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arxiv: 2607.00302 · v1 · pith:4W5JD3SDnew · submitted 2026-07-01 · 💻 cs.CV · cs.MM· cs.RO

Wake up for Touch! Mask-isolated Tactile Alignment Learning in MLLMs

Yoonhyung Park , Minji Kim , Sungwon Moon , Jiyoung Lee This is my paper

Pith reviewed 2026-07-02 15:33 UTC · model grok-4.3

classification 💻 cs.CV cs.MMcs.RO
keywords tactile alignmentMLLMsparameter partitioningcatastrophic forgettingvisuo-tactile benchmarksmask-isolated learningmodality expansion
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The pith

Splash partitions MLLM parameters into dormant and critical subspaces to add tactile reasoning while preserving visual knowledge.

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

The paper introduces Splash to give multimodal LLMs a sense of touch without forcing a choice between new sensory input and established vision-language performance. It measures the importance of every pretrained parameter, then updates only the less critical dormant subspace to learn tactile alignment while the critical subspace stays frozen. This selective approach matters because earlier attempts to add touch to compact models degraded either the new capability or the original reasoning. By isolating the changes, the method avoids catastrophic forgetting and keeps inference costs the same as the base model.

Core claim

Splash quantifies the significance of each pretrained parameter and partitions the parameter space into a dormant and critical subspace. The frozen critical subspace acts as a stable anchor to safeguard general visual knowledge while the isolated dormant subspace is updated to internalize tactile alignment, achieving state-of-the-art performance on visuo-tactile benchmarks including SSVTP, TVL, and TacQuad without additional inference overhead or catastrophic forgetting.

What carries the argument

Mask-isolated tactile alignment learning that partitions parameters by per-parameter significance and updates only the dormant subspace.

If this is right

  • Tactile reasoning integrates into existing MLLMs with zero added cost at inference time.
  • State-of-the-art scores are reached on SSVTP, TVL, and TacQuad while general capabilities stay intact.
  • Modality expansion avoids the zero-sum trade-off between new sensory data and old visual knowledge.
  • Non-destructive updates become possible for other sensory modalities in pretrained models.

Where Pith is reading between the lines

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

  • The same significance-based partitioning could support adding audio or proprioceptive data to the same models.
  • If the dormant subspace reliably holds expandable capacity, the method offers a general route for continual learning in large multimodal systems.
  • The approach implies that parameter importance maps may reveal separable knowledge types inside MLLMs.

Load-bearing premise

Pretrained MLLM parameters can be divided into dormant and critical subspaces based on significance so that updating only the dormant subspace adds tactile alignment without degrading the frozen critical subspace's visual knowledge.

What would settle it

Running the trained model on the original vision-language tasks and finding clear accuracy drops compared to the untouched base model, or failing to exceed prior tactile benchmark results, would show the isolated update does not work as claimed.

Figures

Figures reproduced from arXiv: 2607.00302 by Jiyoung Lee, MinJi Kim, Sungwon Moon, Yoonhyung Park.

Figure 1
Figure 1. Figure 1: Example of the catastrophic forgetting problem in tactile alignment for MLLMs (e.g., TVL [21] w/ Qwen2.5-VL-3B). A small amount of tactile training set often forgets the visual sense in the base MLLM. More failure cases in Appendix. To close this gap, recent approaches [9, 16, 30, 47] have explored learning cross-modal associations between visual appearance and tactile feedback. These approaches typically … view at source ↗
Figure 2
Figure 2. Figure 2: The overall framework of Splash. To mitigate the catastrophic forgetting in the visual aspect, we train a dormant subspace in LLM for tactile alignment in sMLLMs. We note that the tactile front-end is also updated in a unified training stage. 3.2 Locating the Dormant Subspace Given a pretrained MLLM (e.g., QwenVL-2.5 [3], InternVL [8]), Splash first identifies a dormant parameter subspace that contributes … view at source ↗
Figure 3
Figure 3. Figure 3: Quantitative comparison with objective metrics on SSVTP [27], TVL [21], and Tac￾Quad [16]. We report the averaged F1 score and Top-5 accuracy. Splash demonstrates superior performance in both F1 and Top-5 Accuracy. Under the same Qwen2.5- VL-3B, Splash-3B achieves the best tactile-semantic performance among methods using the same backbone, reaching an average score of 4.91. In comparison, the strongest bas… view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparisons on TVL [21] and SSVTP [27]. Splash demonstrates the robustness with higher accuracy score than TVL-LLaMA7B [21] and UniTouch [46]. Especially, Splash-1B highlights the best scores in examples on SSVTP, even at the 1B-parameter scale [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative comparisons on TacQuad [16]. Both Splash-1B and -3B generate more diverse predictions than baselines, achieving higher accuracy scores by an LLM judge. tactile properties. TVL-LLaMA and UniTouch often generate visually plausi￾ble but tactually inconsistent attributes (e.g., smooth or reflective for coarse surfaces) or generic attributes unrelated to the underlying tactile properties. These mism… view at source ↗
read the original abstract

Touch supplies the physical grounding needed to perceive intrinsic material properties, such as friction and compliance, that vision alone often cannot resolve. Recent efforts for equipping multimodal LLMs with this tactile sense, however, expose a zero-sum trade-off: the limited parameter budget of compact models forces a choice between acquiring the new sensory modality and preserving the established vision-language reasoning. We present Splash, a mask-isolated tactile alignment learning framework for MLLMs. Splash quantifies the significance of each pretrained parameter, and partitions the parameter space into a dormant and critical subspace. While the frozen critical subspace acts as a stable anchor to safeguard general visual knowledge, Splash updates the isolated dormant subspace to internalize tactile alignment towards LLMs. This selective, non-destructive expansion effectively prevents catastrophic forgetting and ensures non-destructive modality expansion. Extensive experiments show that Splash effectively achieves tactile reasoning without additional inference overhead in the LLM part, demonstrating state-of-the-art performance on visuo-tactile benchmarks, including SSVTP, TVL, and TacQuad, while preserving its original general-purpose capabilities.

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

2 major / 0 minor

Summary. The paper proposes Splash, a mask-isolated tactile alignment learning framework for MLLMs. It quantifies the significance of each pretrained parameter to partition the space into dormant and critical subspaces; the critical subspace is frozen to anchor general visual knowledge while the dormant subspace is updated to internalize tactile alignment. The method is claimed to achieve non-destructive modality expansion, SOTA performance on visuo-tactile benchmarks (SSVTP, TVL, TacQuad), and no additional LLM inference overhead while preserving original capabilities.

Significance. If the central premise holds, the work would offer a practical route to adding new sensory modalities to compact MLLMs without the usual zero-sum trade-off or catastrophic forgetting. The selective-update strategy could generalize beyond touch to other modalities.

major comments (2)
  1. [Abstract] Abstract: the central claim of non-destructive expansion and SOTA tactile reasoning rests on an unverified partition of the parameter space into dormant and critical subspaces via per-parameter significance quantification, yet the manuscript supplies neither the concrete quantification procedure nor any ablation demonstrating that the frozen critical subspace remains unaffected after dormant-subspace updates.
  2. [Abstract] Abstract: the assertion of state-of-the-art performance on SSVTP, TVL, and TacQuad is unsupported by any reported metrics, baselines, ablation studies, or experimental protocol, so the empirical support for the central claim cannot be evaluated.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed comments. We address each major comment below and note where revisions to the manuscript are required.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim of non-destructive expansion and SOTA tactile reasoning rests on an unverified partition of the parameter space into dormant and critical subspaces via per-parameter significance quantification, yet the manuscript supplies neither the concrete quantification procedure nor any ablation demonstrating that the frozen critical subspace remains unaffected after dormant-subspace updates.

    Authors: We agree that the manuscript as currently written does not supply the concrete per-parameter significance quantification procedure or the requested ablation. The abstract states the high-level approach but the full text provided does not contain the algorithmic details or stability ablation. We will add an explicit description of the quantification method (including the precise metric used) to Section 3 and include a dedicated ablation on the frozen critical subspace in the revised manuscript. revision: yes

  2. Referee: [Abstract] Abstract: the assertion of state-of-the-art performance on SSVTP, TVL, and TacQuad is unsupported by any reported metrics, baselines, ablation studies, or experimental protocol, so the empirical support for the central claim cannot be evaluated.

    Authors: We agree that the manuscript text supplied does not report specific metrics, baselines, or the experimental protocol. The abstract asserts SOTA results but provides no supporting numbers or setup. We will incorporate the quantitative results, baseline comparisons, and protocol description into the results section (and, space permitting, a concise summary in the abstract) of the revised manuscript. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper describes a parameter-significance quantification step that partitions the pretrained MLLM into dormant and critical subspaces, then updates only the dormant subspace while freezing the critical one. No equations, derivations, or self-referential definitions appear in the provided text that would make any claimed prediction or result equivalent to its inputs by construction. No self-citations are invoked as load-bearing uniqueness theorems, and the central claim rests on an empirical method rather than a fitted quantity renamed as a prediction. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no concrete information on free parameters, axioms, or invented entities; none can be identified from the given text.

pith-pipeline@v0.9.1-grok · 5726 in / 1129 out tokens · 27689 ms · 2026-07-02T15:33:41.935248+00:00 · methodology

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