Keeping Video AI Grounded in Reality

Author: Denis Avetisyan

New research addresses the problem of ‘temporal hallucination’ – where video AI generates events that didn’t actually happen – with a novel approach to training these complex systems.

The system addresses video understanding by contrasting original video representations with newly introduced spatial and temporal negatives, achieving both spatial and temporal faithfulness through a “Temporal Homogenization” technique that introduces ambiguity while preserving spatial semantics and a “Self-Diagnostic Mechanism” which uses attention divergence to compute adaptive weights and penalize hallucinations during decoding.

SEASON leverages self-diagnosis and contrastive decoding to improve temporal faithfulness in Video Large Language Models without requiring retraining.

Despite recent advances in video understanding, Large Language Models for video struggle with temporal consistency, often generating descriptions of events that lack plausible causal links. This work introduces SEASON-Self-Diagnostic Contrastive Decoding-a training-free method designed to mitigate temporal hallucination in these models. By dynamically diagnosing each token’s propensity for error and contrasting it with challenging temporal and spatial negatives, SEASON enhances both the faithfulness and coherence of video descriptions. Could this approach unlock more reliable and insightful video understanding capabilities for a broader range of applications?

The Illusion of Understanding: Confronting Hallucination in Multimodal AI

Despite their burgeoning abilities to process and integrate visual information, multimodal large language models (MLLMs) frequently exhibit a disconcerting tendency toward hallucination – the generation of textual content that contradicts the provided visual evidence. This isn’t simply a matter of minor inaccuracies; MLLMs can confidently describe objects or events not present in an image, fabricate details about visible elements, or misattribute actions to the wrong actors. The core of the issue lies in the models’ reliance on learned statistical associations rather than a true understanding of visual reality, leading them to ‘fill in the gaps’ with plausible but ultimately false information. This propensity for hallucination significantly erodes trust in MLLM outputs and poses a substantial hurdle to their deployment in applications demanding factual accuracy, such as medical diagnosis, autonomous navigation, or reliable data analysis.

The propensity of Multimodal Large Language Models (MLLMs) to generate outputs detached from presented visual data poses a significant threat to their real-world utility, particularly in fields demanding precision and reliability. When these models fabricate details or misrepresent observed content, it erodes confidence in their outputs, hindering adoption in applications like medical diagnosis, autonomous navigation, and legal analysis. A hallucination – an assertion unsupported by the visual input – can lead to incorrect decisions with potentially serious consequences, thus necessitating robust methods for detecting and mitigating these inconsistencies before MLLMs can be safely deployed in critical domains where accuracy is paramount and trust is non-negotiable.

The challenge of hallucinations in multimodal large language models is significantly complicated by the ways in which these errors manifest; inconsistencies aren’t limited to simply what an MLLM describes, but also where and when those descriptions occur within a visual scene. A model might accurately identify objects but misplace them relative to one another – a chair floating mid-air, for example – representing a spatial inconsistency. More subtly, temporal inconsistencies can arise when a model incorrectly sequences events depicted in a video, or attributes actions to the wrong moment in time. These interwoven errors, spanning both space and time, demand sophisticated solutions beyond simple fact-checking; developers must address the model’s underlying reasoning about the relationships between visual elements and their dynamic context, a task proving far more complex than anticipated.

SEASON improves temporal faithfulness in video LLMs by addressing limitations of existing methods like DINO-HEAL, which prioritizes spatial saliency over temporal order, and TCD, which contrasts frames without considering causal relationships.

Resourceful Mitigation: Training-Free Approaches to Hallucination Control

Current research efforts are increasingly directed towards training-free hallucination mitigation techniques as a response to the substantial computational cost and data requirements associated with full model retraining. These methods aim to reduce the generation of inaccurate or nonsensical content without updating model weights, offering a more accessible and efficient solution for deployment in resource-constrained environments. This approach is particularly relevant given the large parameter sizes of contemporary large language and vision-language models, where retraining can be prohibitively expensive and time-consuming. The focus is on modifying decoding strategies or utilizing existing model features to improve output fidelity without altering the core model parameters.

DINO-HEAL, a training-free hallucination mitigation technique, operates by utilizing saliency maps to guide the generative process. These maps, derived from a pre-trained visual transformer, identify and highlight the most visually salient regions within an input image or video frame. By weighting the attention mechanism of the large language model (LLM) towards these salient features during text generation, DINO-HEAL effectively suppresses the generation of details not supported by the visual input. This attention weighting is achieved through a modification of the cross-attention layers, increasing the importance of tokens corresponding to salient regions and diminishing the influence of irrelevant or spurious features. Consequently, the LLM is encouraged to ground its textual output in the observed visual content, thereby reducing the occurrence of hallucinations and improving the faithfulness of the generated text.

Token Contrastive Decoding (TCD) operates on the principle of minimizing hallucination by introducing a contrastive loss during decoding. This method generates two sets of predictions: one from the original video and another from a series of frame-dropped versions of the same video. The core innovation lies in maximizing the likelihood of tokens predicted from the original video while simultaneously minimizing the likelihood of tokens predicted from the frame-dropped videos. This contrastive approach encourages the model to focus on temporally consistent features and suppresses the generation of content not supported by the consistent visual stream, effectively reducing hallucinatory outputs. The severity of frame-dropping serves as a hyperparameter controlling the strength of the contrastive penalty.

Applying TCD, DINO-HEAL, and SEASON with LLaVA-OV-7B to the VidHalluc dataset demonstrates varying trade-offs between accuracy and latency.

SEASON: A Framework for Self-Diagnosis and Corrective Action

SEASON is a training-free framework designed to improve the faithfulness of Video Large Language Models (VideoLLMs). It achieves this by integrating two core components: temporal homogenization and a self-diagnostic mechanism. Temporal homogenization intentionally disrupts spurious correlations within video sequences, exposing a model’s propensity to generate hallucinatory content. The self-diagnostic component then quantifies these tendencies by calculating Jensen-Shannon Divergence between frame-attention distributions, providing a measurable assessment of hallucination levels without requiring labeled data. This combined approach allows SEASON to identify and subsequently mitigate hallucinations, improving the overall reliability of VideoLLM outputs.

Temporal homogenization is a technique used to identify spurious correlations within video data that can lead to hallucinations in Video Large Language Models (VideoLLMs). By disrupting the natural temporal order of video frames during processing, the method reveals whether the model relies on genuine content understanding or simply exploits predictable, but ultimately irrelevant, temporal patterns. Specifically, if a model’s predictions remain consistent even with randomized frame sequences, it indicates a dependence on these spurious correlations rather than actual video content, thus exposing its propensity to hallucinate details not present in the original video.

SEASON employs a self-diagnostic mechanism to quantify hallucination tendencies in VideoLLMs by analyzing divergences in frame-attention distributions. This mechanism utilizes the Jensen-Shannon Divergence ($JSD$) to measure the dissimilarity between attention maps of consecutive frames; higher $JSD$ values indicate greater inconsistency and thus a higher likelihood of hallucination. Specifically, the system calculates $JSD$ between the attention distributions of each frame and a reference frame, providing a quantitative score reflecting the degree to which the model’s focus shifts unrealistically over time. This allows for the identification of frames where the model deviates from grounded visual information, enabling targeted corrective measures.

SEASON employs Contrastive Decoding as a corrective mechanism to mitigate hallucinated content in VideoLLMs. This technique actively adjusts the decoding process to favor responses aligned with the input video frames, thereby reducing inaccuracies. When integrated with the Qwen2.5-VL-7B model, SEASON achieves a measurable performance gain of +5.3% on the VidHalluc benchmark, indicating a statistically significant improvement in faithfulness and a reduction in hallucinated details compared to the baseline model without the corrective decoding process.

Quantitative evaluation of SEASON reveals significant performance gains on established benchmarks. Utilizing the LLaVA-OV-7B model, SEASON achieved a 24.5% improvement on the Temporal Storytelling Hallucination (TSH) subtask of the VidHalluc benchmark. Furthermore, when paired with the LLaVA-Video-7B model, SEASON demonstrated a 1.4% increase in TempCompass Accuracy, a metric specifically designed to assess temporal consistency in video understanding. These results indicate SEASON’s effectiveness in mitigating hallucination and improving the reliability of VideoLLMs across diverse evaluation criteria.

SEASON’s self-diagnostic mechanism uses temporal weights to ensure sequence accuracy for crucial tokens like “B” and “first”, while employing spatial weights for object- and interaction-based tokens such as “placing butter” and “hand”, as demonstrated in example video sequences.

Establishing Ground Truth: Rigorous Benchmarking and Evaluation

Objective assessment of hallucination reduction in video-based large language models requires rigorous, standardized benchmarks. Datasets like VidHalluc, EventHallusion, and VideoHallucer offer precisely this, providing controlled environments to evaluate a model’s tendency to generate content inconsistent with the input video. These benchmarks move beyond subjective evaluation by quantifying hallucinations – instances where a model fabricates details or misinterprets visual information – through specific metrics. This allows researchers to compare the effectiveness of different hallucination reduction techniques on a common playing field, fostering faster progress and more reliable advancements in the field of video understanding and generation. Without such benchmarks, improvements would be difficult to verify and reproduce, hindering the development of trustworthy and accurate video-based AI systems.

The development of standardized benchmarks represents a pivotal advancement in the effort to mitigate hallucinations in video-based Large Language Models (LLMs). Prior to these benchmarks – including VidHalluc, EventHallusion, and VideoHallucer – evaluating and comparing different hallucination reduction techniques proved largely subjective and inconsistent. Now, researchers possess a shared foundation for objectively measuring performance improvements, fostering a more rigorous and accelerated pace of progress. These benchmarks don’t simply report scores; they establish a common language for the field, enabling direct comparisons between novel approaches and providing a clear trajectory for future research directions. This standardized evaluation is crucial not only for academic advancement but also for practical deployment, ensuring that improvements translate into more reliable and trustworthy video understanding systems.

Rigorous evaluation of hallucination reduction techniques relies on the utilization of openly available Video Large Language Models (VideoLLMs). Specifically, research employs models like LLaVA-OV-7B, LLaVA-Video-7B, and Qwen2.5-VL-7B, fostering a transparent and collaborative research environment. This commitment to open-source tools is not merely a matter of cost; it fundamentally enables reproducibility, allowing other researchers to independently verify findings and build upon existing work. By providing readily accessible model weights and code, the field avoids the limitations imposed by proprietary systems and accelerates the development of more reliable and trustworthy video understanding technologies. This approach ensures that progress in hallucination reduction is not confined to a select few, but rather benefits the entire community.

Beyond automated metrics, the refinement of VideoLLM behavior increasingly leverages the power of human discernment through Preference Optimization and Reinforcement Learning. These techniques move beyond simply identifying factual errors; they actively shape model responses to align with nuanced human expectations for detail, relevance, and overall quality. By presenting human evaluators with multiple response options, the system learns to prioritize outputs preferred by people, effectively rewarding desirable behaviors and discouraging hallucinated content. This iterative process, driven by human feedback as a reward signal, allows models to not only correct inaccuracies but also to anticipate and avoid generating them in the first place, leading to a more trustworthy and engaging user experience. The result is a subtle yet significant improvement in the model’s ability to deliver coherent and factually grounded video descriptions.

An ablation study reveals that SEASON's performance on the VidHalluc benchmark is consistently robust regardless of which attention layers are selected for aggregation. — An ablation study reveals that SEASON’s performance on the VidHalluc benchmark is consistently robust regardless of which attention layers are selected for aggregation.

The pursuit of temporal faithfulness, as demonstrated by SEASON, echoes a fundamental principle of elegant design. The method’s self-diagnostic contrastive decoding doesn’t simply correct errors; it refines the model’s understanding of video sequences, creating a harmonious interplay between spatial and temporal information. As David Marr observed, “A function is defined by its behavior, not by its internal workings.” SEASON embodies this sentiment; the elegance lies not in complex architecture, but in the method’s ability to produce temporally coherent outputs. The system’s adaptive contrastive learning serves to subtly guide the model towards a more faithful representation, ensuring the interface ‘sings’ with a natural, unbroken flow, even when challenged by difficult video data.

The Horizon Beckons

The pursuit of temporal faithfulness in video understanding feels, at present, less like solving a problem and more like peeling back layers of an onion. SEASON’s approach – leveraging self-diagnosis to refine contrastive learning – represents a worthwhile step, yet it implicitly acknowledges a deeper truth: current VideoLLMs still largely construct narratives, rather than discern them. The elegance of the method lies in its training-free nature, a signal that true progress demands less brute force and more refined perception. However, the generated ‘challenging negatives’ are, by necessity, synthetic. A compelling next step involves incorporating genuine, naturally occurring temporal distortions – the hesitations, repetitions, and reframings inherent in real-world video – as adversarial examples.

The paper’s focus on contrastive decoding addresses what the model says, but less so why it says it. A truly robust system will not simply avoid hallucination; it will offer a degree of epistemic humility – a signal of its own uncertainty. This requires moving beyond superficial faithfulness and toward a deeper understanding of causality and intention within video content. The current architecture treats time as a linear sequence; a more nuanced approach might explore cyclical or hierarchical temporal relationships.

Ultimately, the goal is not to create models that mimic human perception, but to surpass it. Yet, beauty scales, clutter does not. The field risks being bogged down by increasingly complex architectures if it fails to prioritize foundational principles of simplicity, clarity, and, above all, a rigorous understanding of what constitutes genuine temporal coherence.

Original article: https://arxiv.org/pdf/2512.04643.pdf

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

The Illusion of Understanding: Confronting Hallucination in Multimodal AI

Resourceful Mitigation: Training-Free Approaches to Hallucination Control

SEASON: A Framework for Self-Diagnosis and Corrective Action

Establishing Ground Truth: Rigorous Benchmarking and Evaluation

The Horizon Beckons

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