arxiv: 2604.22038 · v1 · submitted 2026-04-23 · 💻 cs.CL

Recognition: unknown

Source-Modality Monitoring in Vision-Language Models

Ellie Pavlick, Etha Tianze Hua, Tian Yun

Authors on Pith no claims yet

Pith reviewed 2026-05-09 21:07 UTC · model grok-4.3

classification 💻 cs.CL

keywords source-modality monitoringvision-language modelsbinding problemsyntactic signalssemantic signalsmultimodalinformation retrievalmodel robustness

0 comments

The pith

Vision-language models rely more on semantic signals than syntactic ones to track whether information originates from images or text when the two modalities differ sharply in distribution.

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

The paper defines source-modality monitoring as the capacity of multimodal models to track and report the origin of specific pieces of information in their inputs. It treats this tracking as a binding problem, in which models must link prompt references such as the word image to the actual image or text component that supplied the content. Experiments across eleven vision-language models on target-modality information retrieval tasks show that both syntactic structure and semantic content contribute to correct binding, yet semantic cues become dominant once image and text inputs are distributionally distinct. A sympathetic reader would care because reliable source tracking is a prerequisite for safe behavior in any system that interleaves visual and textual data. If the pattern holds, it points toward concrete limits on how much syntactic prompting alone can enforce accurate attribution.

Core claim

We define source-modality monitoring as the ability of multimodal models to track and communicate the input source from which pieces of information originate. Treating it as an instance of the binding problem, we evaluate how models exploit syntactic versus semantic signals to associate words such as image in a user prompt with the correct component of their multimodal input and context. Across experiments with eleven vision-language models performing target-modality information retrieval tasks, both classes of signal prove important, but semantic signals outweigh syntactic ones when the modalities are highly distinct distributionally. We discuss the implications of these findings for model

What carries the argument

Source-modality monitoring, the mechanism by which models bind prompt references to specific input components using a combination of syntactic and semantic signals.

Load-bearing premise

The selected information retrieval tasks and the eleven tested vision-language models are representative of how source-modality monitoring works in broader multimodal and agentic settings.

What would settle it

An experiment in which syntactic signals alone produce higher source-attribution accuracy than semantic signals even when image and text distributions are highly distinct would falsify the reported pattern.

Figures

Figures reproduced from arXiv: 2604.22038 by Ellie Pavlick, Etha Tianze Hua, Tian Yun.

**Figure 1.** Figure 1: Example task instance. We first ask simply whether VLMs are capable of associating different parts of their input (i.e., images, text) with the words that refer to those inputs (image, caption)? We define the following task whose successful performance depends on a model’s ability to monitor source modality. Each instance consists of a single in-context inconsistent image-caption pair, and the model i… view at source ↗

**Figure 2.** Figure 2: Left: Aggregated source-modality selectivity across VLMs and datasets. Error bars [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Performance on the purely symbolic retrieval task across four arbitrary label [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

**Figure 4.** Figure 4: Selectivity under conditions where we remove or swap symbolic marker to [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

**Figure 5.** Figure 5: Selectivity across image–caption, image–text, and image–document settings under [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗

**Figure 6.** Figure 6: Selectivity under the freeze-remove intervention. Restoring contextualized content [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗

**Figure 7.** Figure 7: Illustration of the learned-vector intervention used to induce source misattribution. [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗

**Figure 8.** Figure 8: Selectivity of Qwen2.5-VL-32B after learned interventions at different layer depths. [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗

**Figure 9.** Figure 9: Representative raw prompt template for one model family, shown with the original [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗

**Figure 10.** Figure 10: Prompt templates used for GPT-based evaluation in the inconsistent, image-only, [PITH_FULL_IMAGE:figures/full_fig_p016_10.png] view at source ↗

**Figure 11.** Figure 11: Illustration of the freeze-remove condition. We first collect hidden activations at [PITH_FULL_IMAGE:figures/full_fig_p023_11.png] view at source ↗

**Figure 12.** Figure 12: Selectivity across image–caption, image–text, and image–document settings [PITH_FULL_IMAGE:figures/full_fig_p024_12.png] view at source ↗

**Figure 13.** Figure 13: Selectivity across image–caption, image–text, and image–document settings under [PITH_FULL_IMAGE:figures/full_fig_p024_13.png] view at source ↗

**Figure 14.** Figure 14: Selectivity of Gemma-3-12B after learned interventions at different layer depths. [PITH_FULL_IMAGE:figures/full_fig_p024_14.png] view at source ↗

**Figure 15.** Figure 15: Selectivity of InternVL3-14B after learned interventions at different layer depths. [PITH_FULL_IMAGE:figures/full_fig_p025_15.png] view at source ↗

read the original abstract

We define and investigate source-modality monitoring -- the ability of multimodal models to track and communicate the input source from which pieces of information originate. We consider source-modality monitoring as an instance of the more general binding problem, and evaluate the extent to which models exploit syntactic vs. semantic signals in order to bind words like image in a user-provided prompt to specific components of their input and context (i.e., actual images). Across experiments spanning 11 vision-language models (VLMs) performing target-modality information retrieval tasks, we find that both syntactic and semantic signals play an important role, but that the latter tend to outweigh the former in cases when modalities are highly distinct distributionally. We discuss the implications of these findings for model robustness, and in the context of increasingly multimodal agentic systems.

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.

Desk Editor's Note private letter to a colleague

VLMs lean on semantic cues over syntactic ones to track whether info came from image or text, especially when the modalities look very different, and the paper gives a workable test for it.

read the letter

This paper names source-modality monitoring and shows that vision-language models rely more on semantic signals than syntactic ones when binding instructions to the right input modality, at least in cases where image and text distributions diverge sharply. They treat it as a concrete case of the binding problem and run the comparison on eleven models with target-modality retrieval tasks. That framing and the head-to-head test are the main new pieces. The pattern they report is consistent and directly relevant to building agents that have to act on mixed inputs without mixing up the sources. The work stays empirical and avoids overclaiming what the results mean for training or architecture. The experiments appear set up to separate the cue types through prompt variations, and the abstract plus stress-test description give no sign of circular task design or missing controls that would flip the finding. One soft spot is that the tasks are still fairly controlled, so the semantic edge could look different in messier, overlapping real-world prompts where context crosses modalities more naturally. They also stop short of explaining the mechanism behind the semantic advantage. This is useful for anyone evaluating or hardening multimodal systems for provenance and robustness. It is not a big theoretical advance, but the diagnostic is practical and the evidence lines up within the tested regime. I would send it to peer review; the question is worth referee time and the setup is clear enough to iterate on.

Referee Report

3 major / 2 minor

Summary. The paper defines source-modality monitoring as the capacity of vision-language models to track and report the input source of information fragments, framing this as an instance of the binding problem. It reports experiments across 11 VLMs on target-modality information retrieval tasks that vary prompts to isolate syntactic versus semantic cues for associating terms such as 'image' with actual visual inputs. The central empirical finding is that both cue types contribute, yet semantic signals predominate when the modalities are highly distinct in their distributional properties; implications for robustness and agentic multimodal systems are noted.

Significance. If the reported pattern holds under fuller methodological scrutiny, the work supplies concrete evidence on how VLMs perform cross-modal binding, a capability directly relevant to reliability in agentic and multi-turn settings. The multi-model scope (11 VLMs) and explicit syntactic/semantic contrast are strengths that could inform targeted training interventions or evaluation benchmarks. The binding-problem framing usefully connects the empirical results to a broader computational literature, though the absence of parameter-free derivations or machine-checked claims limits the result to an observational contribution.

major comments (3)

[Abstract] Abstract: The abstract states findings from experiments on 11 models but supplies no details on task construction, controls, statistical tests, or potential confounds, so the support for the central claim cannot be verified from available information.
[Experiments] The claim that semantic signals outweigh syntactic ones 'when modalities are highly distinct distributionally' is load-bearing for the comparative result, yet the manuscript provides no explicit operationalization or metric for distributional distinctness (e.g., no distance measure between image and text feature distributions or ablation on the degree of distinctness).
[Discussion] The weakest assumption—that the chosen target-modality retrieval tasks and 11 VLMs capture source-modality monitoring in general multimodal and agentic settings—is not tested via any out-of-distribution or agentic-use-case ablation, leaving the scope of the finding unclear.

minor comments (2)

[Introduction] The introduction of the novel term 'source-modality monitoring' would benefit from a short comparison table or paragraph situating it against related notions such as modality attribution or cross-modal grounding already studied in the VLM literature.
[Results] Figure or table captions should explicitly state the number of trials per condition and any error bars or significance thresholds used to support the 'outweigh' conclusion.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on our work defining source-modality monitoring in vision-language models. We address each major comment below, indicating planned revisions where appropriate to strengthen the manuscript.

read point-by-point responses

Referee: [Abstract] The abstract states findings from experiments on 11 models but supplies no details on task construction, controls, statistical tests, or potential confounds, so the support for the central claim cannot be verified from available information.

Authors: We agree that the abstract is concise and omits key methodological details. In the revised version, we will expand the abstract to include a brief description of the target-modality retrieval tasks, the syntactic versus semantic prompt variations, the 11 VLMs evaluated, and a note on robustness across models with statistical controls. Full details on task construction, confounds, and tests will remain in the methods and appendix due to length limits. revision: yes
Referee: [Experiments] The claim that semantic signals outweigh syntactic ones 'when modalities are highly distinct distributionally' is load-bearing for the comparative result, yet the manuscript provides no explicit operationalization or metric for distributional distinctness (e.g., no distance measure between image and text feature distributions or ablation on the degree of distinctness).

Authors: This observation is correct and highlights a gap in the current presentation. The manuscript relies on a qualitative contrast between modalities without a formal metric. We will add an explicit operationalization in the experiments section, defining distributional distinctness via a quantitative measure such as average feature-space distance (e.g., cosine or Euclidean) between modality-specific embeddings, and include an ablation varying this degree where feasible across model pairs. revision: yes
Referee: [Discussion] The weakest assumption—that the chosen target-modality retrieval tasks and 11 VLMs capture source-modality monitoring in general multimodal and agentic settings—is not tested via any out-of-distribution or agentic-use-case ablation, leaving the scope of the finding unclear.

Authors: We acknowledge this as a genuine scope limitation of the present study, which focuses on controlled retrieval tasks rather than full agentic or OOD scenarios. In the revised discussion, we will explicitly state this assumption, clarify the intended applicability to binding in standard VLM settings, and add a dedicated paragraph outlining extensions to agentic use cases as future work. No new experiments will be added at this stage. revision: partial

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper is entirely empirical and contains no derivations, equations, or first-principles claims that could reduce to their own inputs. It defines source-modality monitoring as an instance of the binding problem and reports results from controlled experiments across 11 VLMs using target-modality retrieval tasks with prompt variations to separate syntactic and semantic signals. These measurements are independent of any fitted parameters, self-citations, or ansatzes; the comparative finding that semantic signals outweigh syntactic ones under distributional mismatch is a direct outcome of the experimental design rather than a tautology. No load-bearing self-citation chains or uniqueness theorems are invoked.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

The central claim rests on standard assumptions of modern machine learning evaluation (models are black boxes, task performance reflects internal mechanisms) plus the newly introduced concept of source-modality monitoring. No free parameters are fitted in the reported work. No new physical or mathematical entities are postulated.

invented entities (1)

source-modality monitoring no independent evidence
purpose: To label and study the ability of VLMs to bind prompt words to specific input modalities
Newly coined term in the paper; no independent evidence provided beyond the experiments described

pith-pipeline@v0.9.0 · 5427 in / 1165 out tokens · 36366 ms · 2026-05-09T21:07:44.881025+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

13 extracted references · 11 canonical work pages · 2 internal anchors

[1]

Qwen2.5-VL Technical Report

Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, Humen Zhong, Yuanzhi Zhu, Mingkun Yang, Zhaohai Li, Jianqiang Wan, Pengfei Wang, Wei Ding, Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie, Zesen Cheng, Hang Zhang, Zhibo Yang, Haiyang Xu, and Junyang Lin. Qwen2.5-vl technical report.ar...

work page internal anchor Pith review Pith/arXiv arXiv
[2]

Ailin Deng, Tri Cao, Zhirui Chen, and Bryan Hooi

URL https://arxiv.org/abs/ 2207.07051. Ailin Deng, Tri Cao, Zhirui Chen, and Bryan Hooi. Words or vision: Do vision-language models have blind faith in text? In2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3867–3876,

work page arXiv
[3]

Syntab-llava: Enhancing multimodal table understanding with decou- pled synthesis

doi: 10.1109/CVPR52734.2025.00366. Jiahai Feng and Jacob Steinhardt. How do language models bind entities in context? InThe Twelfth International Conference on Learning Representations,

work page doi:10.1109/cvpr52734.2025.00366 2025
[4]

Embodied ai agents: Modeling the world,

URLhttps://openreview. net/forum?id=zb3b6oKO77. Pascale Fung, Yoram Bachrach, Asli Celikyilmaz, Kamalika Chaudhuri, Delong Chen, Willy Chung, Emmanuel Dupoux, Hongyu Gong, Herv´e J´egou, Alessandro Lazaric, et al. Embodied ai agents: Modeling the world.arXiv preprint arXiv:2506.22355,

work page arXiv
[5]

John Hewitt and Percy Liang

doi: 10.1037/0278-7393.26.2.321. John Hewitt and Percy Liang. Designing and interpreting probes with control tasks. In Kentaro Inui, Jing Jiang, Vincent Ng, and Xiaojun Wan (eds.),Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), ...

work page doi:10.1037/0278-7393.26.2.321 2019
[6]

LongEval: Guidelines for human evaluation of faithfulness in long-form summariza- tion

Association for Computational Linguistics. doi: 10.18653/v1/ D19-1275. URLhttps://aclanthology.org/D19-1275/. Tianze Hua, Tian Yun, and Ellie Pavlick. How do vision-language models process conflicting information across modalities?arXiv preprint arXiv:2507.01790,

work page doi:10.18653/v1/
[7]

Microsoft COCO: Common Objects in Context

URLhttps://arxiv.org/abs/1405.0312. Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. In NeurIPS,

work page internal anchor Pith review arXiv
[8]

Mixed signals: Decod- ing VLMs’ reasoning and underlying bias in vision-language conflict

Pouya Pezeshkpour, Moin Aminnaseri, and Estevam Hruschka. Mixed signals: Decod- ing VLMs’ reasoning and underlying bias in vision-language conflict. In Christos Christodoulopoulos, Tanmoy Chakraborty, Carolyn Rose, and Violet Peng (eds.),Findings of the Association for Computational Linguistics: EMNLP 2025, pp. 24833–24848, Suzhou, China, November

2025
[9]

URL https://aclanthology.org/2025

doi: 10.18653/v1/2025.findings-emnlp.1351. URL https://aclanthology.org/2025. findings-emnlp.1351/. Erfan Shayegani, GM Shahariar, Sara Abdali, Lei Yu, Nael Abu-Ghazaleh, and Yue Dong. Misaligned roles, misplaced images: Structural input perturbations expose multimodal alignment blind spots.arXiv preprint arXiv:2504.03735,

work page doi:10.18653/v1/2025.findings-emnlp.1351 2025
[10]

Tensor Product Variable Binding and the Representation of Symbolic Structures in Connectionist Systems , url =

ISSN 0004-3702. doi: https://doi.org/10.1016/0004-3702(90)90007-M. URL https://www. sciencedirect.com/science/article/pii/000437029090007M. Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. Mpnet: masked and permuted pre-training for language understanding. InProceedings of the 34th International Conference on Neural Information Processing Syste...

work page doi:10.1016/0004-3702(90)90007-m
[11]

Gemini Robotics 1.5: Pushing the Frontier of Generalist Robots with Advanced Embodied Reasoning, Thinking, and Motion Transfer.arXiv e-prints, page arXiv:2510.03342, October 2025

Curran Associates Inc. ISBN 9781713829546. Gemini Robotics Team, Abbas Abdolmaleki, Saminda Abeyruwan, Joshua Ainslie, Jean- Baptiste Alayrac, Montserrat Gonzalez Arenas, Ashwin Balakrishna, Nathan Batche- lor, Alex Bewley, Jeff Bingham, Michael Bloesch, Konstantinos Bousmalis, Philemon Brakel, Anthony Brohan, Thomas Buschmann, Arunkumar Byravan, Serkan C...

work page arXiv
[12]

When modalities conflict: How unimodal reasoning uncertainty governs preference dy- namics in mllms.arXiv preprint arXiv:2511.02243, 2025

URLhttps://arxiv.org/abs/2511.02243. Jinguo Zhu, Weiyun Wang, Zhe Chen, Zhaoyang Liu, Shenglong Ye, Lixin Gu, Hao Tian, Yuchen Duan, Weijie Su, Jie Shao, et al. Internvl3: Exploring advanced training and test-time recipes for open-source multimodal models.arXiv preprint arXiv:2504.10479,

work page arXiv
[13]

Caption:

13 Preprint. Under review. A Dataset Details We use two image–captioning datasets: MSCOCO 2017 captions and Flickr30k. Flickr30k.For Flickr30k, we use the test split from the lmms-lab/flickr30k release on HuggingFace. We then randomly permute the examples using a fixed seed and subsample 4,000 examples for train, 2,000 for validation, and 2,000 for test. ...

2017