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arxiv: 2605.22012 · v1 · pith:TQDOVCMHnew · submitted 2026-05-21 · 💻 cs.CL · cs.CV

LatentOmni: Rethinking Omni-Modal Understanding via Unified Audio-Visual Latent Reasoning

Yifan Dai , Zhenhua Wu , Bohan Zeng , Daili Hua , Jialing Liu , Bozhou Li , Yuran Wang , Chengzhuo Tong
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Hao Liang Xiaochen Ma Junbo Niu Tianyu Guo Yang Shi Yue Ding Yiyan Ji Bingyin Mei Yushuo Guan Yuanxing Zhang Pengfei Wan Fangcheng Fu Wentao Zhang
This is my paper

Pith reviewed 2026-05-22 06:31 UTC · model grok-4.3

classification 💻 cs.CL cs.CV
keywords omnimodal understandinglatent reasoningaudio-visual reasoningchain-of-thoughtmultimodal large language modelsfeature supervisionposition embeddingjoint reasoning
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The pith

LatentOmni shows that interleaving text with shared audio-visual latent states outperforms explicit text chain-of-thought for joint reasoning tasks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper claims that text-based chain-of-thought weakens audio-visual reasoning by turning continuous signals into discrete language tokens that lose temporal detail and lean on language priors. It proposes instead that a unified latent space can keep dense sensory information intact while still supporting autoregressive generation. LatentOmni puts this into practice by alternating textual steps with audio-visual latent states, supervising those states at the feature level, and adding Omni-Sync Position Embedding to keep audio and visual latents temporally aligned. The approach is trained on a new set of 35K interleaved audio-visual reasoning trajectories. Benchmark results indicate it leads open-source models and beats the text CoT baseline, indicating that latent-space joint reasoning offers a viable route to stronger omnimodal models.

Core claim

LatentOmni is a cross-modal reasoning framework that interleaves textual reasoning with audio-visual latent states. Feature-level supervision aligns the latent reasoning states with task-relevant sensory features, while Omni-Sync Position Embedding maintains temporal consistency between the audio and visual latent sequences. Trained on the LatentOmni-Instruct-35K dataset of audio-visual interleaved trajectories, the model achieves the best performance among the evaluated open-source models and consistently outperforms the Explicit Text CoT baseline on multiple audio-visual reasoning benchmarks.

What carries the argument

Interleaved textual reasoning steps with aligned audio-visual latent states, using feature-level supervision and Omni-Sync Position Embedding (OSPE) to preserve sensory detail and temporal consistency.

If this is right

  • Feature-level supervision can directly tie intermediate reasoning states to raw sensory features instead of relying on language summaries.
  • Omni-Sync Position Embedding can enforce temporal alignment between separate audio and visual latent streams during generation.
  • Interleaved latent trajectories allow models to keep continuous timing information that text tokens normally discard.
  • Training on audio-visual interleaved reasoning data transfers to better performance on downstream joint reasoning benchmarks.
  • Latent-space reasoning remains compatible with standard autoregressive decoding while reducing compression loss.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same interleaving pattern could be tested on additional modalities such as depth or thermal data if comparable latent encoders exist.
  • Reducing dependence on language priors might improve robustness in domains where language cues are sparse or misleading.
  • Scaling the latent supervision to longer sequences could reveal whether the temporal consistency benefits persist at larger context lengths.

Load-bearing premise

A single unified latent space can preserve enough dense audio-visual information to support accurate joint reasoning while remaining compatible with autoregressive text generation.

What would settle it

An experiment that shows an explicit text CoT baseline matching or exceeding LatentOmni accuracy on the same benchmarks after equal training data and compute would indicate that the latent-space advantage does not hold.

Figures

Figures reproduced from arXiv: 2605.22012 by Bingyin Mei, Bohan Zeng, Bozhou Li, Chengzhuo Tong, Daili Hua, Fangcheng Fu, Hao Liang, Jialing Liu, Junbo Niu, Pengfei Wan, Tianyu Guo, Wentao Zhang, Xiaochen Ma, Yang Shi, Yifan Dai, Yiyan Ji, Yuanxing Zhang, Yue Ding, Yuran Wang, Yushuo Guan, Zhenhua Wu.

Figure 1
Figure 1. Figure 1: Comparison between LatentOmni and the Explicit Text CoT baseline (detailed in 4.1). [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of LatentOmni. Left: the model alternates between textual generation and latent [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Construction pipeline of LatentOmni-Instruct-35K. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Ablation studies on latent configurations across three benchmarks, specifically evaluating [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Prompt used to synthesize a single complex, open-ended AVQA pair from temporally [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Prompt used to synthesize a single complex, multiple-choice AVQA pair with grounded [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Prompt used to evaluate the intrinsic quality and modality dependency of synthesized [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Prompt used to synthesize a concise video caption focusing exclusively on observable [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Prompt used to synthesize a concise audio caption detailing identifiable sounds and speech [PITH_FULL_IMAGE:figures/full_fig_p017_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Prompt used to fuse the segment-level video caption and audio caption. [PITH_FULL_IMAGE:figures/full_fig_p017_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Prompt used to refine fragmented segment captions by cross-referencing full captions to [PITH_FULL_IMAGE:figures/full_fig_p017_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Prompt used to synthesize interleaved reasoning trajectories with explicit segment citations [PITH_FULL_IMAGE:figures/full_fig_p018_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: LatentOmni example: AV Event Alignment. LatentOmni accurately anchors task-relevant [PITH_FULL_IMAGE:figures/full_fig_p019_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: LatentOmni example: Inference. LatentOmni accurately anchors task-relevant audio [PITH_FULL_IMAGE:figures/full_fig_p020_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: LatentOmni example: Reasoning. LatentOmni accurately anchors task-relevant audio [PITH_FULL_IMAGE:figures/full_fig_p021_15.png] view at source ↗
read the original abstract

Joint audio-visual reasoning is essential for omnimodal understanding, yet current multimodal large language models (MLLMs) still struggle when reasoning requires fine-grained evidence from both modalities. A central limitation is that explicit text-based chain-of-thought (CoT) compresses continuous audio-visual signals into discrete tokens, weakening temporal grounding and shifting intermediate reasoning toward language priors. We argue that a unified latent space is a better medium for such reasoning because it preserves dense sensory information while remaining compatible with autoregressive generation. Based on this insight, we propose \textbf{LatentOmni}, a cross-modal reasoning framework that interleaves textual reasoning with audio-visual latent states. LatentOmni introduces feature-level supervision to align latent reasoning states with task-relevant sensory features and uses Omni-Sync Position Embedding (OSPE) to maintain temporal consistency between latent audio and visual states. We further construct \textbf{LatentOmni-Instruct-35K}, a dataset of audio-visual interleaved reasoning trajectories for supervising latent-space reasoning. Comprehensive evaluation across multiple audio-visual reasoning benchmarks demonstrates that LatentOmni achieves the best performance among the evaluated open-source models and consistently outperforms the Explicit Text CoT baseline, supporting latent-space joint reasoning as a promising path toward stronger omnimodal understanding.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes LatentOmni, a cross-modal reasoning framework for omnimodal understanding that interleaves textual reasoning steps with audio-visual latent states in a unified latent space. It introduces feature-level supervision to align latent states with sensory features, Omni-Sync Position Embedding (OSPE) for temporal consistency, and the LatentOmni-Instruct-35K dataset of interleaved reasoning trajectories. The central claim is that this latent-space approach outperforms explicit text-based chain-of-thought (CoT) by preserving dense sensory information, with comprehensive evaluations showing best-in-class results among open-source models on audio-visual reasoning benchmarks.

Significance. If the gains are shown to arise specifically from latent-space joint reasoning rather than dataset or supervision changes, the work would provide a concrete alternative to text CoT compression in MLLMs and support the hypothesis that unified latent spaces better maintain temporal and sensory grounding for tasks requiring fine-grained audio-visual evidence.

major comments (2)
  1. [Evaluation] Evaluation section: The claim that LatentOmni 'consistently outperforms the Explicit Text CoT baseline' and achieves best performance among open-source models is presented without component ablations that hold the LatentOmni-Instruct-35K dataset and auxiliary alignment loss fixed while varying only the reasoning representation (latent states vs. explicit text). This leaves open the possibility that observed improvements are driven by the new data and supervision rather than the proposed latent reasoning mechanism.
  2. [Abstract and §3] Abstract and §3 (framework description): The assertion that a unified latent space 'preserves dense sensory information while remaining compatible with autoregressive generation' is central to the motivation, yet the manuscript does not quantify information loss in the text CoT baseline (e.g., via reconstruction metrics or attention analysis) nor demonstrate that OSPE and feature-level supervision are the minimal additions needed to realize this preservation.
minor comments (2)
  1. [§3] Notation for Omni-Sync Position Embedding (OSPE) is introduced without an explicit equation or pseudocode showing how it synchronizes audio and visual latent positions relative to textual tokens.
  2. [Dataset section] The construction details of LatentOmni-Instruct-35K (trajectory generation process, quality filtering, and split statistics) are referenced but would benefit from a dedicated table or appendix for reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback, which highlights important ways to strengthen the isolation of our core contribution. We address each major comment below and outline the revisions we will make.

read point-by-point responses

  1. Referee: [Evaluation] Evaluation section: The claim that LatentOmni 'consistently outperforms the Explicit Text CoT baseline' and achieves best performance among open-source models is presented without component ablations that hold the LatentOmni-Instruct-35K dataset and auxiliary alignment loss fixed while varying only the reasoning representation (latent states vs. explicit text). This leaves open the possibility that observed improvements are driven by the new data and supervision rather than the proposed latent reasoning mechanism.

    Authors: We agree that a more tightly controlled ablation is needed to isolate the effect of latent versus explicit-text reasoning. In the current experiments the Explicit Text CoT baseline is trained on the same LatentOmni-Instruct-35K trajectories (converted to text) but without the auxiliary alignment loss applied to latent states. In the revision we will add a new ablation that trains a text-based model on the identical dataset while adapting the auxiliary alignment loss to operate on text embeddings, thereby holding both data and supervision fixed and varying only the reasoning representation. Results will be reported in Section 4. revision: yes

  2. Referee: [Abstract and §3] Abstract and §3 (framework description): The assertion that a unified latent space 'preserves dense sensory information while remaining compatible with autoregressive generation' is central to the motivation, yet the manuscript does not quantify information loss in the text CoT baseline (e.g., via reconstruction metrics or attention analysis) nor demonstrate that OSPE and feature-level supervision are the minimal additions needed to realize this preservation.

    Authors: We acknowledge that direct quantification of information preservation would strengthen the motivational claim. Although performance gains on tasks that require fine-grained audio-visual evidence provide indirect support, we will add explicit analysis in the revision: reconstruction error of sensory features from the intermediate reasoning states for both the latent and text-CoT paths, together with attention-map comparisons. Regarding minimality, our existing ablations already isolate the contribution of OSPE and feature-level supervision; we will expand the discussion in §3 to present these components as the key enablers identified through ablation rather than asserting they constitute the absolute minimal set. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical results independent of mechanism

full rationale

The paper advances an insight that unified latent space preserves dense sensory information better than explicit text CoT, then introduces a new framework (LatentOmni with feature-level supervision and OSPE), constructs a new dataset (LatentOmni-Instruct-35K), and reports benchmark performance gains over baselines. No equations, fitted parameters renamed as predictions, or self-citation chains are present in the provided text that would make the central performance claim reduce by construction to prior inputs. The evaluation is presented as external validation rather than tautological output of the model's own definitions or fits. This is the common case of an empirical proposal whose claims rest on new measurements rather than definitional equivalence.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The paper rests on the domain assumption that latent representations preserve more useful information than text tokens for cross-modal reasoning and introduces new technical components without external validation of those components.

axioms (1)
  • domain assumption A unified latent space preserves dense sensory information better than discrete text tokens for joint audio-visual reasoning.
    Explicitly stated as the central insight motivating the framework in the abstract.
invented entities (2)
  • Omni-Sync Position Embedding (OSPE) no independent evidence
    purpose: Maintain temporal consistency between latent audio and visual states during interleaved reasoning.
    Newly proposed component described in the abstract as part of the framework.
  • LatentOmni-Instruct-35K no independent evidence
    purpose: Provide audio-visual interleaved reasoning trajectories to supervise latent-space reasoning.
    Dataset constructed specifically for training the proposed method.

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discussion (0)

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