Abstract

The explosive growth of open-source model repositories has created a Model Jungle, where checkpoints are frequently shared without adequate documentation or metadata. While weight-space learning offers a pathway to identify and analyze these models directly from their parameters, processing full-scale weights is computationally prohibitive. Probing-based methods have emerged as a lightweight alternative, extracting permutation-equivariant representations via learnable probe vectors. However, existing probing methods are limited by a single-view design: they capture first-order structures but fail to encode the rich, higher-order correlation patterns inherent in row-column interactions. To bridge this gap, we introduce MVProbe, a multi-perspective probing framework that synthesizes first-order signals with interaction-aware (Gram-based) views. Our approach is theoretically grounded; We analyze the scaling laws of different probing orders to derive a principled standardization and fusion strategy that ensures balanced contributions from all branches. On the Model Jungle benchmark, MVProbe consistently outperforms the state-of-the-art ProbeX across diverse architectures, including discriminative backbones (ResNet, SupViT, MAE, DINO) and large-scale generative LoRA adapters (Stable Diffusion LoRA).

Not every AI model comes with a manual. As open-source model repositories continue to grow, many checkpoints are shared with little information about how they were trained or what data they learned from. As a result, identifying suitable models for reuse—or tracing where they came from—is becoming increasingly difficult.

To address this challenge, Professor Jaejun Yoo and his team at the UNIST Graduate School of Artificial Intelligence developed MVProbe (Multi-view Probe), a method that identifies what an AI model has learned by analyzing its internal parameters alone, without relying on metadata or executing the model itself.

Conventional probing methods analyze model weights from only a single perspective, limiting their ability to distinguish between closely related models. MVProbe overcomes this limitation by examining neural network parameters from four complementary perspectives, capturing not only individual parameter values but also the relationships among them to build a more complete representation of a model.

The researchers also showed why existing probing methods struggle to distinguish similar models, demonstrating mathematically that important information embedded in model parameters is overlooked when weights are analyzed from only a single perspective. By integrating multiple complementary views, MVProbe overcomes this limitation while remaining reliable regardless of which neural network layer is analyzed.

The team evaluated MVProbe on the Model Jungle benchmark, a large collection of modern AI models spanning image recognition networks and large generative models. Across all evaluated architectures, the method consistently outperformed the previous state-of-the-art approach, improving identification accuracy by as much as 5.96 percentage points while maintaining stable performance across different network layers.

Beyond making large model repositories easier to search and organize, MVProbe could support a wide range of applications, including model reuse, provenance analysis, copyright verification, and AI security auditing—all without requiring models to be executed.

“As more pretrained AI models become publicly available, the ability to identify and reuse the right one is becoming increasingly important,” said Professor Yoo. "MVProbe makes it possible to search and manage large model repositories by reading the information already encoded in a model's parameters, without running the model itself."

The study was co-first authored by Eunwoo Heo and Kyungguk Seo of the UNIST Graduate School of Artificial Intelligence. It has been accepted for presentation at the International Conference on Machine Learning (ICML) 2026. This work was supported by the Institute for Information & Communications Technology Planning & Evaluation (IITP) and the National Research Foundation of Korea (NRF).

Journal Reference

Eunwoo Heo, Kyeongkook Seo, and Jaejun Yoo, "What Linear Probes Miss: Multi-View Probing for Weight-Space Learning,"