ProbeLog: Functional Probing & Model Retrieval

• ProbeLog is a methodology that creates logit-level descriptors to perform functional probing and zero-shot retrieval without relying on metadata.
• It employs affine normalization and an asymmetric top-k discrepancy to compare logits, ensuring functional specificity across diverse classifier architectures.
• Leveraging collaborative probing with matrix factorization, ProbeLog achieves up to threefold cost reduction in evaluation while maintaining high retrieval accuracy.

ProbeLog is a methodology and computational toolchain for functional probing, retrieval, and formal specification of models and event logs. The term appears in multiple technical contexts; the most prominent usage designates a mechanism for zero-shot discovery of pretrained model functionality, particularly classification capabilities, in the absence of metadata or training data. A separate formal methods strand relates ProbeLog to specification-based log analysis for safety-critical systems. The central innovation in the model search context is the association of each output dimension (logit) of a classifier with a distinct, functionally derived descriptor, enabling concept-based and text-based retrieval across vast model repositories. This article focuses on the computational and algorithmic underpinnings, retrieval regimes, and empirical performance characteristics of ProbeLog, with reference to its foundational presentation by Ringer et al. (Kahana et al., 13 Feb 2025).

1. Functional Probing and Logit-Level Descriptors

Traditional model search operates in weight space or relies on model/documentation metadata. ProbeLog replaces the monolithic representation of a model with a logit-level functional fingerprint. For a given classifier $f: X \to \mathbb{R}^k$ with $k$ output dimensions, ProbeLog designates a fixed probe gallery $X = \{x_1,\ldots,x_N\} \subset X$ (commonly, $N$ images from a broad dataset such as MS-COCO). For each output index $j$, the raw descriptor $d_j(f) \in \mathbb{R}^N$ comprises the scalar logit responses $[f_j(x_1),\ldots,f_j(x_N)]^T$ over the probe set.

Affine normalization is employed to place descriptors from disparate models into a comparable scale:

$$\mu_j = \frac{1}{N}\sum_{i=1}^N f_j(x_i), \qquad \sigma_j = \sqrt{\frac{1}{N}\sum_{i=1}^N (f_j(x_i) - \mu_j)^2}$$

The normalized descriptor is:

$$\tilde{d}_j(f) = \frac{d_j(f) - \mu_j \mathbf{1}_N}{\sigma_j}$$

This procedure confers invariance to unknown permutations, additions, or scale changes in output units, allowing meaningful comparison of class-specific function across heterogeneous classifier architectures (Kahana et al., 13 Feb 2025).

2. Retrieval Metrics and Asymmetric Top-k Discrepancy

Standard similarity metrics (e.g., Euclidean or cosine distance) on high-dimensional descriptors conflate signal from uninformative probes. ProbeLog addresses this via an asymmetric top-k discrepancy. Given two normalized descriptors $\tilde{d}_q$ (query) and $\tilde{d}_g$ (gallery), ProbeLog sorts $\tilde{d}_q$’s entries in descending order and selects only indices corresponding to its $k$ largest responses. Distance is then defined as:

$$D_k(\tilde{d}_q, \tilde{d}_g) = \left\| [\tilde{d}_q(a_1), \ldots, \tilde{d}_q(a_k)] - [\tilde{d}_g(a_1), \ldots, \tilde{d}_g(a_k)] \right\|_2^2$$

where $a_1, \ldots, a_k$ are indices of the top $k$ entries in $\tilde{d}_q$. This reflects the intuition that a logit should be distinguished by its strongest responses, allowing "functional specificity" without penalizing non-discriminative dimensions (Kahana et al., 13 Feb 2025).

3. Collaborative Probing and Scalability

Evaluating all $M$ logits across $N$ probes can be computationally prohibitive ($M \times N$ forward passes). ProbeLog introduces Collaborative Probing, an application of low-rank matrix completion. Rather than exhaustively probing every logit-probe pair, only a random subset (fraction $p < 1$) is sampled per model, yielding an incomplete response matrix $X \in \mathbb{R}^{M \times N}$. Matrix factorization (e.g., alternating least squares, truncated SVD) is used to estimate the missing entries, optimizing:

$\min_{U, V} \| M \odot (UV^T - X) \|_F^2$

where $M$ is the binary mask of observed entries and $\odot$ denotes element-wise product. This procedure enables descriptor construction with one-third the probing cost, with negligible empirical loss in retrieval accuracy (Kahana et al., 13 Feb 2025).

4. Retrieval Modalities: Logit-Based and Zero-Shot Text Queries

ProbeLog supports two retrieval paradigms:

• Logit-based retrieval ("more like this"): The descriptor of a known logit (e.g., "dog" class of reference model) is used as a query; gallery logits are ranked by $D_k$ to find functionally equivalent classes across models.
• Zero-shot text-based retrieval ("find all dogs"): The user inputs a text query (e.g., "Dog"). A pretrained image–text model (CLIP) embeds both the probes $x_i$ (via image encoder) and the query string (via text encoder) to yield $v_i, v_{\text{text}} \in \mathbb{R}^D$. The text-conditioned descriptor is $$s(c) = [v_1^T v_{\text{text}}, \ldots, v_N^T v_{\text{text}}]^T$$, normalized and compared to gallery descriptors using $D_k$. This enables direct discovery of corresponding logits—often for labels never seen by the repository models in their documentation (Kahana et al., 13 Feb 2025).

5. Empirical Evaluation and Benchmarking

ProbeLog was evaluated on two tasks: synthetic classifiers (INet-Hub, $M \approx 85\,000$ logits) and real-world classifiers from Hugging Face Hub (HF-Hub, $M \approx 400$ logits). Key metrics are Top-1 and Top-5 retrieval accuracy.

Retrieval Task	Top-1 Accuracy	Top-5 Accuracy	Baseline (model-level, Top-1)
Logit-based (INet→INet)	72.8% ± 0.2%	92.6% ± 0.1%	59.9% ± 0.2%
Cross-distribution (HF→INet)	40.6% ± 0.3%	58.6% ± 0.9%	13.9% ± 1.0%
Zero-shot text (INet-Hub)	43.8% ± 1.1%	68.0% ± 0.6%	≈0.1% (random)
Zero-shot text (HF-Hub)	34.0% ± 1.5%	53.7% ± 1.9%	≈0.1% (random)

Table: Retrieval accuracy (±95% confidence, $N=4000$ COCO probes).

Random and model-level baselines are near zero for zero-shot text alignment, indicating that ProbeLog's CLIP-based mapping provides a substantive advantage in unearthing mask-free, concept-level recognition capabilities (Kahana et al., 13 Feb 2025).

6. Architectural Advantages and Limitations

ProbeLog offers four core advantages over prior approaches:

1. Functional specificity: Each output dimension (logit) is described independently, conferring invariance to class permutations or additions.
2. Model-agnostic, zero-shot retrieval: Enables both "find more logits like this" and "find all logits corresponding to" via text, with no fine-tuning.
3. Collaborative probing for scalability: Reduces computational cost by 3× without accuracy degradation.
4. Lightweight descriptors: Normalized logit vectors ($N \ll$ total model parameters) enable efficient nearest-neighbor or angular-distance search, practical at million-logit scale.

Limitations are noted:

• The methodology targets discriminative classifiers with explicit, fixed-dimensional logits. Extension to generative models (e.g., diffusion or autoregressive architectures) remains nontrivial.
• Out-of-distribution (OOD) probe selection works for many concepts with COCO but domains such as medical imaging may require tailored probe sets.
• Collaborative Probing currently uses random sampling for probe selection; more adaptive or optimized probing strategies could further enhance efficiency (Kahana et al., 13 Feb 2025).

7. Future Directions

ProbeLog's current design is oriented toward large-scale, metadata-free classification model repositories. Promising future research avenues include:

• Extending probe-based functional fingerprinting to generative or multimodal architectures.
• Developing more intelligent, possibly coreset-based, strategies for probe selection.
• Adapting probe galleries to specific domain characteristics for non-natural image tasks (e.g., biomedical, satellite imagery).
• Scaling to repositories at the multi-million model scale, with further integration of approximate nearest neighbor frameworks (e.g., FAISS, DiskANN).
• Exploring hybrid schemes leveraging both learned and engineered probe sets for maximal discriminatory power (Kahana et al., 13 Feb 2025).

A plausible implication is that functional, probe-based descriptions—when combined with text-image alignment models—can form the foundation for highly generalized, domain-agnostic model discovery and auditing frameworks. This has significance not only for public model repositories but also for settings lacking reliable documentation or accessible training data.