- Proper coverage of non-spatial attributes (Loudness, Pitch, Duration) and spatial attributes (Azimuth, Elevation, Distance)\n- Detailed error analysis on why models dont do well on the proposed benchmark\n- Thorough reporting of AA and ACR metrics to test the model's reliability\n- Really like the finding \"a fundamental inability to effectively compare, ground, and integrate information from multiple audio inputs\"", "weaknesses": "- missing details of AI-Assisted Automated Filtering. What exactly are we filtering using gemini 2.5 pro\n- Spatial data is synthetic and does not represent real world use cases.\n- Fig 8 not readable\n- Not sure if the baselines support spatial audio. Analysis on those models would not provide beneficial information\n- Support for spatial audio in these models can lead to huge jumps in model performance on the benchmark which questions the difficulty of the benchmark", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "0ucWAv0Z14", "reviewer": "Reviewer_LG4E", "summary": "This paper introduces STAR-Bench, a benchmark to evaluate 4D Audio Intelligence - reasoning over time (1D) and 3D space—via two levels: (a) Foundational Acoustic Perception (six attributes: pitch, loudness, duration, azimuth, elevation, distance; tested in absolute and relative regimes) and (b) Holistic Spatio-Temporal Reasoning (temporal segment re-ordering for continuous/discrete processes; spatial reasoning over static localization, multi-source relations, and dynamic trajectories). The dataset has 2,353 questions and is built through a four-stage pipeline (AI-assisted filtering, human annotation, expert validation) drawing on synthetic audio and real-world corpora (e.g., FSD50K, Clotho, STARSS23). Evaluation is multiple-choice with “robust” perturbations (AA/ACR). Results show humans outperform all models (e.g., ~88% temporal), while the best model (Gemini-2.5 Pro) reaches ~49.6% MA, and a larger drop occurs when answering from captions only, supporting the claim that STAR-Bench targets linguistically hard cues.", "questions": "See the weakness section.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- Clear problem framing ('audio 4D intelligence) and a structured task design that disaggregates perception, temporal reasoning, and spatial reasoning.\n- Useful diagnostic split: absolute vs. relative perceptual tests; temporal re-ordering; spatial subtasks (single-source localization, multi-source relations, dynamic trajectories).\n- Strong curation pipeline with AI filtering, human annotation, and expert validation; explicit use of public datasets + simulated audio for coverage and control. \n- Robustness intent (AA vs. ACR; circular option shuffling; segment order perturbations) is a step forward relative to one-shot MCQ protocols. \n- Empirical evidence that 'caption-only' shortcuts collapse on STAR-Bench more than on MMAU/MMAR, supporting the target of non-linguistic cues. (Claim supported in text/figures.)", "weaknesses": "- Everything is framed as multiple-choice with string-match grading. No open-ended QAs, while they are more real world.\n- A large portion of audio comes from widely used corpora (FSD50K, Clotho, STARSS23); many models likely pretrained on them. The authors argue the task formulation is novel (re-ordering, spatial relations), but clip-level memorization of events/timbres is still possible.\n- The paper doesn't quantify inter-rater agreement, item rejection rates, or ambiguity sources.\n- In table 1, the authors claim that previous benchmarks like MMAR and MMAU-Pro, do not have spatial reasoning questions, which is wrong. MMAR also has multi-audio questions.\n- MMAU, MMAU-Pro and MMAR also contains Temporal Reasoning questions, what does \"Deep\" signify in the task name in table 1?\n- With the recent advancements in Large Audio Language Models where the models are capable of reasoning over all three modalities of audio - sound, speech and music, this benchmark only focuses on sounds which is limiting. Although the authors explicitly frame STAR-Bench as a move away from traditional ASR/captioning style evaluations toward 4D (spatio-temporal) acoustic reasonin - another signal that speech/music tasks per se are out of scope, I strongly feel there can be multiple tasks framed around speech in spatio-temporal setting.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "809XsW6PwD", "reviewer": "Reviewer_7C2V", "summary": "This paper introduces STAR-BENCH, a new benchmark designed to evaluate what the authors term \"audio 4D intelligence\", the ability to reason about sound dynamics in 3D space and time. The authors argue that existing audio benchmarks primarily test for semantic content that is easily described by text captions, a claim they support with a \"caption-only\" evaluation experiment where model performance drops only slightly. STAR-BENCH is proposed to fill this gap by focusing on \"linguistically hard-to-describe\" cues.", "questions": "See Weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper clearly identifies a weakness in existing audio benchmarks. The \"caption-only\" experiment (Figure 1) provides empirical evidence that current benchmarks often test text-level semantics rather than fine-grained audio perception.\n2. The paper's most significant contribution is identifying and proving the insufficiency of standard audio preprocessing in LALMs. The \"additive inverse\" experiment in Figure 3 is a simple and effective demonstration that a model's spatial reasoning capabilities are non-existent if it just averages channels to mono.\n3. The analysis provides clear takeaways for the community, such as the need for models that natively process multi-channel audio and the severe limitations of open-source models in multi-audio grounding (as shown in Figure 9 ablation).", "weaknesses": "1. My primary concern is with the \"channel-wise input\" proposed for spatial reasoning. While this is a clever workaround for the \"mono-averaging\" problem, it fundamentally changes the task. Instead of evaluating native spatial audio perception, this method tests a model's ability to reason about two separate mono audio streams guided by an explicit textual prompt (e.g., \"Audio 1 is the left-ear channel and Audio 2 is the right-ear channel\"). This confounds auditory intelligence with text-based reasoning and an understanding of the experimental setup. It does not truly measure the model's ability to process and fuse interaural cues (ITD, ILD) from a single binaural stream. The paper acknowledges models are not trained on multi-audio inputs, but I think this workaround is a significant compromise.\n2. The temporal reordering task relies on \"strong sequential uniqueness\". This is robust for tasks governed by physics (e.g., Doppler effect , fluid dynamics). However, I am less convinced about the \"Daily Scene Scripts\" and \"Event-Triggered Consequences\"  subcategories. For example, in the teeth-brushing case study (Figure 12) , the \"correct\" order is given as brushing -> spit foam -> turn on tap/spit. However, an alternative like GPT-4o Audio's turn on water -> brush -> rinse/spit is just as logical and common. This suggests \"logical universality\"  is not as high as claimed.\n3. The foundational perception tasks use sine waves for non-spatial attributes. While this provides control, its relevance to perceiving pitch or loudness in complex, real-world sounds (the paper's motivation) is unclear. More importantly, the spatial perception tasks are simulated in rooms with simple source sounds (\"alarm,\" \"applause,\" \"telephones\"). A critical aspect of real-world spatial hearing is localization and tracking in the presence of diffuse background noise and significant reverberation. It is not clear from the paper or appendix if the benchmark tests performance under varying SNRs or reverberation times (RTs), which are standard parameters in auditory scene analysis.\n4. Missing ablation on spatial realism: how do models perform if reverberation or stereo cues are removed?\n5. Section 5 implies that STAR-Bench measures general “audio reasoning ability,” but all examples rely on synthetic or controlled sounds. Thus, the benchmark may not generalize to naturalistic auditory scenes (e.g., urban soundscapes, conversational dynamics). At least testing transfer performance would strengthen external validity.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "F3e2xwMXJk", "reviewer": "Reviewer_pbHk", "summary": "This paper introduces STAR-Bench, a benchmark designed to evaluate “4D Audio Intelligence”, defined as reasoning over sound dynamics in both time (1D) and 3D space. The authors argue that existing audio benchmarks (e.g., MMAU, MMAR) largely assess coarse, text-representable semantics rather than fine-grained perceptual reasoning. STAR-Bench aims to fill this gap through two complementary levels: a) Foundational Acoustic Perception — quantitative evaluation of six acoustic attributes (pitch, loudness, duration, azimuth, elevation, distance) under absolute and relative regimes.b) Holistic Spatio-Temporal Reasoning — tests requiring temporal segment reordering (continuous and discrete processes) and spatial reasoning (localization, multi-source relations, dynamic trajectories). The dataset combines synthetic and real-world audio, curated through a four-stage pipeline involving procedural synthesis, AI-assisted filtering, human annotation, and expert validation. The benchmark comprises 2,353 questions across these tasks.", "questions": "I don't have questions.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Establishes a clear and rigorous formalization of “audio 4D intelligence.”\n- I agree on the problem formulation.\n- The benchmark is novel and challenging. At the same time, it is useful.\n- Benchmark design is systematic, combining physical simulation, human validation, and multiple subtask layers.\n- Offers a quantitative diagnostic structure — separating perceptual, temporal, and spatial reasoning.\n- Provides valuable model insights (e.g., Gemini’s bottleneck in perception vs. open-source deficits in reasoning and grounding).", "weaknesses": "- (Minor) The benchmark’s scale (2.3k samples) is relatively small compared to typical multimodal datasets.\n- Evaluation focuses mainly on multiple-choice QA\n- I don’t have many questions. The work is impressive. Some similar works have popped up lately but can be considered as parallel.\n- Some points about Table 1 I am not sure how the ticks and crosses were made. What does Deep Reasoning meaning and why Spatial DR and Temporal DR are crossed for MMAU-Pro? I think spatial is also covered in MMAU-Pro? Can the authors provide more clarity?\n- I have no questions. This looks like a well-rounded paper, and I vote for acceptance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)