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arxiv: 2604.13694 · v1 · submitted 2026-04-15 · 💻 cs.AI

Recognition: unknown

Weight Patching: Toward Source-Level Mechanistic Localization in LLMs

Chenghao Sun , Chengsheng Zhang , Guanzheng Qin , Rui Dai , Xinmei Tian
Authors on Pith no claims yet

Pith reviewed 2026-05-10 13:27 UTC · model grok-4.3

classification 💻 cs.AI
keywords weight patchingmechanistic interpretabilitylarge language modelsparameter interventionmodel merginginstruction followingsource localizationcausal modules
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The pith

Weight Patching swaps weights from specialized models into base models to localize where capabilities are stored in parameters.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Mechanistic interpretability often tracks activations, yet modules that show strong effects may only amplify signals rather than hold the original capability in their own weights. The paper introduces Weight Patching, which replaces selected module weights from a behavior-specialized model into a base model while holding inputs fixed. This isolates source modules that encode the capability directly. Applied to instruction following, the approach uncovers a hierarchy of shallow carriers, aggregation and routing modules, and downstream execution circuits. The same scores also improve selective fusion when merging expert models.

Core claim

Weight Patching replaces weights of chosen modules from a behavior-specialized model into a base model under fixed inputs. This intervention reveals that target capabilities reside in specific parameter sets. Analysis on instruction following identifies a hierarchy from shallow candidate source-side carriers to aggregation and routing modules and onward to downstream execution circuits. The recovered importance scores further enable mechanism-aware model merging that improves selective fusion across expert combinations.

What carries the argument

Weight Patching, a parameter-space intervention that copies weights from a specialized model into corresponding modules of a base model to test which modules carry the target capability.

If this is right

  • Capabilities can be measured and ranked by the effect of direct weight replacement rather than activation tracing alone.
  • A consistent hierarchy appears in which early modules supply initial signals, mid-level modules aggregate and route, and later modules execute the behavior.
  • Component scores derived from patching improve the quality of selective model merging by favoring compatible experts.
  • The vector-anchor interface supplies a shared internal criterion for detecting whether a task-relevant control state has formed during open-ended generation.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the approach generalizes, it could support precise parameter-level edits that strengthen or remove individual behaviors without retraining the full model.
  • Pairing weight patching with activation-based methods would likely produce a fuller causal map than either technique alone.
  • Applying the same procedure to other model families or capabilities could test whether the observed source-to-execution hierarchy is a general pattern.
  • Importance scores from Weight Patching might also flag minimal module sets whose removal or replacement alters only the target behavior.

Load-bearing premise

Paired same-architecture models differ mainly in how strongly they express the target capability, so weight replacement isolates the source modules without other confounding side effects.

What would settle it

If weight patches on the identified modules produce no greater improvement in the target behavior than patches on randomly chosen modules of equal size, the localization claim would be false.

Figures

Figures reproduced from arXiv: 2604.13694 by Chenghao Sun, Chengsheng Zhang, Guanzheng Qin, Rui Dai, Xinmei Tian.

Figure 1
Figure 1. Figure 1: Weight Patching versus standard Activation Patching. Stan [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Mechanistic summary of instruction following under the pro [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of the proposed framework. We first extract an instruction-direction anchor as a shared behavioral interface, then apply [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Reusing WP-recovered component scores for mechanism [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Layer-wise recovery under task-vector injection. Injecting the [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Component-importance heatmaps under Activation Patching [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Layer distributions and overlaps of top-ranked heads and [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Causal validation of upstream (top row) and downstream (bottom row) modules on the English Capital task. The results support a [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Fine-grained comparison of upstream and downstream neu [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: MLP submodule ablation for neuron localization. (a) Overlap [PITH_FULL_IMAGE:figures/full_fig_p008_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Selective attention of critical heads to instruction tokens. [PITH_FULL_IMAGE:figures/full_fig_p009_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Cross-task ablation and restoration across six IFEval tasks. [PITH_FULL_IMAGE:figures/full_fig_p009_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Overlap of critical modules across tasks. Intersection counts [PITH_FULL_IMAGE:figures/full_fig_p009_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Cross-task specificity of restored upstream neurons. Rows [PITH_FULL_IMAGE:figures/full_fig_p010_14.png] view at source ↗
Figure 16
Figure 16. Figure 16: Instruction-following recovery via injection of localized critical [PITH_FULL_IMAGE:figures/full_fig_p010_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Layer-wise steering correction rates for Llama-3.1-8B and Llama-2-13B. This figure complements [PITH_FULL_IMAGE:figures/full_fig_p025_17.png] view at source ↗
Figure 20
Figure 20. Figure 20: Heatmaps of component importance under activation patch [PITH_FULL_IMAGE:figures/full_fig_p025_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Layer distributions and overlaps of top-ranked components [PITH_FULL_IMAGE:figures/full_fig_p025_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Causal validation of Llama-3.2-3B upstream and downstream modules on the Title task. B.5 Ranking, Validation, and Component Selection Rules AAP and WAP are used directly for interpretability anal￾ysis and module localization, rather than as a prelimi￾nary screening step followed by manual top-k curation. In contrast, exact Weight Patching is substantially more expensive, and is therefore applied mainly at… view at source ↗
Figure 23
Figure 23. Figure 23: Causal validation of Llama-3.1-8B upstream and downstream modules on the Title task [PITH_FULL_IMAGE:figures/full_fig_p028_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: Causal validation of Llama-2-13B upstream and downstream modules on the Title task. 0 5 10 15 20 Top-K 0.0 0.1 0.2 0.3 0.4 Accuracy Head Knockout 0 5 10 15 20 Top-K 0.1 0.2 0.3 Head Restore 0 200 400 600 800 1000 Top-K 0.0 0.1 0.2 0.3 0.4 Neuron Knockout 0 200 400 600 800 1000 Top-K 0.1 0.2 0.3 Neuron Restore Activation Patching Weight Patching Random Select [PITH_FULL_IMAGE:figures/full_fig_p028_24.png] view at source ↗
Figure 25
Figure 25. Figure 25: Causal validation of Gemma2-2B global modules on the English Capital task. scale models simultaneously, the state dictionaries of all expert models are loaded sequentially onto CPU memory, and the model objects are immediately garbage-collected to minimize peak RAM usage. The checkpoints are initially loaded in half-precision (FP16). During the layer-wise fine￾grained fusion, the weight slices are tempora… view at source ↗
Figure 26
Figure 26. Figure 26: Causal validation of Mistral-7B-v0.3 global modules on the English Capital task. highest correction rates. To verify that this intermediate representation is a consistent mechanistic property rather than an artifact of a specific model scale, [PITH_FULL_IMAGE:figures/full_fig_p029_26.png] view at source ↗
Figure 27
Figure 27. Figure 27: Causal validation of Qwen2.5-3B global modules on the English Capital task [PITH_FULL_IMAGE:figures/full_fig_p030_27.png] view at source ↗
Figure 28
Figure 28. Figure 28: Vocabulary projection results of downstream neurons in the [PITH_FULL_IMAGE:figures/full_fig_p030_28.png] view at source ↗
Figure 30
Figure 30. Figure 30: Overlap of critical modules across tasks on [PITH_FULL_IMAGE:figures/full_fig_p030_30.png] view at source ↗
Figure 29
Figure 29. Figure 29: Selective attention of critical heads to instruction tokens. [PITH_FULL_IMAGE:figures/full_fig_p030_29.png] view at source ↗
Figure 32
Figure 32. Figure 32: Layer-wise parameter changes induced by instruction tuning [PITH_FULL_IMAGE:figures/full_fig_p031_32.png] view at source ↗
Figure 34
Figure 34. Figure 34: Absolute performance comparison (IFEval strict accuracy) across three model scales: Llama-3.2-3B, Llama-3.1-8B, and Llama-2-13B. [PITH_FULL_IMAGE:figures/full_fig_p032_34.png] view at source ↗
Figure 35
Figure 35. Figure 35: Case study of Llama-3.2-3B on the Englist Capital task. across all layers without the upstream/downstream split. The results demonstrate a remarkably consistent functional dichotomy across all three architectures: Taken together, these supplementary evaluations con￾firm that the fundamental mechanistic organization iden￾tified in the main text—where sparse MLP neurons act as the genuine parameter-level so… view at source ↗
Figure 36
Figure 36. Figure 36: Case study of Llama-3.2-3B on the Title task. As illustrated in [PITH_FULL_IMAGE:figures/full_fig_p034_36.png] view at source ↗
Figure 37
Figure 37. Figure 37: Heatmaps comparing component localization across distinct [PITH_FULL_IMAGE:figures/full_fig_p035_37.png] view at source ↗
read the original abstract

Mechanistic interpretability seeks to localize model behavior to the internal components that causally realize it. Prior work has advanced activation-space localization and causal tracing, but modules that appear important in activation space may merely aggregate or amplify upstream signals rather than encode the target capability in their own parameters. To address this gap, we propose Weight Patching, a parameter-space intervention method for source-oriented analysis in paired same-architecture models that differ in how strongly they express a target capability under the inputs of interest. Given a base model and a behavior-specialized counterpart, Weight Patching replaces selected module weights from the specialized model into the base model under a fixed input. We instantiate the method on instruction following and introduce a framework centered on a vector-anchor behavioral interface that provides a shared internal criterion for whether a task-relevant control state has been formed or recovered in open-ended generation. Under this framework, the analysis reveals a hierarchy from shallow candidate source-side carriers to aggregation and routing modules, and further to downstream execution circuits. The recovered component scores can also guide mechanism-aware model merging, improving selective fusion across the evaluated expert combinations and providing additional external validation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The paper proposes Weight Patching, a parameter-space intervention technique for source-level mechanistic localization in LLMs. Using paired same-architecture models that differ in the strength of a target capability (instantiated on instruction following), it replaces selected module weights from the specialized model into the base model under fixed inputs. A vector-anchor behavioral interface provides an internal criterion for task-relevant control states. The analysis identifies a hierarchy of components (shallow candidate source-side carriers, followed by aggregation and routing modules, then downstream execution circuits) and applies the recovered scores to improve mechanism-aware model merging.

Significance. If the central assumption holds and the intervention cleanly isolates source components, Weight Patching would usefully extend mechanistic interpretability beyond activation-space methods by directly targeting parameter-level carriers. The reported hierarchy and its use for selective model merging supply an external validation signal that could inform both theory and practical editing techniques.

major comments (3)
  1. [Abstract] Abstract: the claim that weight patching reveals a hierarchy from shallow carriers to aggregation/routing modules to execution circuits is presented without any quantitative results, ablation studies, error bars, or statistical validation, so it is impossible to determine whether the data support the localization claims.
  2. [Method] Method description (abstract): the load-bearing assumption that the paired models differ primarily in target-capability strength (so that weight replacement under fixed inputs isolates source modules without confounding side effects) receives no explicit controls demonstrating that non-target behaviors remain statistically unchanged post-patching; absent such checks the observed hierarchy could reflect intervention-induced routing changes rather than genuine source localization.
  3. [Experiments] Experiments (abstract): the statement that component scores guide improved model merging supplies no metrics, baseline comparisons, or ablation results on the evaluated expert combinations, preventing assessment of whether the external validation is robust.
minor comments (1)
  1. [Abstract] The vector-anchor behavioral interface is introduced without a concise definition or reference to its construction, which may hinder readers' ability to evaluate the shared internal criterion.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive feedback. We address each major comment below with clarifications drawn from the full manuscript and indicate where we will revise the presentation for greater clarity and completeness.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that weight patching reveals a hierarchy from shallow carriers to aggregation/routing modules to execution circuits is presented without any quantitative results, ablation studies, error bars, or statistical validation, so it is impossible to determine whether the data support the localization claims.

    Authors: The full manuscript reports quantitative support for the hierarchy, including per-module recovery rates (e.g., higher recovery for shallow candidate source modules than for downstream execution circuits), ablation studies that isolate the contribution of each layer type, error bars computed over multiple runs, and statistical tests confirming significant differences between component classes. The abstract summarizes these findings at a high level. We will revise the abstract to include representative quantitative metrics, mention the ablation and statistical validation procedures, and add a brief reference to the error analysis. revision: yes

  2. Referee: [Method] Method description (abstract): the load-bearing assumption that the paired models differ primarily in target-capability strength (so that weight replacement under fixed inputs isolates source modules without confounding side effects) receives no explicit controls demonstrating that non-target behaviors remain statistically unchanged post-patching; absent such checks the observed hierarchy could reflect intervention-induced routing changes rather than genuine source localization.

    Authors: The manuscript already evaluates non-target behaviors (general knowledge, safety, and unrelated reasoning tasks) after patching and reports that performance remains statistically indistinguishable from the base model (deviations < 2 % with p > 0.1). To address the concern directly, we will expand the Methods section with a dedicated paragraph that explicitly tabulates these controls, describes the statistical tests applied, and discusses why the observed hierarchy is unlikely to arise from routing artifacts alone. revision: yes

  3. Referee: [Experiments] Experiments (abstract): the statement that component scores guide improved model merging supplies no metrics, baseline comparisons, or ablation results on the evaluated expert combinations, preventing assessment of whether the external validation is robust.

    Authors: The full paper presents concrete merging results: using the recovered component scores yields a 12 % absolute improvement on the combined instruction-following benchmark relative to standard parameter averaging, with ablations showing that score-guided selection outperforms both random and magnitude-based selection. We will revise the abstract to report these metrics, name the baselines, and note the ablation outcomes so that the external validation is immediately visible. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation grounded in external interventions and behavioral criteria

full rationale

The paper proposes Weight Patching as an empirical intervention that replaces module weights between paired same-architecture models differing in target capability strength, then evaluates outcomes via a newly introduced vector-anchor behavioral interface that serves as an external, shared criterion for control-state recovery in generation. The reported hierarchy (shallow carriers to aggregation/routing to execution circuits) is presented as an observed outcome of applying this method to instruction following, not as a mathematical derivation or prediction that reduces to fitted parameters by construction. No self-citations, uniqueness theorems, or ansatzes are invoked to justify core claims; component scores are further validated externally via model merging improvements. The chain remains self-contained against the behavioral interface and paired-model differences rather than tautological.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The approach rests on the assumption that paired models differ mainly in target capability expression and that the new vector-anchor interface reliably measures internal control states; no free parameters or invented physical entities are described.

axioms (2)
  • domain assumption Paired models share identical architecture and differ primarily in the strength of target capability expression under the inputs of interest.
    Required for weight replacement to isolate source components rather than introduce architectural mismatches.
  • domain assumption The vector-anchor behavioral interface supplies a shared, reliable criterion for whether a task-relevant control state has been formed or recovered.
    Central to the evaluation framework described in the abstract.
invented entities (1)
  • vector-anchor behavioral interface no independent evidence
    purpose: Provides a shared internal criterion for task-relevant control state formation or recovery during open-ended generation.
    Introduced as part of the analysis framework; no independent evidence outside the paper is mentioned.

pith-pipeline@v0.9.0 · 5505 in / 1474 out tokens · 49673 ms · 2026-05-10T13:27:42.292502+00:00 · methodology

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