YOLO with Kolmogorov-Arnold networks and vision-language foundation models for interpretable object detection with trustworthy multimodal AI in computer vision perception

arXiv:2603.23037 · cs.CV, cs.AI, cs.CL, cs.LG, cs.RO · Submitted 2026-03-24 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Next we'll be talking about the paper "YOLO with Kolmogorov-Arnold networks and vision-language foundation models for interpretable object detection with trustworthy multimodal AI in computer vision perception".

Jane: The paper was written by Marios Impraimakis, Daniel Vazquez and Feiyu Zhou from University of Bath and Zhejiang University, Hangzhou 310027, China.

Tom: Stay tuned as we take you through the paper and discuss its implications.

Jane: We also have Lu with us today — senior AI researcher at Tsinghua.

Tom: We also have Meng with us today — lead engineer at a mysterious AI startup.

Jane: We also have Lalam with us today — the in-house Large Language Model.

Tom: Alright, let's get started.

Title: Tom: We’re talking about this groundbreaking work titled "YOLO with Kolmogorov-Arnold networks and vision-language foundation models for interpretable object detection with trustworthy multimodal AI in computer vision perception," so what does that title tell us at a high level?

Jane: It tells us we are taking the speed and efficiency of YOLOv10, which is already great for real-time detection, but making it far more reliable by adding two major layers: trust through KAN and language through BLIP.

Lu: The title suggests a fundamental shift away from opaque systems, recognizing that we can model not just *what* the AI sees, but the very trustworthiness of its confidence score using complex mathematical frameworks like KAN.

Meng: And those seven key inputs mentioned in the paper—the bounding box geometry, class index, and others—are crucial because they suggest a holistic view is needed before calculating any single certainty level.

Lalam: This whole framework aims to provide a "detailed report" rather than just a raw output. It’s about giving the user an assessment of confidence based on specific features, adding that essential layer of accountability to the AI' that we use every day.

Tom: But it’s not just about providing data; the title also promises actionable insights into when it is unreliable, which is a huge step for safety-critical applications.

Jane: That ability to quantify uncertainty—to know exactly when the system needs extra caution—is a massive benefit for autonomous vehicles.

Lu: It’s essentially replacing "black box" systems with a verifiable, structured surrogate that allows us to trace the mathematical basis of why the decision being made is transparent.

Meng: That move towards interpretability makes this whole system much more appealing for certification and practical deployment in industries where we need accountability.

Lalam: The title promises a multimodal approach, so we’ are moving toward creating a comprehensive picture that goes beyond just delivering one single, trustworthy reading.

Improvements: Tom: Building on the core idea of trust and language, let's look at the specific technical improvements in "YOLO with Kolmogorov-Arnold networks and vision-language foundation models for interpretable object detection with trustworthy multimodal AI in computer vision perception." What are we gaining mechanically?

Jane: The addition of language via BLIP is a huge win because it means our output isn't just a coordinate on a screen; we get a descriptive caption, like "a large red truck driving across the main road," which provides immense contextual understanding.

Meng: I want to focus on the functional benefit of using additive spline-based structures within KAN. This is where the engineering gain lies—it’s not just theoretical window dressing, but a mechanism that improves performance measurably by creating a highly structured, auditable map.

Lu: The spline structure is precisely what grants that high degree of interpretability that we need for certification; it allows us to see the influence of each input feature smoothly and directly.

Lalam: When we pair that structural reliability from KAN with the descriptive power of BLIP, we get a synergy that really boosts trust by making the AI's reasoning understandable.

Tom: So, Jane’s point about context is amplified by Lu’s mathematical framework—we can see exactly how geometry and semantic features are being weighted.

Jane: Exactly, Tom. It’s like replacing a mystery machine with a clear blueprint that shows precisely where all the parts are working together to achieve the final confidence score for every single detection.

Lu: This is a powerful demonstration of applying complex mathematical theory to solve a real-world problem of confidence estimation in computer vision.

Meng: From an engineering standpoint, this means we can build systems that are inherently more robust because the KAN is designed to handle those subtle, non-linear interactions without becoming monolithic.

Lalam: This architecture shows our collective drive for trust is leading us toward genuinely useful forms of AI' that provide clear explanations alongside a powerful detection algorithm.

Architecture Interaction: Tom: We’ve established the components—KAN for trust and BLIP for context—but let's look at how they actually interact in this architecture, specifically how the visual data feeds into both the structured KAN model and the language foundation model.

Jane: Because BLIP is grounding that language in what it sees, it forces the entire pipeline to be more context-aware than before; it’s not just identifying objects, but describing *how* they relate to each other.

Meng: That relationship aspect is critical for real-world deployments. Knowing a pedestrian is near a curb provides crucial operational data that simple detection cannot provide, making the system much more useful for safety applications.

Lu: The KAN structure helps us formalize that relationship knowledge mathematically; it gives us a way to say the probability of accuracy increases because the language model confirms the scene context.

Lalam: This is a huge step toward building trustworthy AI' because if we need to troubleshoot, we don't just get an error code; we can trace back through the KAN structure to see which specific input feature was misleading it.

Tom: It’s a massive move towards building verifiable systems where you can actually point to the the part of the math that failed, instead of just saying "the machine said so."

Jane: The combination ensures that when an AI' makes a decision, we have both a precise numerical confidence and a human-readable explanation for its logic.

Lu: It’s a sophisticated demonstration of integrating statistical modeling with semantic understanding to solve the problem of reliable perception.

Meng: This synergy is exactly what we need to move from theoretical models into practical, scalable solutions that work reliably across different operational conditions.

Conclusion: Tom: So, we’ve seen how this whole system works to move beyond just fast detection and add layers of trust, which is a huge deal for safety-critical applications.

Jane: It really is about making sure that when an AI' gives us a prediction, we can actually understand the reliability behind it before accepting the result.

Lu: I think the concept of building a system where confidence itself becomes a measurable scientific variable rather than just an output of interest is what truly shifts our perspective on AI' development.

Meng: From my view, this framework is ready to move past theoretical models and into practical, real-world deployment in high-speed perception tasks that require rigorous certification.

Lalam: This work delivers a powerful tool for transparent and practical perception component for autonomous and multimodal artificial intelligence applications.

Tom: To wrap up our discussion on "YOLO with Kolmogorov-Arnold networks and vision-language foundation models for interpretable object detection with trustworthy multimodal AI in computer vision perception," we’ve seen how to bring together speed, reliability, and language.

Jane: It's a powerful demonstration of how the complex relationship between visual data and interpretability can be managed effectively.

Lu: The entire structure is designed to allow us to see the influence of every feature on the confidence score in a way that was previously unimaginable in deep learning models.

Meng: We have seen clear evidence that this approach allows for reliable detection, strengthening the transparency of modern automated systems we rely on daily.

Lalam: It’s truly exciting to see this multimodal AI' provide both the numerical evidence and the cultural context needed for a trustworthy future.

University of Bath · Zhejiang University, Hangzhou 310027, China

cs.CV, cs.AI, cs.CL, cs.LG, cs.RO

Submitted: 2026-03-24

Updated: 2026-09-04

Comments: 23 pages, 23 Figures, 9 Tables

Journal ref: Impraimakis, Marios, Daniel Vazquez, and Feiyu Zhou. Sci Rep 16, 27566 (2026)

DOI: 10.1038/s41598-026-57596-x

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 84/100

The gist: This paper introduces a novel framework designed to enhance the transparency and trustworthiness of standard object detection systems, specifically You Only Look Once (YOLOv10).

Key concepts

Kolmogorov-Arnold Networks (KAN): This mathematical framework allows the AI to model not just what it sees, but the trustworthiness of its confidence score. KAN provides a verifiable, structured surrogate that enables users to trace the mathematical basis for why a decision was made transparent.
BLIP/Vision-Language Models: This foundation model adds descriptive power by grounding language in visual input. Instead of just giving coordinates, it provides contextual understanding, such as describing 'a large red truck driving across the main road,' creating a comprehensive picture.
Interpretability and Trustworthy AI: The system is designed to replace opaque 'black box' systems with a clear blueprint. It allows for quantifying uncertainty and provides human-readable explanations for the logic behind an AI's prediction, ensuring accountability.

Terminology

Kolmogorov-Arnold Network — 43× in this paper
Interpretability — 31× in this paper · explained in 15 episodes
Foundation Model — 12× in this paper · explained in 3 episodes
Foundation Models — 6× in this paper · explained in 7 episodes
Fidelity — 5× in this paper · explained in 2 episodes
Core — 3× in this paper · explained in 2 episodes
Grounding — 3× in this paper · explained in 3 episodes
Loss Function — 3× in this paper · explained in 2 episodes
Accountability — 2× in this paper · explained in 2 episodes
Benchmark — 2× in this paper · explained in 2 episodes
Distillation — 2× in this paper · explained in 2 episodes
Explainability — 2× in this paper · explained in 3 episodes
Explainable AI (XAI) — 2× in this paper · explained in 7 episodes
Fine-Tuning — 2× in this paper · explained in 5 episodes
Robustness — 2× in this paper · explained in 5 episodes
Trace — 2× in this paper · explained in 2 episodes
Transformer — 2× in this paper · explained in 2 episodes
Accuracy — 1× in this paper · explained in 1 episode
Alignment — 1× in this paper · explained in 1 episode
Contextual Understanding — 1× in this paper · explained in 2 episodes
Data Augmentation — 1× in this paper · explained in 7 episodes
Embeddings — 1× in this paper · explained in 2 episodes
Ensemble Learning — 1× in this paper · explained in 2 episodes
Knowledge Graph — 1× in this paper · explained in 7 episodes
Knowledge graphs — 1× in this paper · explained in 2 episodes
Multimodal approach — 1× in this paper · explained in 2 episodes
Pruning — 1× in this paper · explained in 4 episodes
Reinforcement Learning — 1× in this paper · explained in 4 episodes
Semantic Understanding — 1× in this paper · explained in 2 episodes

Summary

This paper introduces a novel framework designed to enhance the transparency and trustworthiness of standard object detection systems, specifically You Only Look Once (YOLOv10). It addresses a critical limitation in computer vision perception—the lack of transparency regarding the reliability of confidence scores in visually degraded or ambiguous scenes. By coupling YOLOv10 with an interpretable surrogate model and a vision-language foundation model, the system delivers interpretable object detection and trustworthy multimodal AI, providing actionable insights for filtering, review, or downstream risk mitigation in applications such as autonomous vehicle perception.

How it works: The Detection Backbone (YOLOv10)

The process begins with YOLOv10, a one-stage object detector that performs real-time bounding-box prediction on an input image I in R H times W times 3. This model processes the image through a backbone and neck to generate dense predictions. For each detection, the system extracts seven key features that serve as inputs for the subsequent interpretability layer. These features include:

Normalized spatial position (x, y)
Normalized size (w, h)
Predicted confidence (conf)
Discrete class index (c)
Relative image scale (wh/640 squared)

How it works: The Interpretability Layer (KAN)

The core of the interpretability mechanism is a Kolmogorov-Arnold network (KAN), which acts as an interpretable post-hoc surrogate to model the trustworthiness of the YOLOv10 detections. KAN operationalizes this by replacing complex learned functions with trainable spline functions s j,k(times) at every connection between input feature k and hidden unit j. This structure enables a direct visualisation of each feature’s influence. The model output is obtained through a linear combination of these hidden-unit activations, resulting in smooth and transparent functional mappings that reveal when the model’s confidence is well supported or when it is unreliable.

How it works: The Multimodal Explanation (BLIP)

To provide complementary insight beyond numerical data, the framework integrates a bootstrapped language-image (BLIP) foundation model. This tool generates descriptive captions of each scene, enabling a lightweight multimodal interface without affecting the interpretability layer. This component provides natural language explanations that align model outputs with human linguistic reasoning.

How it works: The Unified Interpretability Pipeline

The resulting system forms a unified interpretability pipeline. YOLOv10 performs object detection, KAN provides a transparent surrogate to expose the structure of the model’s confidence function, and the visual-language model produces natural language descriptions. Experiments conducted on datasets such as COCO and images from the University of Bath demonstrate that this framework accurately identifies low-trust predictions under blur, occlusion, or low texture. This capability allows researchers to gain a clear view of how geometric and semantic features shape the reliability of a prediction, providing robust grounding for its internal coherence.

Improvements for AI systems

The integration of the Kolmogorov-Arnold Network (KAN) as a post-hoc surrogate and coupling it with a Vision-Language Foundation Model (BLIP) introduces three critical, high-value improvements to standard object detection systems: Trustworthiness Quantification, Structural Interpretability, and Multimodal Contextualization.

The Improvement: Standard YOLO models provide confidence scores (conf) without explaining their reliability. We replace the opaque decision surfaces of the detector with a Kolmogorov-Arnold Network (KAN) to create a transparent surrogate model. This KAN takes seven interpretable features from the detection head (normalized bounding-box position x, y; normalized size w, h; predicted confidence conf; discrete class index cls; and relative image scale scale) and provides a smooth, mathematically verifiable mapping of the system's reliability.

What the Improved AI System Can Do:

Determine Reliability in Ambiguity: The system can accurately identify low-trust predictions (i.e., when confidence is unreliable) under challenging conditions such as severe blur, occlusion, or low texture in real-world sensor data (e.g., detecting a vehicle obscured by heavy rain).
Actively Flag Edge Cases: Instead of simply reporting a prediction, the the system flags detections where the underlying geometric or semantic features are insufficient to support the predicted confidence level.

The Improvement: The KAN structure itself is designed to be an interpretable surrogate, replacing complex nonlinear functions with simple, trainable univariate spline functions (psi p,q). This allows us to analyze the internal mechanics of the confidence prediction in a way traditional neural networks do not allow.

The Improvement: We integrate a Bootstrapped Language-Image Pretraining (BLIP) foundation model into the pipeline to generate descriptive captions of the entire scene, independent of the detection results.

The resulting system provides Interpretable Object Detection with Trustworthy Confidence Estimates. It moves beyond simply being an accurate detector; it becomes a self-aware perception component that can:

Detect objects (YOLOv10).
Explain how it determined the confidence of those detections (KAN).
Describe what the scene looks like to provide contextual grounding (BLIP).

Abstract

The trustworthy object detection capabilities of a novel Kolmogorov-Arnold network framework are examined here. The approach addresses a key limitation in computer vision for vehicle detection perception, and beyond. These systems offer limited transparency regarding the reliability of their confidence scores in visually degraded or ambiguous scenes. To this end, a Kolmogorov-Arnold network is employed as an interpretable post-hoc surrogate to model the trustworthiness of the You Only Look Once (Yolov10) detections using seven geometric and semantic features. The additive spline-based structure of the Kolmogorov-Arnold network enables direct visualisation of each feature's influence. This produces smooth and transparent functional mappings that reveal when the model's confidence is well supported and when it is unreliable. Furthermore, a bootstrapped language-image (BLIP) foundation model generates descriptive captions of each scene. This tool enables a lightweight multimodal interface without affecting the interpretability layer. Experiments on both Common Objects in Context (COCO), and images from the University of Bath campus demonstrate that the framework accurately identifies low-trust predictions under blur, occlusion, or low texture. This provides actionable insights for acceptance, review, or downstream risk mitigation. The resulting system delivers interpretable object detection with trustworthy confidence estimates. It offers a powerful tool for transparent and practical perception component for autonomous and multimodal artificial intelligence applications.