VideoAnchor: Reinforcing Subspace-Structured Visual Cues for Coherent Visual-Spatial Reasoning
Authors: Zhaozhi Wang
Tong Zhang
Mingyue Guo
Yaowei Wang
Qixiang Ye
Venue: ICLR 2026 Poster (2026)
Keywords: visual-spatial reasoning
sparse subspace clustering
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Multimodal Large Language Models (MLLMs) have achieved impressive progress in vision–language alignment, yet they remain limited in visual–spatial reasoning. We first identify that this limitation arises from the attention mechanism: visual tokens are overshadowed by language tokens, preventing the model from consistently recognizing the same visual cues across frames. To address this challenge, we draw a novel connection between the self-expressiveness property in sparse subspace clustering and the attention mechanism in Transformers. Building on this insight, we propose VideoAnchor, a plug-and-play module that leverages subspace affinities to reinforce visual cues across frames without retraining, effectively anchoring attention to shared visual structures. Extensive experiments across benchmarks and backbone models show consistent performance gains — e.g., 3.2% and 4.6% improvements on VSI-Bench and Video-MME (spatial-related tasks) with InternVL2-8B and Qwen2.5VL-72B—while qualitative analyses demonstrate more coherent subspace partitions and stronger visual grounding.

Published as a conference paper at ICLR 2026
VIDEOANCHOR:
REINFORCING SUBSPACE-STRUCTURED VISUAL CUES FOR COHERENT VISUAL-SPATIAL REASONING
Zhaozhi Wang Tong Zhang-1
Mingyue Guo Yaowei Wang
Qixiang Ye4.2
l University of Chinese Academy of Sciences ?eng Cheng Lab
Question: what the spatial relationship between radio and the flowers?
Frame
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Trans)
Respasse
Thace ie died spatial adatiendip hdtees
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locardiio diffren soams willia hoase.
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144
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Aichor
123
13.0
Qvan2
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Baseline Video.Acchrr
(e) V.E -.Sl-Benchhe Score
Our
Figure 1: Effect of the proposed VideoAnchor. (a) Attention activations shared regions (c.g., the sofa) across frames show how consistent patterns are anchored to enhance visual-spatial reasoning, enabling preciser object co-location and contextual proximity than Intern VI.2-8B (baseline). (b-c) Comparison of text-to-image attention proportion within text-to-all attention and the performance w/o VideoAnchor. Values are averaged across text tokens based on first-layer attention weights.
ABSTRACT
Multimodal Large Language Models (MLLMs) have achieved impressive progress in vision-language alignment, yet they remain limited in visual-spatial reasoning. We first identify that this limitation arises from the attention mechanism: visual tokens overshadowed by language tokens, preventing the model from consis- tently recognizing the same visual cues across frames. To address this challenge, draw novel connection between the ceff-expressiveness property in sparse subspace clustering and the attention mechanism in Transformers. Building on this insight, we propose VideoAnchor, plug-and-play module that leverages subspace affinities to reinforce visual cues across frames without retraining, effec- tively anchoring attention shared visual structures. Extensive experiments across benchmarks and backbone models show consistent performance gains 3.2% and 4.6% improvements on VSI-Bench and Video-MME (spatial-related tasks) with Intern VL.2-8B and Qwen2.5VL-72B -while qualitative analyses demonstrate more coherent subspace partitions and stronger visual grounding. Our codes are available at httpss//ggghbb..oom//enuvvvee..t
INTRODUCTION
Advancing visual-spatial reasoning - the ability to reason about relative positions, common struc- tures, or spatial references across multiple visual inputs is crucial for embodied AI, robotics, and spatially grounded decision-making Despite impressive progress in multimodal large language
models (MI.LMs) for semantic understanding (Achiam et al. 22233] Liu al. 2023b Comanici et
2025). current models still exhibit limited capability in this dimension (Yang et al. 2025 Yeh et al.
Correspondence tongzhang acc.

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Published as a conference paper at ICLR 2026
2025| Ouyang et al. 2025 Li al. 2025b). This limitation arises primarily from their strong reliance on textual priors, which tends to overshadow fine- tee-grained visual cues and results in hallucinated or visually inconsistent predictions (Ghatkesar et al. 2025). A key reason is that the contributions of individual image tokens are too weak to establish a coherent representational structure. In short, existing MILMSs lack mechanism consistently preserve visual cues across frames, which often results in hallucinations and prevents them from achieving coherent visual-spatial reasoning.
This limitation calls for general mechanism that can reliably preserve visual evidence within attention. Existing designs such as patch-level attention, however, treat tokens independently and thus struggle to capture the higher-level continuity of objects or regions across frames. One promis- ing direction is to organize tokens to semantic subspaces. which directly motivates our approach, Anchor, Fig. aa). Vicho leverages this principle to guide attention toward consistent subspace structures, enhancing attention to shared regions across frames and anchoring coherent visual patterns. To avoid the heavy retraining costs and the risk of overfitting in dataset-centric approaches, we instead focus improving visual-spatial reasoning directly at test time.
Building on this intuition, we propose VideoAnchor, a plug-and-play module that enhances vi- sual-spatial reasoning in MLLMs at test time. VideoAnchor establishes novel connection between the self-expressiveness property in sparse subspace clustering (SSC) (Ji et al., 2017 Zhang et al. 2019) and the attention mechanism in Transformers (Vaswani et al. 2017). and it is composed of two units. First, the Posspace-to-Scallee Unit leverages SSC to organize tokens into semantic subspaces. For each token, it estimates how strongly the token can be expressed by other tokens within the same subspace, thereby reflecting its stability and representativeness in the shared structure. Tokens with stronger expression are regarded more stable components of coherent visual elements. Building on these estimates, the Attention Regularization Unit incorporates them as scalers into both query-key similarities and value updates, amplifying the influence of tokens that exhibit stronger subspace coher- ence. Together, these units anchor attention to consistent visual structures across frames, mitigating the dominance of textual priors and ultimately enabling more coherent visual-spatial reasoning, as further illustrated in Fig. [b)().
The contributions of this study include:
We introduce VideoAnchor, a plug-and-play module that enhances visual-spatial reasoning in MLLMs at test time without requiring additional training.
To capture consistent visual anchors across frames, we design Subspace-to-Scaler Unit that leverages SSC to construct semantic subspaces and generate token-level scalers.
To reinforce visual grounding within the attention mechanism, we develop an Attention Reg- ularization Unit that integrates the generated scalers into the attention process, amplifying coherent visual tokens.
We extensively evaluate VideoAnchor with different MLLMs on VSI-Bench, All-Angles- Bench, SPAR-Bench, and Video-MME, and demonstrate consistent performance improvements. On VSI- Bench, VideoAnchor achieves a 3.2% improvement when applied to VL2-8BB (Chen et al. 2024b while All-Angles-Benn Bench, it brings a 3.1% % gain with LLaVAVideo-72B ęZhang al.
2025b Beyond quantitative results, visualizations of clustering outputs show that VideoAnchor produces more semantically coherent subspace partitions over the baseline. Analyses of attention maps further demonstrate that Aicho strengthens attention on visual anchors and mitigates over-reliance on textual priors.
2
RELATED WORKS
2.1 MULTIMODAL LARGE LANGUAGE MODELS
Multimodal large language models (MLLMs) have emerged as a powerful paradigm for integrating
text, images, and other modalities (Yin et al. 2024). Extending the success of large language models
(LLMs) (Achiam et al. 2023 Touvron et al. 2023 Team et al. 2024: Guo et al. (005),, they enable the joint synthesis and interpretation of multimodll information. Early efforts such as CLIP (Radford ct al. (202]) aligned vision and language through large-scale contrastive learning, while BLIP (Li al.
2022) and BIIP-2 (Li al. 2023) advanced -lngunnl understanding through pre-training
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Published as a conference paper at ICLR 2026
and instruction tuning. Building this foundation, Flamingo Alaayrac al. 2022 employed cross-attention to connect visual features with frozen LLMs, enabling shot reasoning. More recent systems such as LLaVA (Liu al. 2023b). Qwen- VL (Bai al. 2023 Wang et al. 2024 Bai et al. 2025), and Intern (Chen et al. 2024c b a integrate visual encoders with language backbones, achieving strong performance
tasks such image captioning, visual question answering, and multi-image reasoning. Despite these advances, MLLMs still face challenges in visual-spatial reasoning. Their strong reliance on textual priors often suppresses fine-grainet spatial cues GGattessar et al. (025), leaving image tokens underrepresented and leading to hallucinated or incoherent predictions when performing iisual-spatio reasoning across frames.
2.2
VISUAL- MLLMS
To address the insufficient visual grounding in MLLMs, researchers have explored different strategies to strengthen the visual component. One prominent line of work improves high-resolution input (Loo
ett al. 2025 Guo al. 2024 Li al. 2024), which divides images into patches or combines multiple
resolutions to capture fime- grained details. Another line research enhances feature fusion (Lin
al. 2023 Shi et al. 2024 Yao et al. 2024), integrating diverse encoders deformable attention
mechanisms to enrich visual representation, though often at the cost of more tokens and computation.
To overcome the retraining overhead and scalability issues of these approaches. third line of work investigates training-freee solutions. Representative examples include DC (Wang et al. 2025). ControlMLLM (Wu et al. 2024), and VisionFuse (Chen al. 2024d), which enhance perception at inference by recursive image partitioning, attention adjustment, or multi-encoder fusion. However, these approaches still face trade-offs: 2C incurs inference overhead, ControlMLLM requires manual region specification, and VisionFuse depends multiple encoders. Thus, enhancing the visual perception of MI.LMs in a scalable and adaptive manner remains an open challenge.
2.3
VISUAL-SPATIAL REASONING
Early benchmarks @Johnson et al. 2017 Liu et al. 2023a Tang al., 2024 Ramakrishnan et al.)
2025)
primarily assessed this ability in single images. probing spatial re relations within static scenes.
More recent efforts (Yang al. 2025 Yeh et al. 2025 Ouyang et al. 2025 Li al.
2025b) extend
the evaluation to videos and multi-view input, which better reflect real-world spatial perception but also reveal persistent weaknesses in current MLLMs.
To improve performance, some approaches (Feng et al. 2025 Wu et al. 2025a Liao et al. 2025
construct video-centric datasets to enhance spatial awareness, but these methods incur heavy retraining costs and risk limited generalization. In contrast, our proposed VideoAnchor enhances spatial reasoning directly at test time a training-free, plug-and-play manner, offering efficiency, broad compatibility with existing MILMs, and reliable generalization without retraining.
3
METHODOLOGY
3.1
VIDEOANCHOR OVERVIEW
The framework of VideoAnchor shown Fig. 2 Given a sequence of frames or multi-view images, pretrained visual encoder first yields visual tokens; VideoAnchor then elevates evidence that is shared across frames-see serving as visual anchors improve visual grounding at inference time. To achieve this, VideoAnchor is composed of two units:
Subspace-to-Scaler Unit.
The goal of this unit is to identify anchor tokens and convert their importance into of simple, multiplicative scalers. This process unfolds in three steps: discovering the underlying geometric structure of the tokens, calculating a "sharing expression score" for each token based on that structure, and converting these scores into scalers for the attention mechanism.
Attention Regularization Unit. Obtained the scalers from the above, we modulate attention at inference by pairwise gating of query- interactions after the exponential and per- token amplification of values, thereby allocating more probability mass and representational capacity to visual anchor tokens.
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Published as a conference paper at ICLR 2026
Sunglie Framss
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Figure 2: The overall framework of our proposed VideoAnchor.
This design improves visual-spatial reasoning without additional training while preserving scalability and generalization.
3.2 SUBSPACE-TO-SCALEE UNIT
Self-expressiveness. The foundation of our approach is the seff-expressiveness property, which posits that each data point in union of subspaces can be represented as a linear combination of other points from the same subspace. We leverage Sparse Subspace Clustering (SSC) to find such representation. Given the flattened visual tokens Xvis E DD extracted across frames or views, where Nvis and D denote the number of visual tokens and the feature dimension respectively, we reveal their underlying subspace structure using Sparse Subspace Clustering (SSC). Specifically, we learn self-expression matrix W
R
Nv. Ny, through
min W
Ae Xvis
X WI, + A [WI,, s.t. diag(W = 0.
Here, A enforces reconstruction fidelity under an & loss, ensuring robustness to noise and pertur- bations, while Ag imposes sparsity that each token is reconstructed using only small number of points from the same union of subspaces. The constraint diag W) = 0 prevents trivial self- reconstruction and encourages the discovery of meaningful cross-token relations.
Eq. (T) is solved via the Alternating Direction Method of Multipliers (ADMM) (Gabay & Mercier 1976). which decomposes the problem into iterative updates of the coefficient matrix, sparse approximation, and the residual. This strategy provides an efficient solver with provable convergence under mild conditions. Details of the update rules are provided in Appendix A
After optimizing the self-expression matrix W. we construct symmetric adjacency matrix W + W and apply spectral clustering on it to assign subspace labels o; to each visual token where each label corresponds to unique subspace. This process partitions the tokens into distinct semantic groups
Sharing expression score. To identify tokens that can serve as reliable visual anchors, we quantify their "sharing strength" by jointly considering how broadly they are distributed within a subspace (subspace cardinality) and how strongly they contribute to reconstructing others (self-exxrression for
row-sum). With the optimized self-expression matrix W and subspace labels c, derive each token that quantifies its importance as a visual anchor. We hypothesize that the best anchors are both structurally representative (i.e., they are important for reconstructing other tokens) and widely
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Published as a conference paper at ICLR 2026
shared (i.e., they belong to large, coherent semantic cluster. Specifically, for token i we define
Ti Eie==a), ri Σ Way
35 Ti
where π, measures the cardinality of token i's subspace (i.e., how many tokens share the same cluster), and r; captures the overall reconstruction strength of i in the self-expression matrix W. The product & therefore favors tokens that are not only central to the reconstruction process but also broadly connected within a subspacc-intuuiively reflecting anchors that are both structurally representative and widely shared.
Since the absolute scale of 8; may vary across samples, we normalize the values to [0, 1] via min max adjustment:
8: min(6)
8: max min(s)++
with > 0 ensuring numerical stability. This normalization step facilitates consistent integration across different instances and prevents dominance by outlier tokens. The resulting score vector Nys thus encodes the relative anchor strength for all visual tokens.
E I
Token-wise scalers. To integrate textual tokens without adding extra emphasis, we extend the visual-only score vector to length N by assigning fixed value of 0 at all text-token positions; here N = Nvss + Nea and Nies denotes the number of text tokens. The resulting E R IxxN is then converted into multiplicative, token-wiss scalers for queries, keys, and values:
TQ 1 aq
YK- 1 ok s".
TV
ov
where aq aK; Rv 0 control the modulation strength, and 1 is an all-ones column vector of length N. In our experiments, the values of a, are set mannually. Note that, when a, = 0, we recover the original, unmodulated attention. With zeros text positions in textual tokens receive additional amplification, whereas higher-scored visual tokens are proportionally upweighted.
3.3 ATTENTION REGUIAARIZATION UNIT
This unit uses the computed scalers y to modify the standard self-attention mechanism, forcing it to prioritize the identified visual anchors. Let per-head queries, keys, and values be Q, K, K.V Nxdx where d, is the head dimension. We introduce test-time attention regularization, consisting of (i) pairwise gating mechanism applied to query-key interactions after the exponential operation, and (ii) a per-token amplification applied to values. The resulting attention matrix E R. N is given by
(n47k) O exp
QKT van
A = E, (nank),, exp Van
(5)
QK'
and the output representation is computed as
Y A (Expand O V).
(6)
Here @ denotes element-wise multiplication, and Expand broadcasts the token-wise scaling vector across the channel dimension of V.
For practical implementation, instead of modifying the definition of softmax(-) :), we adopt mathe- matically equivalent formulation that directly incorporates the gating term into the logits:
A = softmax QKT Vds
+ +ogg(777k))
This equivalence allows us to compute without altering the standard softmax implementation, while maintaining the same gating effect as Eq. (5).
By applying the gate after the exponential, all entries remain nonnegative and the normalization step preserved, while selectively reweighting attention toward shared anchors This formulation
Please refer Appendix D for the ablation study of applying the gate before after the exponential.
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Published as a conference paper at ICLR 2026
Table Performance improvement by VideoAnchor with different MLLMs on VSI-Bench.
Obyy Count
Abe Dist.
Obj Size
Room Sizee
Rel. Dist.
Ref. Dir.
Route Plan
Appe Order
Models
Frames
Avg.
Numericul Answer
Multtppe-Choice Answer
V1.-28
2024b
27.4
24.9
33.8
44.2
7.1
28.6 (+1.2)
29.0
25.4
22.0
32.5
34.6
44.6
34.0
6.5
InternV1.2244 Chen et al. 2024b
8
31.7
26.3
26.9
37.5
25.4
34.5
42.9
35.0
25.4
+ Anchoo
34.5 (+2.8)
43.3
29.7
33.0
26.2
35.2
44.3
40.2
23.8
Intern VL2-8B Chen 2024b
34,6
23.1
28.7
48.2
39.8
36.7
30.7
29.9
39.6
VideoAnchor
37.8 (+3.2)
39.3
30,3
50.5
38.8
38.6
31.0
36.1
37.8
Qwen2.5- VL-3B VL--22
(Bai ct al. 2025
16
26.9
18.6
16.9
16.0
26.5
39.1
45.9
29.2
23.7
27.8 (+0.9)
20.6
17.2
16.0
26.8
40.4
46.7
30.5
24.0
Qwen2.5-VL-7B
(Bai al.
2025
31.8
28.9
15.2
45.8
30.3
37.6
40.5
25.8
30.2
Anchor
16
33,3 (+1.5)
28.3
16.4
46.9
31.6
38.6
43.2
28.9
32.5
LLaVA-Video-7B Zhang al 2025b
16
34.6
44.3
14.6
45.5
25.9
41.3
40,4
36.5
29.5
+ Anclooi
16
35.7 (+1.1)
47.0
15.1
48.3
25.2
43.1
41.1
34.6
31.2
LLaVA-Video-72B Zhang 2025b
39.4
43.1
23.7
54.6
38.0
41.1
35.4
49.2
VjdeoAnchor
16
40.9 (+1.5)
48.5
24.9
56.4
35.5
41.6
36.2
34.5
49.4
achieves the same outcome standard attention while remaining fully compatible with existing Transformer libraries. The final amplification of V by "V ensures that the information from anchor tokens is not only attended to more strongly but also given more weight in the final aggregated output representation. In practice, the Attention Regularization Unit is inserted into every block of the MILM to enhance spatial grounding consistently.
3.4 DISCUSSION
Why does SSC help visual-spatial reasoning? SSC groups tokens into semantic subspaces that capture shared visual content across frames. The resulting self-expression matrix W in Eq. (T) can be regarded as self-supervised form of self-attention (Ji et al. 2017] Zhang et al.] 2019 Vidal 2022). where each token is reconstructed from a sparse set of neighbors within its subspace. This formulation naturally encodes geometric dependencies and higher-level object continuity, providing structural cues that support visual-spatial reasoning tasks.
Why does saareenntttieenes--vaarr attention scaling help? While patch-level attention treats tokens independently and thus often overlooks higher-level semantic continuity, the sharing expression score in Eq. (2) provides complementary signal by quantifying token representativeness. It combines subspace cardinality (m; with reconstruction strength (r;), yielding a graded notion of representativeness that reflects both semantic sharing and structural influence. Incorporating into the attention mechanism biases probability mass toward more representative tokens, ensuring that the aggregated representation emphasizes semantically coherent and structurally central elements. This modulation aligns with the intuition that robust spatial reasoning relies on shared anchors across views or temporal contexts, thereby reinforeing the role of visual evidence in multimodal reasoning.
EXPERIMENT
To assess the effectiveness of visual-spatial reasoning and the generalization ability of VideoAnchor, we evaluate it on three representative benchmarks-VSII-Bendd (Yang al. (2025) (video inputs) and All-Angles-Bend Bench Yeh et al. 2025) (multi-view images) for visual-spatial reasoning, and Video- MME (uu et al. 2025) for general video understanding -ustanndiiinnnnnggggggggg multiple sate-of-the--as MLLMs.
Detailed settings are provided in Appendix B
4.1 BENCHMARK EVALUATION
VSI-Bench. Table D reports results VSI-Bench, which evaluates numerical and multiple-choice visual-spatial reasoning over videos. Across all backbones, VideoAnchor consistently improves performance. For instance, Intern VL.2-8B (8 frames) gains + 16.22 on object counting and +6.2 on

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Published as a conference paper at ICLR 2026
Table 2: Performance improvement by VideoAnchor with different MLLMs on All-Angles-Bench
Cam. Pose
Attribute
Counting
Manipul.
Rel. Dir.
Rel. Dist.
Models
Avg.
Celliple-Choiee Answer
IntemVLL2.52B CCen et 2024a
44.2
26.7
38.6
47.8
31.8
47.5
Vidco
45.5 (+1.3)
61.1
27.9
41.1
47.5
33.3
48.6
IntemVVL...-8B
Chen ct
2024a
48.5
77.5
26.7
48.6
39.9
34.6
51.8
Video Ancloor
50.2 (+1.7)
76.3
24.3
52.4
44.0
38.2
52.5
1ntemVL2.5-338B
Chen ct al.
2024a
53.1
80.5
54.9
45.7
40.9
54.0
VidcoAnchor
(+1.9)
82.3
33.0
$7.0
48.6
43.8
54.7
LLaVA-One Vision-0.5B
[
2025a
42.4
51.4
35.2
25.1
34.0
45.9
AideoAAchoo
43,9 (+1.5)
55.4
36.4
26.0
49.6
37.3
46.2
LLaVA-OmeVissoo-78 Li ct 2025a
44.1
63.7
16.5
37.0
42.4
35.8
50.0
VideoAAchhrr
46.7 (+2.6)
63.3
23.9
40.6
46.0
37.7
52.0
LLaVA-Video-7B Zhung ct al. 20256
43.1
65.5
14.8
37.4
42.6
31.5
47.3
VidcoAnchor
44.3 (+1.2)
41.0
40.1
34.1
50.4
LLaVA-Video-72B Zhang 2025b
49.9
75.7
29.0
40.6
44.5
42.3
52.8
VidcoAnchor
53.0 (+3.1)
77.1
33.0
41.9
45.8
44.1
60.2
room planning. while Qwen2.5-VL-78 shows broad improvements across nearly all categories. including relative direction (+2.7 with 16 frames). Larger models also benefit: VaaaV--ieeo-72 achieves an overall +1.5 improvement under 16-frame input, with noticeable gains on both numerical and multiple-choice tasks. Interestingly, the improvements tend to scale with model size-sialll smaller backbones such as Intern VL2-2B and LLaVA-Video-77 obtaín modest gains (+1.2/+1.1 overall), while larger backbones such as VL2-8B and overall boosts (+3.2 and +1.5, respectively). These results demonstrate not only the effectiveness iisual-spaties reasoning, but also its favorable scaling behavior as
achieve more substantial
of VideoAnchor for video-bass model capacity increases.
All-Angles-Bench. Table summarizes results All-Angles-Bench, which evaluates spatial reasoning from multi-view images. Similar to VSI-Bench, VideoAnchor consistently improves performance across backbones. Taking LLaVA-One Vision from +1.5 on the 0.5B model to +2.6 on the 7B model, while for LLaVA-Video, the improvement grows from +1.2 the 7B model +3.1 1 on the 72B model. These results suggest scaling trend, indicating that Lurgrr-capacit models may be able to leverage VideoAnchor more effectively and yield stronger improvements. Beyond overall accuracy, VideoAnchor also brings targeted gains in challenging categories, averaging +2.6 on counting and +2.5 on relative direction across the listed models. The improvements further extend to camera pose, manipulation, and relative distance, demonstrating that VideoAnchor generalizes well beyond temporal reasoning to strengthen multi-view visual-spatial consistency.
example, the overall gain increases
Table 3; Left: Results on Reasoning).
Right: Results on Video- MMde (Spatial Perception & Spatial
Models
Avg.
Spatial Perception
Spatial Reasoning
Models
Frames
Avg.
IntemVL2-2B
28.0
* VideoAnchor
29.6 (+1.6)
VT..55V.
69.9
63.0
76.8
IntemVLL25-2BB
29.9
Aichor
8
74.4 (+4.55
64.8
83.9
8 VideoAnchor
31.2 (+1.3)
LLaVA- MaaVA-Video-P7
Video-7B
16
71.6
59.3
83.9
1ntemVL2-44B
32.0
+ VideoAnchor
16
73.4 (+1.8
61.1
85.7
+ VideoAnchor
33.4 (+1.4)
Qwen2.5VL-72B
16
75.4
72.2
78.6
IntemVLL..5.4
30,5
+ VideoAnchor
16
80.0 (+4.6)
77.8
82.1
VideoAnchor
33.1 (+2.6)
SPAR-Bench
To validate the generalizability of VideoAnchor across diverse spatial reasoning tasks, we additionally evaluate on SPAR-Bench (Zhang et al., 2025a), a more comprehensive benchmark covering both single-view and multi-view spatial understanding. As shown in Table (left), VideoAnchor consistently improves the average score by +1.3 to +2.6, demonstrating the generalization and the robustness of VideoAnchor challenging spatial tasks.

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Published as a conference paper at ICLR 2026
EMAP
ITO
ITD
Figure 3: Comparison of visual token embedding clustering using k-Means (left) and Sparse Sub- space Clustering (SSC, right). Each row shows one scene, with UMAP projections of visual token embeddings (leftmost column) and corresponding token overlay visualizations on two representative frames (middle and right). Compared to k-Means, SSC yields purer clusters and better captures cross-frame semantic consistency, effectively grouping spatially aligned regions (c.g., sofa, carpet, chairs) across different views.
Vid We further evaluate Aichor the general-purpose Video-MME benchmark, which spans diverse video understanding tasks. Here we report results on those most relevant to spatial understanding. As shown Table (right), VideoAnchor yields consistent improvements across models: Owen2.5VL-772 improves from 75.4 to 80.0 (+4.6) on average, with notable gains in spatial perception (+5.6) and reasoning (+3.5). Intern VL2.5-8B and LLaVA- -iVVA-Vieoo-BB achieve +4.5/+1.8 1.8 gains, respectively, confirming the generality of VideoAnchor across models and scales. These results show that ViceoAnchor enhances both fine-grained spatial perception and higher-Ievel spatial reasoning. Full results on Video-MME are provided in Appendix C
4.2 ANALYSIS
Subspace bridging and clustering quality. Fig. iillustrate that SSC groups semantically related regions (c.g., sofa, carpet, chairs) into stable clusters that persist across video frames, providing coherent anchors for modeling spatial relations and shared object references. Compared with k- Means, which often yields scattered fragmented clusters due its reliance Fuclidean distance in high- nigh-dimensional space, SSC produces clearer semantic boundaries and more coherent token groupings. This advantage further confirmed quantitatively Table 4 where SSC consistently surpasses k-Means across categories, achieving higher overall score on VSI-Bench (37.8 vs. 35.2). Together, these results highlight SSC's ability to form semantically meaningful subspaces that not only persist across frames but also translate into tangible gains in visual-spatial reasoning performance.
Table 4: VSI-Bench performance of VideoAnchor with IntermV2--88 (8 frames) under different attention scaling paradigms: uniform, centroid-based (k-Means), and subspace-based (SSC).
Methods
Baseline
UniformBoost
A-Means
SSC
Avg.
34.6
34.2 (-0.4)
35.2 (+0.6) 37.8 (+3.2)
Cluster to attention enhancement. Fig. presents the visualizations the self-expression row-sum, SSC labels, sharing expression score, text-to-image attention maps, and model responses. The self-expression row-sum quantifies each token's 's overall reconstruction strength when representing others, though small categories may introduce noise. In conjunction with SSC labels that delincate semantically coherent regions across views (e.g., the fireplace and its surrounding structures), enables the computation of the sharing expression score, which serves as robust anchor signal.
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Question: If I'm standing by the TV and facing the entrance, is the fireplace to my left, right, or back? Selt-Expresion Row-Sum
SSC labels
Sharing Expression Score
Basellne: back. (x)
Ours: left. (+)
Figure 4: Example question with visualizations of self-expression row-sum, SSC labels, sharing expression score, and text-to-image attention maps, together with responses from baseline (Intern VL.2-8B) and our Vicho SSC-guided attention enhancement improves visual-spatial reasoning and yiclds the correct answer.
These anchors guide VideoAnchor to reweight tokens and refine the original attention, resulting in sharper and more consistent text-to-image alignments compared with the baseline (InternVL2-8B). Consequently, Video.Anchr attains more accurate visual-spatial reasoning and produces correct responses, whereas the baseline tends to disperse attention and generate errors.
Effect of scaling positions (Q/K/V) of VideoAnchor. 5 the impact of applying scaling different attention components. Scaling only on yields the largest gain (+1.9). while adding Q and V further improves performance (+2.2), with the best result from scaling all Q /V (+2.8). This suggests that value scaling primarily enhances visual evidence, while query and key scaling provide complementary benefits that make spatial reasoning more consistent and stable.
Table 5: Ablation of Q/K/V scaling in VideoAnchor on VSI-Bench (Intern VL2-4B, 8 frames).
Methods
Baseline
Scaling on V Scaling on Q&V
Scaling on Q&K&
Avg.
31.7
33.6 (+1.9)
33.9 (+2.2)
34.5 (+2.8)
Ablation of the Sharing Expression Score. Table 6 presents the ablation results the computation of the sharing expression score. Using only the cluster number results in a slight improvement (+1.0), while relying solely self-expression coefficients yields results close to the baseline (+0.4). Combining both yields the highest improvement (+3.2), highlighting their complementarity: self- expression characterizes token-level reconstruction strength, whereas cluster number reflects the stability and representativeness of subspaces. Small clusters may introduce noise, and large clusters may dilute distinctiveness; their joint consideration balances these effects and provides more reliable guidance signal. This demonstrates that the sharing expression score not only stabilizes anchor selection but also enhances the consistency of attention modulation across views, thereby reinforcing visual-spatial reasoning.
Table 6: Ablation of sharing expression score computation in VideoAnchor on VSI-Bench.
Methods
Intern IntemVL2-88 frames)
Cluster number
Self-expression
Cluster number Self-expression
Avg.
34.6
35.6 (+1.0)
35.0 (+0.4)
37.8 (+3.2)
Robustness frame sampling. Table 7 reports results on VSI-Bench with different frame sam- pling settings. VideoAnchor consistently improves performance across 8/16/32/64 frames for both Intern VL2-8B and Qwen2.5-VL-7B. These results indicate that the benefits VideoAnchor are stable under varying numbers of input frames, demonstrating robustness to frame sampling strategies. Compared to Video-RI (Feng et al.) 2025). a CRRPO-fin-ttnnl Qwen2.5- VL model, our method maintains robustness across different numbers of input frames, whereas Video-RI surpasses only under the specific setting was fine-tuned on. Please refer to Appendix F for results and further analysis.

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Published as a conference paper at ICLR 2026
Table VSI-Bench performance with VideoAnchor under different frame sampling settings.
Sampling Frames
Sampling Frames
AinerVLL--BB
34.6
36.8
37.4
37.6
@wen2.55V1.22
28.3
31.8
36.4
VidenAnchor (-32 38.1 (+1.) (+1.))
39.8
Ancho
(+1.2)
33.3 (+1.5) (+1.0)
Sensitivity analysis of aq/K/V. To further validate the robustness in aQ/K/V setting of VideoAn- chor, we conduct experiments with Intern VL.2-4B (8 frames) on VSI-Bench. As detailed in Table g we varied aq, 0K, and ay with a step size of 0.2 within the ranges of [1.5, 3.5], 3.0). and [2.0, 4.0], respectively. The results suggest that the model performance remains relatively consis- tent across these variations, with scores fluctuating within the range of 33.6 to 34.5. Furthermore, these configurations consistently show improvements over the baseline score of 31.7, implying that VideoAnchhr may not be overly sensitive to precise hyperparameter tuning.
Table 8: Sensitivity analysis of aQ/K// VSI-Bench with Intern VL2-4B (8 frames).
Tuning Parameters
Values
VSI-Bench Score
Baseline
31.7
eq
1.5-3.5
34.3-34.5 (42.6-2.8)
0KK
1.0-3.0
34.0-34.5 (+2.3-2.8)
2.0-4.0
Runtime comparison. In VideoAnchor, the iterative ADMM optimization in SSC introduces additional latency and additional complexity (O(22 with the number of visual tokens N) compared to the baseline. However, to mitigate this, we implemented the ADMM solver using CuPy for efficient GPU acceleration, which significantly reduces the computational bottleneck. Furthermore, we notice that VideoAnchor is robust to the ADMM convergence threshold (e), thus choosing suitable relaxed thresholds reduces runtime with marginal impact on the performance. Measured on NVIDIA V100 GPUs, the optimized VideoAnchor increases inference latency by only 0.8s per sample while yielding a substantial +3.2 gain on VSI-Bench (as shown in Table 9). demonstrating a highly acceptable trade-off between efficiency and accuracy.
Table 9: The runtime and the performance comparisons of using VideoAnchor VSI-Bench with Intern InternVI.2288 VL.2-8B (8 frames).
Models
Runtime (s/iter)
VSI-Bench Score
Intern L2-8B 3B& (8 frames)
6.0
34,6 37.8 (+3.2)
VideoAnchor
6.8
5 CONCLUSIONS
In this work, we presented VideoAnchor, a plug-and-pla module that enhances visual-spatin reasoning in multimodal large language models at time. We identified that MLLMs' reasoning failures often stem from an inability to ground themselves in consistent visual evidence across frames. To address this, VideoAnchor leverages the self-expressiveness property from sparse subspace clustering to identify and amplify shared visual structures, effectively anchoring the model's attention without requiring any retraining. Across visual-spatial reasoning benchmarks, VideoAnchor achieves consistent improvements from lightweight to large-scale models, validating our core hypothesis and highlights test-time attention regularization based on semantic subspaces as a promising and efficient direction for improving the visual-spatial reasoning.
ACKNOWLEDGMENT
This work supported by National Natural Science Foundation of China (NSFC) under Grant 62225208 and 62521007, and CAS Project for Young Scientists Basic Research under Grant No. YSBR-117.
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Reproducibility Statement. The source code implementing VideoAnchor has been submitted in the supplementary material. Experimental settings and part of the hyperparameters are provided in the main text and the appendix to facilitate the reproducibility of our results.
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A ADMM SOLVER FOR SSC
To solve Eq. I we introduce auxiliary variables and apply the Alternating Direction Method of Multipliers (ADMM). The optimization problem is decomposed into three main steps at each iteration:
Update of coefficient matrix A:
A - (Ax X xvs+ (A, X (x) +nn') 1111--))) -A:). (8)
where is the penalty parameter and A1, Ag dual variables.
Update of sparse representation Witer via soft soft-thresholding:
Witer + (1 + A Witer + Witer - diag Witer):
(9)
where Sn(v) sign(v) max [e] 11 0) denotes the element sonn operator.
Update of residual E:
E -- Sx./A. (Xvis - Av.
(10)
Dual updates:
A + Ai + 1(A 1).
(11)
A₂ + A + p(A A-Wieer Witer)
(12)
The iterations are repeated until convergence, typically checked via | - Witer Imx <. The final W iter is used the self-expression matrix W.
B DETAILED SETTINGS
For the Sparse Subspace Clustering (SSC) module, adopt the ADMM solver described in Ap- pendix [A] For most experiments, the penalty parameter p is set to 300, and the sparsity weight Aę is to 800, while the reconstruction weight Åe set to 800. The convergence tolerance is chosen as 2e- and the maximum number of iterations is capped at 10000.
After obtaining the self-expression matrix W, we apply column-wise thresholding to retain only the most significant reconstruction coefficients, controlled by a threshold parameter threshold c (set to our experiments unless otherwise specified). We then construct an affinity matrix by symmetrization W + W Spectral clustering is finally applied with the number of subspaces fixed 24. In our implementation, we set the nearest-neighhor parameter retained when building the similarity graph, and clustering solved via normalized cuts.
meaning that all edges
our VSI-Bench implementation, the adopted values of aq2, OK; at for representative MLLMs are as reported in Table [00 We also observe that moderate variations in these values do not substantially affect the performance gains, suggesting the robustness of Video to this choice.
In all experiments, adopt do sample False, num,cams 1 during inference. Under this setting, sampling-relan parameters such as temperature and top-p are not used for generation and thus have effect the output. Consequently, the models' responses are fully deterministic and consistent across runs, eliminating the possibility of inference variance.
Table 10: Scaling coefficients (aq, ak, av) for representative MLLMs in VideoAnchor on VSI- Bench.
Models
aQ
0K
av
Intern VL.2-4B
2.5
2.0
3.0
Intern V1.2-8B
4.0
9.5
2.5
LLaVA-Video-7B
2.0
2.0
0.5
Owen2.5VL-78
3.5
3.5
0.25
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C VIDEO-MME RESULTS
Table presents results on the Video-MME benchmark. VideoAnchor consistently improves performance all evaluated models, with gains ranging from +0.3 +1.2. The improvements are especially notable on spatial-related tasks in Table S (right), confirming that Vicho is primarily designed to strengthen visual-spatin spatial perception and reasoning.
Table 11: Results on Video-MME.
Models
Frames
Avg.
Short
Medium
Long
Intern VI.2-4B
8
49.8
60.0
47.2
42.2
VideoAnchor
8
51.0 (+1.2)
61.6
48.2
43.1
InternVL.2.58BB
8
57.9
68.1
56.6
49.1
VideoAnchor
8
58.2 (+0.3)
68.2
56.7
49.6
LLa LLaVA-Video-7B
16
59.9
70.9
58.7
50.1
* VideoAnchor
60.4 (+0.5)
71.3
59.4
50.3
Qwen2.5VL-72B
16
63.5
71.8
61.8
56.9
VideoAnchor
16
63.8 (+0.3)
72.4
62.1
57.1
D SCALING Q&K BEFORE VS. AFTER SOFTMAX
Table 12 compares the effect of applying Q&K scaling before versus after the Softmax operation. Applying the scaling before Softmax severely degrades performance (5.2, -29.4), as it distorts the similarity scores and destabilizes the resulting attention weights. contrast, applying the scaling after Softmax yields clear improvement our design. By introducing the gate after the exponential, all entries remain nonnegative and the normalization step is preserved, while attention be selectively reweighted toward shared anchors. This design ensures that Vicho emphasizes spatially coherent tokens without disrupting the overall distribution of attention weights.
the baseline (+3.2), confirming the effectiveness of
Table 12: Ablation study on applying Q&K scaling before vs. after the Softmax operation in VideoAnchor on VSI-Bench.
Methods Avg.
Intern VL.--8B (8 frame)
Before softmax(.)
After softmax(()
34.6
5.2 (-29.4)
37.8 (+3.2)
E EFFECT OF SUBSPACE AND BOOSTED SUBSPACE COUNTS
Table 13 studies the role of subspace numbers and boosted subspace counts. On the left, performance increases as the number of subspaces grows, reaching the best result at 24 (37.8), which suggests that 24 subspaces provide the most suitable semantic partitioning for visual-spatial reasoning this scenario. Intuitively, using too few subspaces may merge distinct visual patterns together, while too many can fragment coherent regions; around 24 appears to strike balance between these extremes. On the right, boosting all subspaces consistently achieves the highest score, whereas partially boosting them leads to slight drops, indicating importance enhancing every subspace rather than a subset.
Table 13:
Ablation study on the number of subspaces (left) and boosted subspaces (right) on VSI- Bench with Intern VL2-8B (8 frames). In the left table. all subspaces boosted by default, and performance is reported as the number subspaces varies. In the right table, the total number of subspaces is fixed at 24, and only subset is boosted, chosen in descending order of token frequency.
Subspace
12
16
28
32
Boosted Subspace
24
22
18
Average
36.2
36.8
37.3
37.8
36.8
37.3
36.7
Average
37.8
37.6
37.3
37.4
37.2
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F COMPARISON WITH GRPO-TRAINED MLLMS
Table 14 compares VidcoAnchor and Video-RI-7B, both based on the Qwen2.5-VL-7B backbone. With 16 input frames, Video-RI I achieves higher score (34.6 vs. 33.3), reflecting the advantage of its GRPO-bbsed training tailored this setting. However, when the number of frames increases, VideoAnchor demonstrates stronger generalization: at 32 frames it clearly surpasses Video-RI (37.4 4 vs. 35.8), even slightly outprrormmng the 64-frame variant of Video-RI (37.4 vs. 37.1). A plausible explanation is that Video-R1 is optimized during training with a fixed 16-frame configuration, which limits its adaptability to other sampling strategies. In contrast, VideoAnchor avoids such overfitting by directly reinforeing spatially consistent cues at test time, thereby offering more robust and efficient solution for enhancing visual-spatial reasoning under varying frame settings.
Table 14: Video-R1-7B (GRPO-trained with Qwen2.5-VL) vs. VideoAnchor (test-time).
Models
Frames
VSI-Bench Score
Qwen2.5-VI.
16
31.8
Qwen2.5-VL, + VideoAnchor
16
33.3
Video-R1
16
34.6
Video-R1 + VideoAnchor
16
36.2
Qwen2.5-VL.
32
36.4
Video-R1
32
35.8
Video-R1
64
37.1
Qwen2.5-VL. + VideoAnchor
32
37.4
G TEXT-TO-IMAGE ATTENTION COMPARISON
As shown in Fig. 5 VideoAnchor enhances attention effectively semantically relevant regions compared to the baseline. In particular, it strengthens focus on objects such as the cabinet and shoes, leading to sharper and more coherent visual grounding.
Baseline
Ours
Figure 5: Text-to-image attention comparison of Intern VL2-8B (baseline) and with VideoAnchor (ours). Values are averaged across text tokens based on first-layer attention weights.
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Affinity Matrix
Atleation Differences (Ours Baseline)
Text Tokens
Figure 6: Visualization of subspace structures and attention modulation. Left: Affinity matrix of visual tokens from InternVL2-885 showing 10 clustered subspaces. Right: Attention differences between VideoAnchor and the baseline (red increase, blue decrease). Tokens are reordered with visual tokens (grouped by subspace) preceding text tokens; green dashed lines mark subspace boundaries.
H SUBSPACE-AWARE ATTENTION MODULATION
As shown Fig. VideoAnchor substantially reduces self-attention among text tokens while consistently enhancing attention towards visual tokens. This shift indicates that the model allocates less capacity to redundant linguistic priors and instead emphasizes visual grounding. Moreover, the enhancement is not uniformly distributed: attention gains concentrated along subspace boundaries, suggesting that Vicho strengthens interactions both within and across subspaces. This demonstrates that subspace-aware modulation effectively bridges semantic structures in the visual domain while mitigating the dominance of textual bias.
I COMPATIBILITY WITH FLASHBIAS
VideoAnchor introduces a multiplicative re-weighting of attention, which can be equivalently ex- pressed an additive log-bias added before the softmax, as provided by Eq, 7 In principle, such bias could be injected into FlashAttention by converting it into dense altention_mask: However, this approach leads to prohibitively high memory consumption, especially for long video sequences.
To address this, we refer to recent proposed FlashBias (Wu et al., 2025b), a high-performance inference framework designed integrate prior-driven attention biases compatible with efficient backends like Triton or PoTch''' SDPA. Given modified attention
QK dn
0 = softmaz(
+ BVV
when the attention bias is low-rank and can be decomposed as B Q Qiass KT bias FlashBias provides kernel implementation compatible with FlashAttention2 wwttout any dense mask con- struction-and the inference cost becomes essentially identical to pure FlashAttentin (no extra memory overhead, no kemel modification).
The introduced attention bias in VideoAnchor is log(TQ which can be exactly decomposed with rank 2 by:
log(royk) [log(7Q), Angxi][[nn [nox:]]nnxx.oo[((x]]]]]
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This allows us directly adopt the FlashBias kernel and guarantees: need for dense masking, no extra memory overhead, and the inference efficiency that could match standard FlashAttention. Thus, VideoAnchor naturally fits into the class of attention-biar mechanisms supported efficiently by FlashBias, providing both theoretical correctness and practical compatibility with modern high- performance inference frameworks. With FlashBias, based on Qwen2.5-VL- (16 frames) on VSI-Bench using NVIDIA 80G A100 GPUs. Note that for this kernel-level comparison, the SSC clustering results were pre-cached. The runtime, GPU memory allocation, and the results are provided in Table 15 VideoAnchor incurs negligible latency increase and maintains the GPU memory consumption compared the baseline, validating that attention regularization units in Vicho are compatible with high-performance inference frameworks.
test VideoAnchor's inference efficiency
Table 15: The runtime GPU memory/ / VSI-Bench performance of VideoAnchor with FlashBias.
Models
Runtime (sfiter)
GPU Memory (GB)
VSI-Bench Score
Swen2.5-VL-7BB (16 frames, FlashAttention) ViceoAnchhr (16 frames, FlashBias SDPA)
5.8
38.7
31.8 33.3 (+1.5)
6.1
38.7
Figure 7: Failure Case 1. Question: How many sofa(s) are in this room? GT: 3. Baseline response: 1. VideoAnchor response: 2.
Figure 8: Failure Case 2. Question: I am standing by the bathtub and facing the bed, is the table to my left, right, back? An object is to my back if I would have to turn least 135 degrees in order to face it. GT: Left. Baseline VideoAnchor: Back.
J FAILURE CASES
While VideoAnchor demonstrates significant improvements in visual-spatial reasoning, still has some limitations. To better understand the generalization boundaries of VideoAnchor, we analyze two representative failure cases in VSI-Bench with VLternVL2-8B (8 frames). In failure case as shown in Fig. 7 the model may exhibit counting ambiguities; this stems from the intrinsic mechanism of Sparse Subspace Clustering (SSC), which tends to merge highly correlated visual features into single subspace, thereby blurring the attention boundaries between distinct but near-identical instances. In failure case 2, as shown Fig. X the model struggles with questions regarding fleeting events that occur between sampled frames. This limitation is attributed to the discrete nature of temporal sampling rather than the clustering module itself, as Viceo functions as a feature enhancement mechanism and cannot recover visual information physically absent from the input grid.
K COMPARISON WITH USING TEXT PROMPTS
We' ve conducted experiments with Intern VL2-4B/8B on VSI-Bench using the prompt "Please anchor common objects across frames for reference." ", and the results are provided in Table 16 Explicitly prompting MLLMs brings marginal gains on VSI-Bench, while VideoAnchor improves significantly under the same settings.
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Published as a conference paper at ICLR 2026
Table 16: Comparison of VideoAnchor with using text prompts.
Models
VSI-Bench Score
VL2-4B (8 frames)
31.7
+Prompt
32.1
+ VideoAnchor
34.5
Intern VL2--B -B (8 frames)
34.6
+Prompt
35.1
+ +VceeoAnhhor
37.8
L STATEMENT ON LLM USAGE
We acknowledge the use of large language models (LLMs) exclusively for writing assistance, limited polishing grammar and style. No part of the research design, experiments, analysis, or core contributions relied on LLMs, and all text was critically reviewed and finalized by the authors.
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Reviews:
[{"review_id": "0gMy9VaG85", "reviewer": "Reviewer_H6LV", "summary": "The paper considers the visual-spatial reasoning in the context of MLLMs, and proposes VideoAnchor, a plug-and-play module, to address the limitation of current MLLMs that they lack a mechanism to consistently preserve visual cues cross frames. Specifically, the paper aims to draw connection between the self-expressive property in sparse subspace clustering (SSC) and attention mechanisms in Transformers. To implement this, the proposed VideoAnchor consists of two units: (1) subspace-to-scaler unit, and (2) attention regularization unit. Methods and empirical evaluations are presented.", "questions": "(As mentioned in \"Weakness\" Section) Can author(s) provide further discussion/justification on the tradeoff between expected performance gain and the retraining effort?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Overall, the paper is easy to follow. The problem setting, the intuition/motivation (that token organization in the semantic subspace helps), the technical approach, and the empirical evaluations are presented in a relatively clear and organized manner. The utilization and illustration of SSC (e.g., the discussions in Section 3.4, illustrations in Section 4.2) provide different perspectives and justify the incorporation of two units in VideoAnchor. Experimental results also show improvements.", "weaknesses": "The improvement over baseline is relatively modest. This is not entirely unexpected now that the module does not require re-training. That being said, I am wondering if author(s) can share thoughts on whether the plug-and-play module can have a more \"integrated\" version, in the sense that how would VideoAnchor operate if (lightweight) retraining is feasible. Additional discussion on the tradeoff between expected performance gain and the retraining effort may be preferable (e.g., to better justify the contribution), in addition to the claim that VideoAnchor is plug-and-play.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "WPT6d9YoMB", "reviewer": "Reviewer_84JG", "summary": "This paper introduces *VideoAnchor*, a plug-and-play attention modulation module designed for test-time enhancement. The core idea is to cluster multi-frame or multi-view visual tokens into semantic subspaces using sparse subspace clustering, compute a *shared expression score* for each token, and convert it into scaling factors applied to QKV in the attention mechanism.  Extensive experiments on VSI-Bench, All-Angles-Bench, and Video-MME demonstrate consistent improvements across multiple multimodal LLM backbones, supported by ablation studies and visual analyses.", "questions": "1. What is the computational complexity of VideoAnchor in terms of frames, tokens, and subspace counts? A runtime comparison with DC2 and VisionFuse would clarify its practicality.\n2. Does the sharing expression score bias attention toward large clusters, potentially overlooking small but semantically critical objects? Could weighting or normalization mitigate this issue?\n3. When strong appearance changes or motion blur occur, how does the linear self-expression assumption of SSC hold? Have the authors considered nonlinear or graph-based subspace alternatives?\n4. Could VideoAnchor be combined with learning-based methods such as Video-R1 or GRPO fine-tuning to further improve robustness and visual grounding?\n5. Across benchmarks,VSI-Bench, All-Angles-Bench, and Video-MME,which specific sub-tasks (e.g., spatial relation, counting, manipulation) benefit most, and does the improvement mainly come from enhanced perception or reasoning?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. A training-free and model-agnostic *subspace-to-scaler* mechanism that can be universally applied across architectures, linking self-expressive subspace modeling with transformer attention in a novel way.\n2. Comprehensive experimental validation on diverse benchmarks with consistent gains and detailed ablations covering QKV scaling, clustering methods, and subspace counts.", "weaknesses": "1. The comparison with other training-free enhancement approaches (e.g., DC2’s high-resolution perception [1], ControlMLLM’s visual prompting [2], VisionFuse’s multi-encoder fusion [3]) lacks equal experimental settings and quantitative cost-performance analysis.\n2. Missing a deeper comparison or hybridization with learning-based reinforcement approaches such as Video-R1 or T-GRPO [4], the paper only briefly mentions them in the appendix without unified benchmarks.\n3. The failure cases and generalization limits (e.g., occlusions, fast viewpoint changes, low-texture regions) are underexplored, with only anecdotal visual evidence.\n4. The SSC plus spectral clustering steps could introduce high latency when processing long videos or thousands of tokens, yet no systematic runtime or throughput evaluation is provided.\n5. Hyperparameter sensitivity is superficially treated. Although claimed “robust,” only fixed αQ/αK/αV and subspace counts are reported, without cross-dataset robustness or automatic selection strategies.\n\n[1] Li et al. *DC2: Decoupled Cross-resolution Conditioning for High-Resolution Multimodal Models*.\n\n[2] Hu et al. *VisionFuse: Multi-Encoder Fusion for Multimodal Reasoning*.\n\n[3] Xu et al. *ControlMLLM: Test-time Control for Multimodal Large Language Models*.\n\n[4] Wang et al. *Video-R1: Reinforcing Video Reasoning in Multimodal Large Language Models*.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "gnyozai4dX", "reviewer": "Reviewer_tF4U", "summary": "This paper introduces VideoAnchor, a plug-and-play module designed to enhance visual–spatial reasoning in multimodal large language models (MLLMs) during inference. The key idea is to exploit the self-expressiveness property of Sparse Subspace Clustering (SSC) to uncover consistent visual structures across frames, and then translate these structural relations into token-wise scaling factors that modulate attention.\n\nVideoAnchor consists of two components: 1. Subspace-to-Scaler Unit – Applies SSC to visual tokens extracted from multiple frames, computes each token’s “anchor strength” based on its intra-subspace connectivity, and generates scaling coefficients for the Q/K/V projections. 2. Attention Regularization Unit – Injects these scalers into the attention computation, amplifying stable visual regions and mitigating text-dominated biases without retraining. The method operates entirely at inference time, is model-agnostic, and can be seamlessly integrated into all self-attention layers of various MLLMs. Experiments across multi-frame and multi-view benchmarks demonstrate consistent accuracy gains (typically +1–3%).", "questions": "1) Have the authors verified compatibility with high-performance inference frameworks (e.g., vLLM, FlashAttention, xFormers)? Does the multiplicative scaling require any modification to fused attention kernels or memory layouts?\n\n2) The choice of subspace number and the scaling coefficients (α_Q, α_K, α_V) lacks principled derivation or sensitivity analysis beyond empirical tuning.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1）Clear Conceptual Novelty. The paper introduces a distinctive idea by incorporating the self-expressiveness property of Sparse Subspace Clustering (SSC) into Transformer attention modulation. This provides a theoretically grounded and interpretable way to reinforce visual anchoring during inference.\n\n2) Training-Free and Plug-and-Play Design. VideoAnchor operates entirely at inference time without parameter updates or retraining. Its plug-and-play nature makes it broadly compatible with various multimodal large language models (e.g., InternVL, LLaVA, Qwen-VL).\n\n3) Strong Interpretability. The approach offers explicit geometric and semantic interpretability: subspace consistency identifies stable visual anchors, and attention amplification directly follows from these structural relations, unlike opaque learned gates.\n\n4) Comprehensive Ablation Analysis. The paper includes systematic ablations on Q/K/V scaling, gating placement, and subspace cardinality, providing convincing evidence for the soundness of each design choice.", "weaknesses": "1）Lack of Quantitative Efficiency and Complexity Analysis. Although the paper emphasizes that VideoAnchor is lightweight, it provides no explicit measurements of runtime, FLOPs, or latency, leaving the actual computational efficiency unverified. While the ADMM optimization process is described, there is no formal analysis of its convergence behavior or computational scaling under realistic token counts.\n\n\n2) No Discussion of Scalability to Long Videos or High-Resolution Inputs. Since the SSC step scales quadratically with the number of tokens, potential issues in long-sequence or high-resolution scenarios remain untested and unexplored.\n\n\n3) Empirical Scope Limited to Spatial Reasoning Tasks. VideoAnchor appears to be a more general approach, but the evaluation focuses on visual–spatial reasoning; generalization to broader multimodal reasoning tasks (e.g., temporal dynamics, abstract understanding) remains unclear.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "N7LjcYmtc3", "reviewer": "Reviewer_G6Ba", "summary": "This paper proposes VideoAnchor, a training-free module that improves visual–spatial reasoning in MLLMs by anchoring attention on shared visual cues across frames. It leverages sparse subspace clustering to identify consistent visual structures and regularizes attention at inference time. Evaluated on VSI-Bench, All-Angles-Bench, and Video-MME, it achieves limited performance gains without retraining.", "questions": "Please refer to the **Weaknesses**", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1.VideoAnchor operates purely at inference time, making it easily applicable to existing MLLMs without fine-tuning.\n\n2.Comprehensive analysis: Includes ablation studies and visualization to explain the mechanism’s effect on attention coherence.\n\n3.This work establishes a connection between sparse subspace clustering and attention mechanisms, offering a principled way to preserve visual consistency across frames.", "weaknesses": "1. The method primarily targets pairwise spatial relations by anchoring one shared object. It remains unclear how this mechanism would generalize to broader scenarios such as navigation, counting, or reasoning over complex multi-object relations involving three or more entities.\n\n2. The paper evaluates InternVL with 8-frame input and Qwen2.5-VL/LLaVA-Video with 16 frames on VSI-Bench, but does not justify this inconsistency. Moreover, since most videos in VSI-Bench and Video-MME last longer than 60s, sparse frame sampling may miss visual continuity, raising questions about how anchoring remains effective when frame overlap is minimal.\n\n3. While VideoAnchor fits multi-view scenarios, evaluation across more multi-view benchmarks such as SPAR-Bench [1] would strengthen claims of generalizability. Also, models vary across benchmarks; evaluating the same model series would provide a clearer understanding of cross-task generalization.\n\n4. Important inference details (e.g., temperature, rounds) are missing. It is unclear whether the observed gains are statistically significant or could be influenced by inference variance rather than the VideoAnchor mechanism itself.\n\n5. Since VideoAnchor modifies attention internally, I am curious how explicit prompt-based anchoring performs—for instance, instructing the model to first identify an anchor object and then describe spatial relations. \n\n[1] From Flatland to Space: Teaching Vision-Language Models to Perceive and Reason in 3D, 2025.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)