Patchscope: Unified LLM & Dynamic Language Scoping

• Patchscope is a dual-framework that defines a 5-tuple abstraction for both LLM interpretability and controlled extension methods in dynamic languages.
• It enables modular intervention using techniques like vocabulary projection, causal tracing, cross-model patching, and multihop reasoning to improve token precision and error correction.
• In dynamic languages, Patchscope employs lexical activation and hierarchy-first selection to safely manage extension methods while minimizing unintended method overrides.

Patchscope refers to two distinct but structurally analogous frameworks: (1) a rigorous mechanism for controlling the scope and override risk of extension methods in dynamically-typed languages, and (2) a unified formalism for inspecting and intervening in internal representations of LLMs. This dual usage is documented in the programming languages literature (Polito et al., 2017) and machine learning interpretability (Ghandeharioun et al., 11 Jan 2024). Both share foundational themes of modularity, explicit scope control, and protection against unintended interactions.

1. Formal Definition in LLM Interpretability

Patchscope in LLM interpretability is a 5-tuple abstraction:

$(T,\,M^\ast,\,\ell^\ast,\,i^\ast,\,f_\theta)$

where:

• $M$: source LLM, $L$: layers in $M$
• $S = \langle s_1,\dots,s_n \rangle$: source prompt, $h^\ell_i \in \mathbb{R}^d$: hidden state at layer $\ell$, token position $i$
• $M^\ast$: target (possibly different) LLM, $T = \langle t_1,\dots,t_m \rangle$: target prompt, $$h^{\ast,\ell^\ast}_{i^\ast} \in \mathbb{R}^{d^\ast}$$
• $f_\theta: \mathbb{R}^d \to \mathbb{R}^{d^\ast}$: mapping or transformation The algorithm:

1. Forward pass on $M$ using $S$, extract $h^\ell_i$
2. Transform via $f_\theta$, yielding $z \in \mathbb{R}^{d^\ast}$
3. Forward pass on $M^\ast$ using $T$ up to layer $\ell^\ast$
4. Overwrite $h^{\ast,\ell^\ast}_{i^\ast} := z$
5. Complete forward pass, generate outputs (tokens, text, probabilities, features)

This generalizes causal intervention (patching) within and across transformer models for interpretability tasks such as identifying token identity, extracting attributes, or characterizing error pathways (Ghandeharioun et al., 11 Jan 2024).

2. Unified Framework and Its Encompassed Methods

Patchscope subsumes a spectrum of LLM interpretability techniques:

• Vocabulary-space projection: Logit Lens, Tuned Lens as $M^\ast = M$, $T = S$, $\ell^\ast = L$, $f_\theta$ affine; outputs softmax$(U f_\theta(h^\ell_i))$ where $U$ is the unembedding matrix.
• Future-Lens: $T \neq S$, $\ell^\ast < L$, patching to query for future tokens.
• Causal tracing / attention-knockout: $f_\theta(x) = 0$ or noise, patching at various layers.
• Probing classifiers: $M^\ast$ is a classifier; $f_\theta$ is a trained linear map to discrete labels.

These prior methods vary only in their specification of $(T, M^\ast, \ell^\ast, i^\ast, f_\theta)$ (Ghandeharioun et al., 11 Jan 2024), establishing Patchscope as a unifying interpretability formalism.

3. Addressing Limitations in Previous Methods

Empirical and procedural limitations of legacy approaches are mitigated as follows:

• Early Layer Failure: Logit Lens and similar projections demonstrate poor accuracy for $\ell \lesssim 5$; Patchscope overcomes this by permitting free-form natural language decoding starting from early layers (e.g., $\ell \approx 3$–$5$), with robust token identification and description extraction.
• Expressivity and Training Constraints: Whereas traditional probes and projections are limited to fixed-label or vocabulary decoding and often require extensive labeled training data, Patchscopes achieve zero- or few-shot performance, open-vocabulary response generation, and natural language explanations without additional gradient updates.

Quantitative results: Few-shot token-ID Patchscope achieves up to +98% Precision@1 compared to Logit/Tuned Lens from layer 10 onward; zero-shot Patchscope outperforms logistic regression probing on 6/12 commonsense/factual tasks ($p < 10^{-5}$) (Ghandeharioun et al., 11 Jan 2024).

4. Extended Applications and Experimental Protocols

Patchscope enables advanced operations:

• Cross-model patching: Using a calibrated affine $f_\theta$ between model families (e.g., Vicuna 7B $\to$ 13B), entity descriptions can be decoded using a larger model, yielding Precision@1 $\approx 0.7$–$0.8$ for next-token prediction and improved RougeL similarity for entity resolutions.
• Multihop reasoning error correction ("CoT Patchscope"): Circuit patching allows surgical transfer of intermediate-step representations between sites (e.g., step-1 answer $\to$ step-2 query), with accuracy increasing from 19.6% $\to$ 50% on held-out two-hop queries.

Key protocol metrics include Precision@1, surprisal, exact-match entity extraction within 20 tokens, and RougeL/1/SBERT similarity for description tasks (Ghandeharioun et al., 11 Jan 2024).

5. Patchscope in Dynamically-Typed Programming Languages

Patchscope, as originally described in (Polito et al., 2017), synthesizes mechanisms from Ruby Refinements, Groovy Categories, Classboxes, and Method Shelters, designed to:

• Use lexical activation (as in Ruby refinements), restricting extension methods' visibility to the definition-site context, not the call stack.
• Employ hierarchy-first selection in method lookup, scanning up the class hierarchy for extension method definitions on a per-extension-group basis.
• Allow protected/hidden extension groups, preventing override of critical methods (cf. Method Shelters' hidden chambers).

The formal model:

• Active extensions: $$activeExts_{PS}(\rho) = imports(\rho_1) \cdot \langle e_{global} \rangle$$
• Lookup: $$lookup_{PS}(c,s,\rho) = select_{hrc}(c,s,activeExts_{PS}(\rho))$$ where $select_{hrc}$ scans class hierarchy for defined methods in lexical imports only.

Safety and Efficiency: Accidental Override Space (AOS) is minimized:

$$AOS_{PS} = (|superclasses(c_{def})| + |subclasses(c_{def})| + 1)\cdot (i-1)$$

No stack walk is required, and per-lookup cost is $O(|imports(\rho_1)| + \text{depth}(C))$.

Trade-offs synthesized:

• Minimal accidental override risk (hierarchy-first selection)
• No runtime stack-inspection (lexical activation)
• Fine-grained control over protected extension methods
• All expressiveness of prior approaches is retained (Polito et al., 2017)

6. Comparative Summary and Thematic Connections

Patchscope, across both LLM interpretability and dynamic language method scoping, exemplifies modular intervention, precise scope definition, and robust protection against unintended information flows or collisions. In LLMs, it formalizes intervention and readout for probing hidden states, unifying existing methods and supporting new natural-language, cross-model, and multihop reasoning tasks (Ghandeharioun et al., 11 Jan 2024). In programming languages, it provides a compositional, protection-aware extension method mechanism based on lexical activation and hierarchy-oriented selection, yielding superior safety and efficiency profiles compared to legacy “local rebinding” systems (Polito et al., 2017).

The underlying architectural principles—modular composition, localized scope, override minimization, and expressiveness—enable Patchscope to function as a unifying abstraction relevant to both interpretability and extensible software design.