Seeing is Believing: How Multimodal AI Prioritizes Sensory Input

Author: Denis Avetisyan

New research reveals that even the most advanced multimodal AI systems exhibit a strong bias towards visual and textual information, impacting their reasoning abilities when data conflicts.

Long-context interference demonstrably degrades the performance of multimodal large language models, as evidenced by decreasing accuracy across both visual and audio prompts when presented with irrelevant preceding text.

This review investigates modality alignment and proposes a fine-tuning strategy to enhance cross-modal grounding and robustness in Multimodal Large Language Models.

Despite recent advances in multimodal artificial intelligence, current models often exhibit surprisingly brittle reasoning when faced with conflicting sensory inputs. This work, ‘Some Modalities are More Equal Than Others: Decoding and Architecting Multimodal Integration in MLLMs’, introduces a comprehensive benchmark and interpretability analysis revealing a strong reliance on visual and textual modalities within Multimodal Large Language Models (MLLMs). We demonstrate that these models struggle with semantic misalignment across modalities, highlighting a critical gap in robust cross-modal reasoning. Can we engineer MLLMs to achieve truly balanced integration, effectively prioritizing and leveraging information from all available sensory streams?

The Fragile Harmony of Multimodal Understanding

Multimodal Large Language Models (MLLMs) represent a significant leap toward artificial intelligence capable of processing information from multiple sources – text, images, and audio – to achieve a more comprehensive understanding of the world. However, this potential is currently hampered by a fundamental challenge: inconsistencies arising when these different modalities present conflicting information. While designed to integrate these inputs, MLLMs often struggle to reconcile discrepancies, leading to outputs that prioritize one modality over others or generate responses that don’t logically align with all available evidence. This issue isn’t merely a technical glitch; it strikes at the heart of truly integrated multimodal reasoning, hindering the development of AI systems that can reliably interpret and synthesize information from diverse sources to form coherent and accurate conclusions.

Current Multimodal Large Language Models (MLLMs) frequently exhibit a pronounced textual bias, meaning they disproportionately favor information presented in text format even when it contradicts data from other modalities like images or audio. Studies reveal this isn’t simply a preference, but a systemic tendency where textual inputs often override conflicting visual or auditory cues during processing. This reliance suggests MLLMs aren’t truly integrating multimodal information, but rather translating other modalities into textual representations and then prioritizing the original text. Consequently, a visual depiction of a red apple might be described as green if the accompanying text states it is green, highlighting a failure in cross-modal consistency and raising concerns about the reliability of reasoning in complex scenarios where modalities disagree.

Semantic misalignment poses a critical hurdle for multimodal large language models, frequently resulting in the generation of hallucinations – instances where the model confidently asserts information not supported by the provided inputs. This disconnect arises because the model struggles to consistently integrate information across different modalities, like text and images; a visual cue indicating one outcome might be overridden by textual information suggesting another. Consequently, the model’s reasoning capabilities become unreliable, as it prioritizes textual data even when it contradicts other sensory inputs, leading to internally inconsistent and demonstrably false conclusions. This inability to achieve true cross-modal understanding severely limits the potential of MLLMs to perform complex tasks requiring coherent interpretation of diverse information streams.

Despite robust visual reasoning, auditory reasoning significantly degrades when presented with conflicting video and audio, demonstrating a consistent visual dominance bias across various model architectures.

Cultivating Harmony: Aligning Sensory Input

Current research investigates methods to improve modality alignment within Multimodal Large Language Models (MLLMs), which refers to the process of effectively integrating information derived from disparate input sources such as text, images, and audio. This enhancement aims to address limitations in how MLLMs correlate and synthesize data across modalities, leading to more accurate and consistent outputs. Approaches focus on strengthening the connections between feature representations extracted from each modality, allowing the model to better understand the relationships between different types of input and generate responses grounded in the combined information. Improved modality alignment is critical for tasks requiring cross-modal reasoning, such as visual question answering and image captioning, as it directly impacts the model’s ability to accurately interpret and utilize all available data.

AutoSteer, MC2, and Arrow-of-Time represent training methodologies specifically designed to enhance modality alignment in Multimodal Large Language Models (MLLMs). AutoSteer operates by steering the attention mechanisms during training to focus on relevant cross-modal features. MC2 (Multimodal Contrastive Consistency) introduces a consistency objective, encouraging the model to produce similar representations for semantically equivalent information presented across different modalities. Arrow-of-Time focuses on enforcing temporal consistency between modalities, particularly in video-text applications, by penalizing inconsistencies in representations across time steps. These techniques directly address misalignment by manipulating the training process to prioritize and reinforce coherent cross-modal understanding.

Distillation frameworks for modality alignment, including Bridging Ears and Eyes, operate by transferring knowledge from a larger, pre-trained multi-modal model to a smaller student model. These frameworks specifically focus on aligning the encoder representations of different modalities – such as text and images – by minimizing the distance between their feature embeddings. This alignment process encourages the student model to develop a shared understanding of the information present in each modality, improving its ability to accurately ground language in visual or auditory contexts. The resulting cross-modal grounding enhancements facilitate improved performance on tasks requiring reasoning across multiple input types.

Recent research indicates that targeted fine-tuning procedures specifically designed to enhance modality alignment yield substantial improvements in grounding performance for Multimodal Large Language Models (MLLMs). These methods move beyond general pre-training by introducing objectives and data that directly address the correspondence between different input modalities, such as image and text. Empirical results demonstrate that focusing on alignment during fine-tuning reduces instances of cross-modal misalignment – where the model incorrectly associates information from different sources – leading to more accurate and reliable cross-modal reasoning and improved performance on tasks requiring integrated understanding of multiple modalities.

Semantic misalignment between video and audio significantly degrades performance across both visual-only and audio-only prompting conditions.

A Rigorous Examination: The MMA-Bench

MMA-Bench is a benchmark designed to systematically assess the robustness of Multimodal Large Language Models (MLLMs) when faced with variations in input modalities and inconsistencies between them. The benchmark achieves this by introducing perturbations-such as missing modalities or misaligned data-and then evaluating the model’s ability to maintain performance on downstream tasks. This systematic probing allows researchers to identify specific failure modes and weaknesses in MLLM architectures and alignment strategies. The evaluation framework includes a diverse set of tasks and perturbation types, providing a comprehensive assessment of model resilience beyond standard performance metrics.

MMA-Bench facilitates the evaluation of Multimodal Large Language Model (MLLM) performance under conditions of missing or misaligned input modalities. This benchmark employs a systematic methodology to assess model resilience by introducing perturbations to the input data, specifically examining scenarios where visual, auditory, or textual information is absent or inconsistent. The Qwen2.5-Omni model is utilized as a primary testbed within MMA-Bench, allowing researchers to quantify the impact of these modality disruptions on downstream task accuracy and identify areas for improvement in MLLM robustness. The benchmark provides a standardized framework for comparing different MLLMs and alignment techniques based on their ability to maintain performance despite modality challenges.

Evaluation using the MMA-Bench benchmark indicates ongoing challenges for Multimodal Large Language Models (MLLMs) in scenarios involving modality perturbations and misalignment. Specifically, performance degradation is observed when input modalities are incomplete or present inconsistent information. Analysis of model responses on MMA-Bench tasks reveals difficulties in maintaining consistent reasoning and accurate output generation under these conditions. These findings emphasize the necessity for continued research into robust alignment techniques that improve MLLM resilience and ensure reliable performance across diverse and potentially noisy input data. Further development is needed to address limitations in the models’ ability to effectively integrate and interpret information from multiple modalities, particularly when faced with ambiguous or conflicting signals.

Fine-tuning the Qwen2.5-Omni-7B model with a modality-aware approach yielded a 90.27% accuracy rate in zero-shot abstention when presented with visually ambiguous inputs. This performance metric indicates the model’s ability to correctly identify and refrain from answering questions when the visual input lacks sufficient clarity or contains conflicting information. This represents a significant improvement over baseline models lacking this specialized training, demonstrating enhanced robustness to problematic visual data and a reduced tendency to generate potentially inaccurate responses based on insufficient evidence. The abstention rate was evaluated on a held-out dataset of visually ambiguous prompts.

Performance evaluations utilizing the AVHBench benchmark demonstrate an 8.2% improvement following the implementation of modality-aware fine-tuning on Qwen2.5-Omni-7B. This gain indicates enhanced generalization capabilities and increased robustness against inconsistencies present in audio-visual data. AVHBench specifically assesses a model’s ability to correctly identify actions and events when presented with potentially mismatched or misleading audio and visual cues, making this performance increase a quantifiable metric of improved multi-modal reasoning.

Attention heatmaps reveal that Qwen2.5-Omni relies heavily on textual priors, with the majority of attention focused on textual tokens at layer 28, which significantly influences its performance.

Beyond Correction: Cultivating Reliable Abstention

Current research indicates that addressing the issue of hallucination in large language models doesn’t necessarily require extensive and expensive retraining processes. Innovative decoding schemes, notably AVCD (Adaptive Vector Creation and Decomposition) and Fork-Merge, present a compelling alternative. These techniques operate by refining how a model generates text, rather than altering the model’s core knowledge. AVCD, for example, strategically manages the creation and selection of potential word sequences, while Fork-Merge explores multiple reasoning paths before committing to a final answer. By intelligently navigating the decoding process, these schemes effectively constrain the model’s output, reducing the probability of generating factually unsupported or nonsensical content. This approach offers a practical pathway towards more reliable and trustworthy language models, minimizing computational costs and enabling easier adaptation to new data and tasks.

Multimodal large language models (MLLMs) are increasingly capable, but a persistent challenge remains: confidently generating incorrect or misleading information – a phenomenon known as hallucination. Recent strategies focus on enabling zero-shot abstention, a process by which the model learns to identify when a query falls outside its knowledge boundaries and refrains from answering. This isn’t simply about admitting ignorance; it’s about developing a robust internal assessment of informational sufficiency. By empowering MLLMs to recognize the limits of their understanding, researchers aim to prevent the propagation of false claims and foster more reliable interactions. The benefit lies in shifting the model’s behavior from attempting an answer at all costs, to prioritizing accuracy through informed refusal, ultimately enhancing trustworthiness and mitigating the risks associated with confidently incorrect responses.

Chain-of-Thought prompting represents a powerful strategy for enhancing the reasoning abilities of large multimodal models and, consequently, diminishing the incidence of unsupported content generation. This technique encourages the model to articulate its thought process – to break down complex questions into a series of intermediate steps – before arriving at a final answer. By explicitly outlining its reasoning, the model becomes more transparent and allows for easier identification of potential flaws or inaccuracies in its logic. This deliberate approach not only improves the overall quality of responses but also significantly reduces the likelihood of the model confidently presenting information that isn’t logically supported by the provided context, offering a crucial step towards building more reliable and trustworthy artificial intelligence systems.

Recent advancements in large language models demonstrate a significant leap in the ability to abstain from answering when lacking sufficient information, a crucial step in mitigating the pervasive issue of hallucination. Specifically, a fine-tuned version of the Qwen2.5-Omni-7B model achieved a remarkable 90.27% accuracy in zero-shot abstention – meaning it correctly identifies questions it cannot confidently answer without any prior training on such scenarios. This represents a substantial improvement over baseline models, which exhibited considerably lower accuracy rates of 10.94% and 15.05% respectively. The heightened capacity for abstention directly translates to a notable reduction in confidently incorrect responses, positioning this fine-tuned model as a promising solution for building more reliable and trustworthy artificial intelligence systems.

Despite a noted decrease in accuracy concerning semantic misalignment when utilizing Chain-of-Thought prompting – registering at 58.46% – the fine-tuned Qwen2.5-Omni-7B model demonstrably maintains a high level of overall accuracy at 88.14%. This outcome underscores the effectiveness of the implemented fine-tuning strategy in bolstering the model’s ability to discern and avoid generating responses that, while syntactically correct, lack meaningful coherence with the provided context. The preserved accuracy suggests that the fine-tuning process successfully prioritized factual grounding and logical consistency, even when faced with the complexities introduced by CoT reasoning, effectively mitigating the risk of confidently delivering misleading or unsubstantiated information.

Our model consistently avoids the hallucinated predictions common in the baseline by demonstrating robust grounding in the requested sensory input.

The study illuminates a critical aspect of MLLM design: the inherent asymmetry in how these models process diverse sensory inputs. It’s not simply about integrating modalities, but about ensuring harmonious interaction, much like a well-tuned orchestra. This echoes Fei-Fei Li’s sentiment: “AI is not about replacing humans; it’s about augmenting human capabilities.” The research demonstrates that when modalities conflict, MLLMs disproportionately rely on visual and textual cues, revealing a lack of true cross-modal grounding. Achieving genuine intelligence, therefore, requires a delicate balance, where each modality contributes meaningfully to the overall understanding, allowing the interface to ‘sing’ when elements harmonize rather than shout over each other.

Beyond the Echo Chamber

The observed predilection of Multimodal Large Language Models for vision and text – a comfortable echo of the datasets upon which they feast – is less a revelation than a confirmation of existing biases. The study rightly exposes the fragility of ‘reasoning’ when sensory inputs conflict, but the question lingers: can true cross-modal understanding ever emerge from a system fundamentally built on correlation rather than genuine integration? Consistency is empathy; a robust MLLM should not simply process diverse data, but mediate between them, flagging discordance not as error, but as potentially valuable information.

Future work must move beyond simply improving performance on existing benchmarks. These evaluations, while useful, risk becoming a form of elaborate self-congratulation. The real challenge lies in crafting scenarios that actively test for semantic misalignment – not by presenting ambiguous data, but by deliberately introducing contradiction. Beauty does not distract, it guides attention; a truly elegant MLLM will not smooth over inconsistencies, but highlight them, forcing a more nuanced and – dare one say – honest interpretation of the world.

Ultimately, the pursuit of robust multimodal understanding demands a shift in perspective. The goal is not to build machines that mimic human reasoning, but to create systems that reveal the limitations of that very process. Perhaps, in acknowledging the inherent messiness of perception, we can begin to design AI that is not just intelligent, but also – and crucially – aware.

Original article: https://arxiv.org/pdf/2511.22826.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

The Fragile Harmony of Multimodal Understanding

Cultivating Harmony: Aligning Sensory Input

A Rigorous Examination: The MMA-Bench

Beyond Correction: Cultivating Reliable Abstention

Beyond the Echo Chamber

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