# Adaptive Modality Reliability Diagnosis and Restoration for Robust Multimodal Intent Recognition

arXiv: https://arxiv.org/abs/2608.03475 · HTML: https://mlsanity.com/paper/2608.03475/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 30 references that are in the map.
- First posted on arXiv 4 Aug 2026.

## Builds on (most cited first, 15 of 15)

- [Qwen2.5-Omni Technical Report](https://mlsanity.com/paper/2503.20215/) (2025)
- [Multimodal Transformer for Unaligned Multimodal Language Sequences](https://mlsanity.com/paper/1906.00295/) (2019)
- [MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2005.03545/) (2020)
- [SMIL: Multimodal Learning with Severely Missing Modality](https://mlsanity.com/paper/2103.05677/) (2021)
- [MIntRec: A New Dataset for Multimodal Intent Recognition](https://mlsanity.com/paper/2209.04355/) (2022)
- [MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations](https://mlsanity.com/paper/2403.10943/) (2024)
- [Integration of Pre-trained Networks with Continuous Token Interface for End-to-End Spoken Language Understanding](https://mlsanity.com/paper/2104.07253/) (2021)
- [Token-Level Contrastive Learning with Modality-Aware Prompting for Multimodal Intent Recognition](https://mlsanity.com/paper/2312.14667/) (2023)
- [Text Takes Over: A Study of Modality Bias in Multimodal Intent Detection](https://mlsanity.com/paper/2508.16122/) (2025)
- [Multimodal Classification and Out-of-distribution Detection for Multimodal Intent Understanding](https://mlsanity.com/paper/2412.12453/) (2024)
- [Deep Learning Approaches for Multimodal Intent Recognition: A Survey](https://mlsanity.com/paper/2507.22934/) (2025)
- [LLM-Guided Semantic Relational Reasoning for Multimodal Intent Recognition](https://mlsanity.com/paper/2509.01337/) (2025)
- [WDMIR: Wavelet-Driven Multimodal Intent Recognition](https://mlsanity.com/paper/2506.10011/) (2025)
- [Evolutionary Multimodal Reasoning via Hierarchical Semantic Representation for Intent Recognition](https://mlsanity.com/paper/2603.03827/) (2026)
- [Mitigating Multimodal Inconsistency via Cognitive Dual-Pathway Reasoning for Intent Recognition](https://mlsanity.com/paper/2605.09468/) (2026)

## Abstract (arXiv)

Multimodal intent recognition combines linguistic, acoustic, and visual evidence, but individual modalities may be noisy, missing, semantically conflicting, or disproportionately dominant. Existing methods typically infer modality importance implicitly and either reweight or suppress unreliable inputs, without determining whether a degraded modality can be repaired and subsequently trusted. We propose PRIME (Precision-weighted Reliability Inference and Modality rEstoration), a closed-loop reliability guided framework that jointly diagnoses, restores, and reassesses modality quality at the sample level. PRIME represents the weakness of each modality through a contextual log-variance estimated from complementary diagnostic evidence, including predictive confidence, epistemic disagreement, cross-modal consensus, and feature degeneracy. Because modality-reliability annotations are unavailable, the estimator is explicitly trained using controlled modality corruption with known degradation severity, together with a heteroscedastic uncertainty objective. Rather than directly discarding an unreliable modality, PRIME uses its estimated weakness to control a prototype-conditioned variational restoration module that reconstructs the degraded representation from complementary modalities. Crucially, reliability is re-estimated after restoration, allowing the model to determine whether the repaired representation has become sufficiently trustworthy to contribute to prediction. The resulting post-restoration precisions are used for inverse-variance multimodal fusion. Experiments on multimodal intent-recognition benchmarks show that PRIME maintains competitive clean-data performance while improving robustness under missing, noisy, conflicting, and modality-imbalanced conditions.