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arxiv: 2606.30597 · v1 · pith:2ITJ5ULNnew · submitted 2026-06-29 · 💻 cs.CV

Learning from Reliable Latent Prompts for Visual Recognition with Missing Modalities

Taixi Chen , Nancy Guo This is my paper

Pith reviewed 2026-06-30 06:04 UTC · model grok-4.3

classification 💻 cs.CV
keywords missing modalitiesprompt learningvisual recognitionmultimodal modelslatent promptscross-modal compensation
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The pith

Learnable latent prompts capture stable modality-intrinsic priors that remain reliable even when input modalities are heavily missing.

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

The paper contends that conditioning prompts on instance-level features leads to unstable learning as missing rates rise because those features become unreliable. It proposes instead to treat learnable prompts as fixed latent anchors that hold modality-intrinsic priors decoupled from any particular corrupted input. This input-agnostic design is claimed to supply consistent guidance and enable cross-modal compensation at extreme missing rates such as 90 percent. Experiments on three benchmarks are said to show state-of-the-art results across many missing-modality configurations.

Core claim

By modeling input-agnostic learnable prompts as stable latent anchors rather than generating them from unreliable instance features, the method supplies robust guidance and effective cross-modal knowledge compensation under high rates of missing modalities.

What carries the argument

Input-agnostic learnable prompts treated as stable latent anchors that encode modality-intrinsic priors independent of corrupted inputs.

If this is right

  • The method achieves state-of-the-art accuracy on three benchmark datasets under a wide range of missing-modality conditions.
  • Performance remains high even when 90 percent of modalities are absent.
  • The paradigm supplies a robust alternative to input-conditioned prompt strategies for the missing-modality problem.

Where Pith is reading between the lines

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

  • The same latent-anchor idea could be tested on other multimodal tasks such as audio-visual or text-image retrieval.
  • If the priors prove truly input-agnostic, the approach might reduce reliance on explicit modality imputation or reconstruction steps.
  • The design suggests a general route for making prompt-based models more tolerant to noisy or incomplete training signals.

Load-bearing premise

Learnable latent prompts can hold stable, modality-intrinsic information that does not depend on the actual input data being present.

What would settle it

A controlled comparison at 90 percent missing rate where replacing the latent-prompt module with a standard input-conditioned prompt generator produces equal or better accuracy.

Figures

Figures reproduced from arXiv: 2606.30597 by Nancy Guo, Taixi Chen.

Figure 1
Figure 1. Figure 1: Comparison between input-conditioned prompting methods (e.g., DCP [ [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the proposed Learning from Reliable Latent Prompts (LLP) framework. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of our final model (Ours) with (1) baseline, which directly drops the features [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Generalizability Analysis of Our Method to Different Missing Rates on MM-IMDb [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Visualization of concatenated prompt-enhanced features from the two encoders on the [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Analysis of Robustness to Different Missing Rates on Hateful Memes dataset [ [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Ablation study on prompt length and depth on the Hateful Memes dataset [ [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗
read the original abstract

Large-scale multimodal models (LMMs) have achieved superior performance in visual recognition by synergizing information across diverse, massive-scale paired modalities. In real-world scenarios, however, missing-modality inputs are ubiquitous, causing models optimized for modality-complete data to exhibit precipitous performance degradation. Existing research has introduced prompt learning to mitigate this issue, typically by generating dynamic prompts from instance-level features, regardless of whether the input modalities are complete or partially absent. However, such input-conditioned strategies are hindered by the escalating unreliability of instance-level features; as higher missing rates increase the proportion of incomplete modalities, the resulting instability in prompt learning limits the model's performance. To address this limitation, we hypothesize that learnable latent prompts themselves encapsulate stable, modality-intrinsic priors that are decoupled from corrupted inputs. Consequently, we propose a novel paradigm: Learning from Reliable Latent Prompts. Unlike prior methods, we model input-agnostic learnable prompts as stable latent anchors that enable robust guidance and effective cross-modal knowledge compensation, even under extreme missing rates (e.g., 90%). Empirical results across three benchmark datasets demonstrate that our "learn-from-latent-prompts" approach achieves state-of-the-art performance across a wide range of missing-modality scenarios. Extensive experiments further confirm the effectiveness of this paradigm in providing a robust solution to the missing-modality problem.

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 / 1 minor

Summary. The paper hypothesizes that learnable latent prompts encapsulate stable, modality-intrinsic priors decoupled from corrupted inputs. It proposes a 'Learning from Reliable Latent Prompts' paradigm that models input-agnostic prompts as stable latent anchors for robust cross-modal compensation under missing modalities (including extreme rates like 90%). It claims this yields state-of-the-art performance on three benchmark datasets across a wide range of missing-modality scenarios, outperforming prior input-conditioned prompt-learning methods.

Significance. If the empirical claims hold with proper validation, the approach could address a practical limitation in multimodal models by avoiding instability from instance-level feature conditioning at high missing rates, offering a more reliable paradigm for real-world visual recognition tasks.

major comments (2)
  1. Abstract: The manuscript asserts SOTA empirical results across three benchmark datasets and a wide range of missing-modality scenarios but supplies no methods, baselines, error bars, dataset details, ablation studies, or quantitative evidence, rendering the central performance claim unverifiable from the provided text.
  2. Abstract (paragraph 2): The claim that input-conditioned strategies are limited by 'escalating unreliability of instance-level features' at higher missing rates is presented as motivation, but no supporting analysis, equations, or preliminary results are shown to establish this as the load-bearing limitation versus other factors.
minor comments (1)
  1. Abstract: The term 'learn-from-latent-prompts' is introduced in quotes without a clear definition or distinction from standard prompt learning in the opening paragraphs.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the two major comments on the abstract below. The full manuscript contains the requested details in Sections 3–5; we propose targeted revisions to the abstract for improved clarity.

read point-by-point responses

  1. Referee: Abstract: The manuscript asserts SOTA empirical results across three benchmark datasets and a wide range of missing-modality scenarios but supplies no methods, baselines, error bars, dataset details, ablation studies, or quantitative evidence, rendering the central performance claim unverifiable from the provided text.

    Authors: The abstract is intentionally concise and summarizes the contribution; the full manuscript supplies all requested elements (methods in Sec. 3, baselines/results with error bars in Sec. 4, dataset details in Sec. 4.1, ablations in Sec. 5). To make the abstract more self-contained, we will revise it to name the three benchmarks and note the consistent outperformance margin. revision: yes

  2. Referee: Abstract (paragraph 2): The claim that input-conditioned strategies are limited by 'escalating unreliability of instance-level features' at higher missing rates is presented as motivation, but no supporting analysis, equations, or preliminary results are shown to establish this as the load-bearing limitation versus other factors.

    Authors: The motivation is substantiated by analysis and preliminary experiments in Sec. 3.2 of the full manuscript, which quantify the instability of instance-level conditioning at high missing rates. We will add a brief parenthetical reference to this analysis in the revised abstract. revision: partial

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper articulates a hypothesis that input-agnostic latent prompts provide stable modality-intrinsic priors, proposes a corresponding paradigm, and reports empirical SOTA results on three benchmarks. No equations, fitted parameters renamed as predictions, or self-citation chains are present in the supplied text that would reduce the central claim to a definitional tautology or construction from its own inputs. The derivation is self-contained as a standard empirical proposal whose validity rests on external benchmark performance rather than internal reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no information on free parameters, background axioms, or new postulated entities; cannot populate ledger entries.

pith-pipeline@v0.9.1-grok · 5766 in / 1025 out tokens · 27814 ms · 2026-06-30T06:04:47.528894+00:00 · methodology

discussion (0)

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