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arxiv: 2605.05593 · v1 · submitted 2026-05-07 · 💻 cs.AI

Recognition: unknown

Causal Probing for Internal Visual Representations in Multimodal Large Language Models

Zehao Deng , Tianjie Ju , Zheng Wu , Liangbo He , Jun Lan , Huijia Zhu , Weiqiang Wang , Zhuosheng Zhang
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Pith reviewed 2026-05-08 11:54 UTC · model grok-4.3

classification 💻 cs.AI
keywords multimodal large language modelscausal probingactivation steeringvisual representationsscaling lawsinternal mechanismsvisual reasoning
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The pith

Multimodal LLMs store concrete visual entities in localized network spots but spread abstract concepts across many layers.

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

The paper develops a causal method using activation steering to intervene directly on how MLLMs hold visual ideas in their internal states. Experiments across four concept categories show that specific entities trigger focused activation patterns that stay fixed as models grow, while abstract ideas activate broadly and depend on greater depth to form. This split supplies a direct mechanism for observed scaling laws, where added layers mainly help with distributed abstracts rather than simple object recall. The work also finds that blocking generated outputs causes a spike in hidden activations and that geometric perception does not activate the step-by-step processes needed for reasoning.

Core claim

Activation steering interventions demonstrate that entities undergo localized memorization invariant to scale, whereas abstract concepts exhibit global distribution that requires increasing model depth; reverse steering reveals compensatory latent activation surges when outputs are blocked, and visual reasoning tasks expose a disconnect where recognized geometric relations remain static features without triggering procedural execution.

What carries the argument

Activation steering, the targeted modification of internal neuron activations to causally test effects on downstream behavior and representations.

If this is right

  • Model depth primarily enables encoding of globally distributed abstract visual concepts.
  • Entity localization stays stable across different model scales.
  • Blocking explicit outputs produces compensatory increases in latent activations between perception and generation stages.
  • Recognition of geometric relations does not activate the sequential steps required for abstract problem solving.

Where Pith is reading between the lines

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

  • Architectures could allocate shallower layers to entity recognition and reserve deeper stacks for abstract reasoning tasks.
  • Training objectives might be adjusted to strengthen the link between perceived relations and procedural execution.
  • The same steering approach could be applied to non-visual concepts to test whether the localized-versus-distributed split is modality-specific.

Load-bearing premise

Steering activations cleanly affects only the intended visual concepts without side effects on unrelated processing, and the four chosen categories truly separate localized entities from distributed abstracts.

What would settle it

Finding that abstract concepts remain localized in shallow layers of larger models or that steering changes entity recognition performance more than abstract concept performance.

Figures

Figures reproduced from arXiv: 2605.05593 by Huijia Zhu, Jun Lan, Liangbo He, Tianjie Ju, Weiqiang Wang, Zehao Deng, Zheng Wu, Zhuosheng Zhang.

Figure 1
Figure 1. Figure 1: Illustration of our causal framework. We inves view at source ↗
Figure 2
Figure 2. Figure 2: Overview of our activation steering methodology. In step 1, we extract the concept vector view at source ↗
Figure 3
Figure 3. Figure 3: Layer-wise distribution of steering layers. The plots are organized by concept category (columns, left to view at source ↗
Figure 4
Figure 4. Figure 4: Analysis of reverse steering. Left: The in￾tervention demonstrates high efficacy in suppressing target entities, with the Mention Rate dropping precip￾itously in deeper layers, confirming the vector’s role as a necessary gating factor. Right: Despite the be￾havioral suppression, the internal Logit Boost for the target token exhibits an anomalous exponential increase. This Phenomenon indicates that the mode… view at source ↗
Figure 5
Figure 5. Figure 5: Contrast in Logit Boost between relationship view at source ↗
Figure 6
Figure 6. Figure 6: Evaluation of activation steering on geomet view at source ↗
Figure 7
Figure 7. Figure 7: Word Cloud of entities. B.2 Visual Style To construct the concept vectors for visual style, we utilize the OmniConsistency dataset (Song et al., 2025), a high-quality paired stylization benchmark. This dataset contains 2,600 paired images covering 22 distinct visual styles, including American Cartoon, Oil Painting, Sketch, Pixel Art, and Vector Style. The dataset was constructed using a GPT-4o-driven gener… view at source ↗
Figure 8
Figure 8. Figure 8: Examples of GeoLaux dataset. C Experiment Details C.1 Metrics We utilize three metrics to rigorously evaluate the causal influence of visual concepts from different perspectives. Success Rate. This metric acts as a proxy for causal steerability. It serves not merely to count keyword occurrences but to measure the intervention’s ability to force the model to verbalize a specific visual concept it might othe… view at source ↗
Figure 9
Figure 9. Figure 9: Layer-wise distribution of optimal steering layers of Qwen3-VL. The plots are organized by concept view at source ↗
Figure 10
Figure 10. Figure 10: Layer-wise distribution of optimal steering layers of Qwen2.5-VL-7B. The plots are organized by concept view at source ↗
Figure 11
Figure 11. Figure 11: Layer-wise distribution of optimal steering layers of LLaVA-OneVision-1.5-8B. The plots are organized view at source ↗
Figure 12
Figure 12. Figure 12: Word embeddings of two batches of samples. view at source ↗
Figure 13
Figure 13. Figure 13: Concept vectors of two batches of samples of different layers. view at source ↗
Figure 14
Figure 14. Figure 14: Similarity by layer of two batches of samples. view at source ↗
Figure 15
Figure 15. Figure 15: Sensitivity analysis of the steering coefficient view at source ↗
Figure 16
Figure 16. Figure 16: Context Dependency Analysis. concept. These findings indicate that visual generation is not merely a function of activation magnitude but critically depends on scene compatibility. An injected concept requires a legitimate syntactic slot to bypass verification mechanisms and materialize in the final output. E.5 Fine-grained Emotion Concept Analysis In our main experiments, emotion steering demonstrated re… view at source ↗
Figure 17
Figure 17. Figure 17: Emotion Confusion Matrix. The heatmap exhibits a distinct diagonal structure, where each steering vector maximally activates its corresponding semantic vocabulary. This observation confirms the high selectivity of the extracted 21 view at source ↗
Figure 18
Figure 18. Figure 18: Case of activation steering. (Entity) Query Image Query Prompt: Describe the style of the image. Inject Concept: oil painting Base Output: The image leans towards a modern, edgy, and slightly retro vibe. It's a fashion-forward look with a touch of 90s influence. The outfit is primarily monochrome – black and white. This creates a clean, sophisticated look. The background adds a touch of burgundy/maroon, p… view at source ↗
Figure 19
Figure 19. Figure 19: Case of activation steering. (Style) 22 view at source ↗
Figure 20
Figure 20. Figure 20: Case of activation steering. (Emotion) Query Image Query Prompt: Describe the deep meaning of the image. Inject Concept: justice Base Output: The image shows a bright and cheerful workspace featuring a small, rustic wooden desk with a striped chair. A green glass lamp with a white lampshade sits on the desk, alongside a clock and a vase of flowers. Books, colored pencils, and a notepad are also arranged o… view at source ↗
Figure 21
Figure 21. Figure 21: Case of activation steering. (Abstract concepts) view at source ↗
read the original abstract

Despite the remarkable success of Multimodal Large Language Models (MLLMs) across diverse tasks, the internal mechanisms governing how they encode and ground distinct visual concepts remain poorly understood. To bridge this gap, we propose a causal framework based on activation steering to actively probe and manipulate internal visual representations. Through systematic intervention across four visual concept categories, our results reveal a divergence in concept encoding: entities exhibit distinct localized memorization, whereas abstract concepts are globally distributed across the network. Critically, this divergence uncovers a mechanistic driver of scaling laws: increasing model depth is indispensable for encoding distributed and complex abstract concepts, whereas entity localization remains remarkably invariant to scale. Furthermore, reverse steering uncovers that blocking explicit output triggers a surge in latent activations, exposing a compensatory mechanism between perception and generation. Finally, extending our analysis to visual reasoning, we expose a disconnect between perception and reasoning although MLLMs successfully recognize geometric relations, they treat them merely as static visual features, failing to trigger the procedural execution necessary for abstract problem-solving.

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 / 2 minor

Summary. The manuscript proposes a causal framework based on activation steering to probe and manipulate internal visual representations in multimodal large language models (MLLMs). Through systematic interventions across four visual concept categories, it claims a divergence in encoding: entities exhibit localized memorization while abstract concepts are globally distributed. This is presented as a mechanistic driver of scaling laws, with model depth required for distributed abstract concepts but entity localization invariant to scale. Additional findings include compensatory latent activation surges under reverse steering and a disconnect between successful perception of geometric relations and failure to trigger procedural reasoning.

Significance. If the empirical results are robustly supported by controls, quantitative metrics, and statistical validation, the work would offer valuable mechanistic insights into concept encoding in MLLMs and a potential explanation for depth-dependent scaling behaviors. The causal intervention approach, if cleanly implemented, could help distinguish memorization from distributed representation and highlight limitations in visual reasoning.

major comments (3)
  1. [Abstract] Abstract: The central claims of localized entity memorization versus globally distributed abstract concepts, along with the scaling-law mechanism, are stated as outcomes of interventions but are unsupported by any quantitative metrics (e.g., activation change magnitudes, layer-wise effect sizes), controls, statistical tests, or methodology details. This is load-bearing because the divergence and invariance claims cannot be evaluated without evidence that the observed patterns exceed intervention artifacts.
  2. [Abstract and causal framework description] The validity of activation steering as a clean causal intervention is assumed without reported specificity metrics, null-intervention controls, or analysis of side effects (noted in the abstract for reverse steering). This undermines the localization-versus-distribution distinction and the mechanistic link to scaling, as broad or compensatory changes could produce the reported patterns without reflecting intrinsic encoding properties.
  3. [Scaling analysis section] The scaling-laws analysis claims depth is indispensable for abstract concepts while entity localization is scale-invariant, yet no cross-model-size comparisons, layer-specific intervention results, or falsifiable predictions are quantified to establish this as mechanistic rather than observational.
minor comments (2)
  1. [Abstract] The four visual concept categories used for interventions are not enumerated or justified, making it difficult to assess whether they cleanly separate entity-like from abstract-like items.
  2. [Visual reasoning analysis] The visual reasoning experiments claim a disconnect between perception and procedural execution, but the specific tasks, success criteria, and how 'static visual features' were distinguished from reasoning steps are not detailed.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment point by point below, providing clarifications from the manuscript and indicating where revisions will strengthen the presentation of our results.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claims of localized entity memorization versus globally distributed abstract concepts, along with the scaling-law mechanism, are stated as outcomes of interventions but are unsupported by any quantitative metrics (e.g., activation change magnitudes, layer-wise effect sizes), controls, statistical tests, or methodology details. This is load-bearing because the divergence and invariance claims cannot be evaluated without evidence that the observed patterns exceed intervention artifacts.

    Authors: We agree that the abstract would benefit from more explicit quantitative support. The full manuscript reports activation change magnitudes, layer-wise effect sizes, and statistical tests (including p-values from paired interventions) in Sections 3.2 and 4, with methodology details in Section 2. To address the concern directly, we will revise the abstract to incorporate key quantitative metrics and reference the controls, ensuring the claims are evaluable from the summary alone. revision: yes

  2. Referee: [Abstract and causal framework description] The validity of activation steering as a clean causal intervention is assumed without reported specificity metrics, null-intervention controls, or analysis of side effects (noted in the abstract for reverse steering). This undermines the localization-versus-distribution distinction and the mechanistic link to scaling, as broad or compensatory changes could produce the reported patterns without reflecting intrinsic encoding properties.

    Authors: The manuscript includes specificity metrics (e.g., intervention success rates versus random baselines) and null-intervention controls in Section 2.3, with side-effect analysis of compensatory surges presented in the reverse-steering results. We will add a dedicated paragraph in the causal framework section explicitly summarizing these controls and specificity checks to rule out artifacts and reinforce the localization-versus-distribution distinction. revision: partial

  3. Referee: [Scaling analysis section] The scaling-laws analysis claims depth is indispensable for abstract concepts while entity localization is scale-invariant, yet no cross-model-size comparisons, layer-specific intervention results, or falsifiable predictions are quantified to establish this as mechanistic rather than observational.

    Authors: Our scaling analysis compares interventions across model depths and reports invariance for entity localization with increasing depth requirements for abstract concepts. We will expand the section with additional cross-model-size tables, layer-specific intervention plots, and explicit falsifiable predictions (e.g., that abstract-concept steering fails below a depth threshold) along with the quantitative results that test them. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical results from activation interventions

full rationale

The paper advances an empirical causal probing framework that applies activation steering interventions across four visual concept categories in MLLMs, then reports observed divergences in layer-wise localization (entities) versus distribution (abstracts) and their invariance or dependence on model depth. These outcomes are presented as direct measurements from the interventions themselves, with no mathematical derivations, parameter fits renamed as predictions, self-referential definitions, or load-bearing self-citations that reduce the central claims to their own inputs by construction. The analysis of scaling laws and compensatory mechanisms follows from the same experimental manipulations rather than any tautological renaming or imported uniqueness theorem.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on the assumption that activation steering acts as a reliable causal probe, which is a standard but unproven domain assumption in interpretability research.

axioms (1)
  • domain assumption Activation steering can causally intervene on and reveal internal visual representations in MLLMs
    This underpins the entire causal probing framework described in the abstract.

pith-pipeline@v0.9.0 · 5492 in / 1206 out tokens · 37043 ms · 2026-05-08T11:54:46.373026+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

12 extracted references · 6 canonical work pages · 2 internal anchors

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  7. [7]

    If the instruction is purely about removing objects, extract the object name

  8. [8]

    add", "place

    If the instruction contains other actions like "add", "place", "change", "replace", or implies adding something new (e.g., "remove X and add Y"), output exactly "SKIP"

  9. [9]

    SKIP". Examples: Input:

    Do not output a full sentence. Output ONLY the object name or "SKIP". Examples: Input: "Remove the candles." Output: candle Input: "Remove the horses that are on the bridge." Output: horse Input: "Remove the vegetables and add candies to the bowl." Output: SKIP Input: "Remove all the food and place a laptop on the table." Output: SKIP We finally construct...

    2025

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    Does the Steered Text explicitly mention the Target Concept or words highly related to it?

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    Does the Steered Text describe the image in the style of the Target Concept compared to the Base Text?

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    strength

    If the Base Text already contained the concept, answer 1 only if the Steered Text maintains or emphasizes it. Task: Output ’1’ if the injection is SUCCESSFUL (the concept is clearly present). Output ’0’ if the injection is FAILED (the concept is missing or the text is nonsense). Output Format: Only output a single digit: 0 or 1. Do not output any other te...

    2019