Hear, Localize, and Reason: Spatially Aware Scene Understanding for Audio-Visual LLMs

arXiv:2610.21588 · cs.CV, cs.SD · Submitted 2026-10-15 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: We're looking at the paper: "Hear, Localize, and Reason".

Jane: The gist The work introduces HLR-AVSceneQA, a comprehensive benchmark for spatial audio-visual scene understanding organized around a hear-localize-reason hierarchy, and HLR-LLM,

Tom: First, who's behind it and why it matters.

Paper summary: Tom: Hey Jane, so we're looking at this paper today, "Hear, Localize, and Reason: Spatially Aware Scene Understanding for Audio-Visual LLMs." Basically, they’re tackling the problem that most audio visual large language models just treat audio as just a sound event without thinking about where it came from or how it connects to what you see.

Jane: That sounds like a big gap in multimodal understanding, Tom. So, what is the main idea here for this paper? What are they actually proposing?

Tom: They introduce something called HLR-AVSceneQA, which they call a comprehensive benchmark. It’s structured around this hierarchy: you first have to recognize what sound you hear, then localize where that sound comes from in both your egocentric and allocentric views, and finally use that localized source to reason about how it relates to the visual scene.

Lu: It seems like they're setting up a very specific evaluation structure. I see them evaluating spatial hearing, which is just recognizing what is heard, then spatial grounding for localization in both egocentric and allocentric views, and then spatial relational reasoning for connecting those sound sources to the visual scene itself.

Meng: A benchmark sounds good on paper, but how do they actually build this stuff? Is it just a set of questions? I need to know if this is something that can actually be used reliably in production or on real traffic.

Tom: They built it using a simulator-based pipeline where Stage one synthesizes spatial audio visual scenes with structured metadata using things like SoundSpaces two point zero and HMthree deeSem to create indoor three dimensional scenes from an agent’s egocentric viewpoint <ref:2610.21588#pg1>.

Jane: So they are creating these controlled environments, these simulated rooms, to generate the data for the benchmark tasks. That sounds like a solid starting point for making sure the annotations are reliable before you even test any models on it.

Lu: They use this pipeline to produce diverse and controllable spatial audio visual scenes with reliable annotations. The setup seems designed to give them a very structured way of testing different aspects of spatial understanding, which is what they need when you’re training a model on complex reasoning tasks.

Tom: Then they introduce HLR-LLM, which is their proposed model. It augments an existing strong audio visual foundation model with an explicit spatial audio branch alongside its monaural semantic audio pathway.

Meng: An augmented foundation model sounds like it adds complexity to the architecture, which usually means more parameters and more overhead for running it. What exactly is this spatial branch doing that the base model isn't handling?

Paper summary: Lalam: The paper describes a spatial audio encoder and projector providing binaural spatial cues alongside visual context and monaural semantic audio. This allows for specific spatial audio tokenization involving an encoder like fspa nine and a trainable MLP projector pspa <ref:2610.21588#pg3>.

Jane: So, instead of just looking at the sound as a generic semantic token, they are explicitly encoding it into a binaural spatial representation that gets mapped into the language model embedding space. That’s where the spatial modeling comes in.

Tom: And they train this HLR-LLM using a three stage curriculum. Stage one is spatial audio alignment, Stage two is spatial audio visual instruction tuning, and Stage three is final stage chain-of-thought supervision for multi step spatial reasoning <ref:2610.21588#pg1>.

Lu: The idea of that curriculum suggests they are building the model up piece by piece to handle the complexity. Stage one pretrains the spatial audio projector using a spatial audio only QA dataset nine to verbalize spatial audio properties, which is really about getting the basic spatial mapping right first <ref:2610.21588#pg3>.

Meng: Chain-of-thought supervision sounds like heavy instruction tuning, which is fine for accuracy on a test set, but how do you ensure that the model isn't just memorizing those reasoning steps instead of actually learning the underlying physics of sound?

Tom: Stage three specifically uses CoT formatted answers for multi step spatial tasks to encourage the model to connect auditory cues, spatial estimates, and visual evidence through explicit intermediate reasoning steps <ref:2610.21588#pg1>. This is meant to force the model to show its work when it has to do complex spatial reasoning.

Jane: So, the whole point seems like they're trying not just for the model to get a right answer, but for it to be able to explain *how* it got that answer by showing these explicit steps. That’s a big step toward making the AI more interpretable.

Lu: And what they claim is that existing audio visual large language models remain limited in spatial grounding and relational reasoning, but HLR-LLM substantially improves spatial audio visual scene understanding without compromising semantic audio visual perception.

Meng: So, what are the concrete results? I want to see the numbers on how much better it is than the dual channel Qwen2 point 5 Omni baseline you mentioned <ref:2610.21588#pg1>.

Tom: In Table four HLR-LLM achieves the best performance across all egocentric and allocentric grounding metrics and consistently outperforms that dual channel Qwen2 point 5 Omni baseline <ref:2610.21588#pg1>. And their ablation study shows that binaural audio is essential for spatial grounding, because removing the binaural pathway substantially degrades egocentric and allocentric grounding and spatial relational reasoning.

Paper summary: Jane: That’s a strong finding, Tom. So the binaural audio isn't just nice to have; it’s actually critical for getting the localization right in both directions—looking out from your perspective or looking at the scene from outside.

Lu: And they also found that final stage CoT tuning improves both egocentric and allocentric grounding, which supports their idea that explicit reasoning helps solidify those spatial estimates.

Meng: If I were deploying this, I’d be worried about the cost of running this enhanced model with all those extra spatial tokens. Does the complexity add significant latency when it's actually in use?

Tom: Well, they tested it on STARSS23 and showed that HLR-AVSceneQA pretraining provides useful spatial priors for downstream adaptation. They also mention that real world adaptation experiments on STARSS23 show this benefit.

Jane: It sounds like the paper is saying that for agents in indoor environments or robotics, this ability to recognize what sound is present, localize where it originates, and reason about how it relates to the current scene is especially important. That’s really relevant for assistive agents and navigation.

Lu: The work concludes by showing that HLR-LLM generates explicit CoT rationales that localize the sound source, relate it to visible objects, and derive the correct answer. Crucially, they say the model uses spatial audio for localization and subsequent spatial reasoning rather than relying solely on the visible source position in counterfactual analysis.

Tom: The implication here is that explicit spatial audio modeling benefits not just source localization but also reasoning about sound sources in the visual scene. HLR-AVSceneQA provides a unified hear localize reason evaluation that spans spatial hearing, grounding, multi source scenes, and scene level relational reasoning.

Jane: So to wrap up what we've heard about "Hear, Localize, and Reason: Spatially Aware Scene Understanding for Audio-Visual LLMs," it seems like the core contribution is moving AV LLMs past just recognizing sound events to actually understanding their physical placement in a scene.

Tom: Exactly. They show how adding that explicit spatial audio branch and using a structured curriculum helps models improve spatial reasoning while keeping the semantic perception intact.

Lu: The paper gives us a framework, HLR-AVSceneQA, for evaluating whether these hierarchical abilities—hearing, localizing, and reasoning—are being developed in current models.

Meng: For me as someone who ships models, it tells me that if you want spatial awareness in your production systems right now, you need to build that explicit spatial pathway into your foundation model architecture.

Jane: It gives us a concrete direction for building these spatially aware systems for embodied AI and robotics. We’ll keep an eye on how this benchmark gets used to drive future developments.

Conclusion: Tom: So we've seen how they build HLR-LLM, how they train it with that three-stage curriculum, and how it beats the old models on spatial grounding metrics.

Jane: It really shows how a model can learn to actually use sound in space, not just as a background noise thing. The main idea is that we need to move past just hearing what’s there to knowing exactly where it came from and how it relates to what you see.

Lu: The authors are arguing that existing AV LLMs stop at semantic audio content, but they aren't really doing any spatial reasoning about the sound source itself. This paper introduces a framework called HLR-AVSceneQA to test if models can actually do that hear-localize-reason hierarchy.

Meng: So, what does this mean for me when I’m trying to deploy something? It means we need more than just good semantic understanding; we need models that can map sound tokens into physical locations in three dee space.

Lalam: I'm pretty sure the core finding is that adding a dedicated spatial audio branch and using those chain-of-thought steps really unlocks this spatial capability. The binaural audio input is definitely what makes the difference for localization.

Tom: Exactly, and they show that without that binaural pathway, the egocentric and allocentric grounding metrics drop significantly. That’s a hard number showing how essential it is.

Jane: For someone just listening to the show, this changes things because it means an AI agent doesn't just know "a dog is barking," it knows "the dog is barking over there on the left side of your view."

Lu: The paper suggests that this explicit spatial modeling helps models reason about sound sources in a way they didn't before. It’s not just about picking a visible source position; it’s using the sound itself to anchor the reasoning.

Meng: I see how that translates to real-world robotics, where an agent needs to know if a warning noise is coming from the hallway or from the kitchen right now. It moves it from guessing to knowing.

Lalam: That's what they claim with their qualitative analysis; they generate explicit chain-of-thought rationales that show exactly how the sound source relates to visible objects.

Tom: So, looking at the title, "Hear, Localize, and Reason," it’s really a roadmap for building multimodal systems that are actually grounded in physics rather than just statistics.

Jane: It feels like they’ve set up a complete test—a benchmark—that forces us to verify if these spatial abilities are actually being learned correctly across the board.

Lu: The implication is that future foundation models need this level of explicit spatial modeling built into their core architecture, not just tacked on as an afterthought.

Meng: It gives me something concrete to look at in my next project planning—we need to include a clear spatial audio tokenization and projection layer if we want reliable localization.

Lalam: I’m confident that HLR-AVSceneQA provides the best way to measure if this hierarchy is actually being developed effectively in current AI.

Kim Sung-Bin, Lee Jung-Mok, Jinwoo Jung, Oh Hyun-Bin, Hyeonggon Ryu, David Harwath, Tae-Hyun Oh

POSTECH · KAIST · Hankuk University of Foreign Studies · The University of Texas at Austin

cs.CV, cs.SD

Submitted: 2026-10-15

Updated: 2026-10-15

Comments: NeurIPS 2026; Project page: https://hlrllm.github.io/

Project page: https://hlrllm.github.io

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 90/100

The gist: The gist The work introduces HLR-AVSceneQA, a comprehensive benchmark for spatial audio-visual scene understanding organized around a hear-localize-reason hierarchy, and HLR-LLM, which augments a

Key concepts

HLR-AVSceneQA: A new benchmark designed to test how well AI models can perform three sequential spatial tasks: hearing what is present, locating sound sources in different viewpoints (egocentric/allocentric), and using that location information to reason about the scene. It follows a structured hierarchy for comprehensive evaluation.
HLR-LLM: An enhanced audio-visual language model that adds a specific branch for spatial audio. This branch processes binaural spatial cues alongside visual and monaural audio, allowing the model to explicitly model where sounds come from and how they connect to the visual environment.
Hear-Localize-Reason Hierarchy: A structured evaluation framework that breaks down spatial understanding into three steps: first, perceiving what sound is heard (spatial hearing); second, pinpointing the sound source location in 3D space (spatial grounding); and third, explaining how that located sound relates to visible objects or the scene (spatial relational reasoning).
Chain-of-Thought Supervision: A training strategy where the model is trained not just on final answers, but on detailed, step-by-step reasoning processes. For spatial tasks, this forces the LLM to explicitly connect auditory cues, spatial estimates, and visual evidence through intermediate logical steps.

Summary

The gist The work introduces HLR-AVSceneQA, a comprehensive benchmark for spatial audio-visual scene understanding organized around a hear-localize-reason hierarchy, and HLR-LLM, which augments a strong audio-visual foundation model with an explicit spatial audio branch and chainofthought supervision to improve spatial reasoning while preserving semantic perception.

HLR-AVSceneQA Benchmark

HLR-AVSceneQA is introduced as a comprehensive benchmark for spatial audio-visual scene understanding organized around a hear-localize-reason hierarchy, which evaluates whether models can recognize what is heard, localize sound sources in egocentric and allocentric views, and use these localized sources to reason about how they relate to the visual scene The benchmark follows a hear-localize-reason hierarchy: spatial hearing evaluates what is heard, spatial grounding evaluates source localization in egocentric and allocentric views, and spatial relational reasoning evaluates how localized sound sources relate to the visual scene. It spans 12 tasks as summarized in Table 2. The benchmark is constructed with a simulator-based pipeline, producing diverse, controllable spatial audio-visual scenes with reliable annotations. Stage I synthesizes spatial audio-visual scenes with structured metadata using SoundSpaces 2.0 and HM3DSem to create indoor 3D scenes from an agent’s egocentric viewpoint.

HLR-LLM Architecture and Training

HLR-LLM is a spatially aware AV-LLM that extends audio-visual semantic understanding with explicit spatial audio modeling. It builds on an existing AV-LLM and augments it with a spatial audio branch, where a spatial audio encoder and projector provide binaural spatial cues alongside visual context and monaural semantic audio. Spatial audio tokenization involves encoding a binaural signal with a pretrained spatial audio encoder fspa [9] and mapping the features into the language model embedding space using a trainable MLP projector pspa. The resulting multimodal input sequence is X = [E(q), Zvis, Zaud, Zspa], where E(q) denotes text embeddings, Zvis visual tokens, Zaud monaural audio tokens, and Zspa spatial audio tokens. The model is trained through a three-stage curriculum: Stage 1 is spatial audio alignment, Stage 2 is spatial audio-visual instruction tuning, and Stage 3 is final-stage CoT tuning for structured spatial reasoning.

Curriculum Training Strategy

The training curriculum progresses through three stages to guide multi-step spatial reasoning. Stage 1, Spatial audio alignment, pretrains the spatial audio projector using a spatial audioonly QA dataset [9] to verbalize spatial audio properties. Stage 2, Spatial audio-visual instruction tuning, fine-tunes the model on the training split of HLR-AVSceneQA to teach it to jointly use visual tokens, monaural audio tokens, and spatial audio tokens across spatial hearing, grounding, and relational reasoning. Stage 3 is Chain-of-thought tuning, which continues instruction tuning with CoT-formatted answers for multi-step spatial tasks to encourage the model to connect auditory cues, spatial estimates, and visual evidence through explicit intermediate reasoning steps.

Experimental Results and Findings

Experiments show that existing AV-LLMs remain limited in spatial grounding and relational reasoning, while HLR-LLM substantially improves spatial audio-visual scene understanding without compromising semantic audio-visual perception. In Table 4, HLR-LLM achieves the best performance across all egocentric and allocentric grounding metrics and consistently outperforms the dual-channel Qwen2.5-Omni baseline. The ablation study indicates that binaural audio is essential for spatial grounding, as removing the binaural pathway substantially degrades egocentric and allocentric grounding and spatial relational reasoning. Furthermore, final-stage CoT tuning improves both egocentric and allocentric grounding. Real-world adaptation experiments on STARSS23 further show that HLR-AVSceneQA pretraining provides useful spatial priors for downstream adaptation.

Qualitative Analysis and Conclusion

Qualitative results demonstrate that HLR-LLM generates explicit CoT rationales that localize the sound source, relate it to visible objects, and derive the correct answer. The model uses spatial audio for localization and subsequent spatial reasoning rather than relying solely on the visible source position in counterfactual analysis. Overall, the results show that explicit spatial audio modeling benefits not only source localization but also reasoning about sound sources in the visual scene. The work concludes that HLR-AVSceneQA provides a unified hear-localize-reason evaluation that spans spatial hearing, grounding, multi-source scenes, and scene-level relational reasoning.

Broader Impact

HLR-AVSceneQA and HLR-LLM aim to support spatially aware audio-visual understanding for embodied AI, assistive agents, robotics, and accessibility applications. The ability to recognize what sound is present, localize where it originates, and reason about how it relates to the current scene is especially important for agents that navigate indoor environments and assist in daily situations. Future deployment of spatial audio-visual systems should follow strict privacy, consent, and data-minimization practices.

LLM Usage

The LLMs are used in three ways: first, HLR-LLM is built on a large language model as the reasoning module that integrates unified inputs including visual, monaural audio, and binaural spatial audio tokens. Second, an LLM is used to paraphrase question templates to improve linguistic diversity and reduce the risk of models learning fixed template patterns. Finally, an LLM is used to assist with editing and grammar checking, table refinement (Table S4), figure generation and refinement (Fig. S8, S9, S10, 11), and development of the data-viewer web page.

Licenses and Terms

The authors use existing datasets, simulators, models, and 3D assets only under their stated licenses and terms of use. SoundSpaces 2.0 is released under the Creative Commons Attribution 4.0 International license. HM3DSem / HM3D are used under their academic research terms and respect the restrictions of the underlying Matterport3D assets. Objaverse-XL is licensed under ODC-By v1.0, while individual objects have their own licenses. LibriSpeech is based on public-domain audiobooks and is distributed under a Creative Commons license.

References

The work cites numerous prior studies, including those on spatial audio understanding [9, 10, 31], general AV-LLMs [4, 5], and specific spatial audio-visual benchmarks like SAVVY [13] and JAEGER [14]. The paper also references work on chain-of-thought prompting for reasoning in LLMs [17, 18], and datasets such as STARSS23 for real-world adaptation [19].

Additional Details

The benchmark includes a two-stage simulator-based pipeline: Stage I synthesizes spatial audio-visual scenes with structured metadata, and Stage II generates QA pairs from this metadata. Human verification confirms that 99.0% of the QA pairs pass human verification across all tasks.

Improvements for AI systems

Spatial audio-visual large language models (AV-LLMs) can be improved by incorporating a spatial audio branch to explicitly model binaural cues, allowing the model to jointly attend to text, visual, downmixed monaural audio, and binaural spatial audio tokens. This allows the system to move beyond treating audio as monaural semantic content and gain direction-and-distance-sensitive cues for grounding.
The HLR-LLM architecture can be trained using a three-stage curriculum: spatial audio alignment, spatial audio-visual instruction tuning, and final-stage CoT tuning. This staged approach encourages the model to connect auditory, spatial, and visual cues through intermediate reasoning steps rather than directly predicting an answer, which is crucial for complex multi-step spatial reasoning.
The system can be enhanced to perform comprehensive scene understanding across a hear-localize-reason hierarchy by evaluating spatial hearing, spatial grounding for localizing sound sources in egocentric and allocentric views, and spatial relational reasoning. This allows the AI to reason about how localized sound sources relate to the visual scene, enabling it to handle tasks like determining if a source is hidden behind an object or identifying which visible object is spatially aligned with the sound.
The system can be adapted for real-world deployment by utilizing HLR-AVSceneQA pretraining, as experiments show that this training provides useful spatial priors for adaptation to real-world data, leading to significant gains in tasks like direction and distance prediction on STARSS23 recordings.