Readout Blindness: VLM Scores Miss the Spatial Direction Their Frozen Encoders Retain

Guangyuan Li; Tianming Du; Yan Jiang; Bihan Wen; Jiancheng Yang

cs.CVOct 5, 2026

Readout Blindness: VLM Scores Miss the Spatial Direction Their Frozen Encoders Retain

Authors: Guangyuan Li, Tianming Du, Yan Jiang, Bihan Wen, Jiancheng Yang

Organizations: ELLIS Institute Finland · Aalto University · University of Oulu · Nanyang Technological University

Abstract

CLIP-like vision-language models remain a cornerstone of multimodal systems, yet their scores stay near chance on directed spatial relations, such as whether one object is left of another. We call this failure readout blindness and analyze, theoretically and empirically, why deployed scores miss the direction: when scoring rules treat the subject and object symmetrically, direction cancels regardless of encoder training. Guided by this analysis, we introduce Antisymmetric Displacement Readout (ADR), which aligns caption words with image patches in the frozen features and scores each relation by the signed displacement between matched object centroids. Notably, ADR succeeds without additional training or learned parameters, thereby demonstrating that directional information remains in the frozen encoder. However, text and world priors can inflate accuracy, so we further introduce prior deflation, which measures the benefit of the image-text pairing as the grounded gain over a null that pairs each item with an unrelated image. Extensive experiments across encoder families show that ADR substantially improves over deployed scores, which remain near chance on most direction-balanced sets even for fine-tuned encoders. Compared with more complex readouts, ADR outperforms the evaluated MLLM likelihood readouts and is competitive with their chat inference at a small fraction of the computation. These results support our claim that directional information can be recovered from frozen features by an appropriate readout. Our implementation and evaluation kit will be publicly available.

Figures & tables

Figure 1: Readout blindness and ADR. (a) Choosing between captions with opposite relation directions. The deployed score gives near-equal scores to both, while ADR recovers the direction from the signed centroid displacement $D$ from the same frozen encoder. (b) Grounded gain on VG-Relation left/right. Dots mark the best readout per group, and segments show the range.

Figure 2: ADR (top) versus deployed score (bottom) on the same frozen VLM. ADR (1) computes patch–word similarity $\mathbf{S}$ , (2) keeps the excess Sinkhorn coupling $\tilde{\pi}$ , (3) localizes the subject and object centroids, and (4) scores their signed displacement $D=\mathbf{e}_{r}^{\top}(\mathbf{c}_{s}-\mathbf{c}_{o})$ along the relation axis. The deployed score compares pooled features (CLS token for CLIP, attention-pooled token for SigLIP) and gives near-equal scores to a caption and its argument swap, leaving direction ambiguous.

Subset	Random	Metric	CLIP	NegCLIP	SigLIP2	LLaVA-1.5	Qwen3.6
Subset	Random	Metric	CLIP	NegCLIP	SigLIP2	likelihood	likelihood
Prior-based subset	50.03	acc.	72.71	85.14	62.07	93.26	93.99
VALSE actant swap		n.acc.	41.31	62.80	28.35	88.51	88.41
		dev.	$-8.7$	$+12.8$	$-21.7$	$+38.5$	$+38.4$
		gain	$+31.4$	$+22.3$	$+33.7$	$+4.8$	$+5.6$
Direction-balanced subset	50.01	acc.	49.64	50.26	49.95	65.80	74.76

Table 1: Prior effects on a prior-biased subset (VALSE actant swap) and a direction-balanced subset (VG-Relation left/right). Acc. is accuracy with the original images. n.acc. is accuracy under the derangement null (Sec. 6 ). Random is accuracy from uniformly random predictions. Dev. (

\text{n.acc.}-\text{random}

) measures deviation from random guessing. Gain is grounded gain (

\text{acc.}-\text{n.acc.}

) and measures the benefit of the original image-caption alignment. Accuracies are reported in percent (%), and dev. and gain in percentage points. Full results on all eight subsets are given in Tab. 5 .

Figure 3: Prior deflation compares accuracy under the original pairing and a derangement null. Their difference is grounded gain.

Readout	VG-Rel L/R			COCO			WU-A			WU-B			VSR
Readout	acc.	n.acc.	gain	acc.	n.acc.	gain	acc.	n.acc.	gain	acc.	n.acc.	gain	acc.	n.acc.	gain
Random	50.01	50.01	0.0	49.99	49.99	0.0	25.02	25.02	0.0	50.01	50.01	0.0	50.01	50.01	0.0
CLIP	49.64	49.69	$-0.1$	45.30	47.28	$-2.0$	26.96	25.98	$+1.0$	53.67	52.83	$+0.8$	53.87	53.21	$+0.7$
ADR- $\tilde{\pi}$	90.45	49.03	$+41.4$	92.33	51.73	$+40.6$	31.62	25.74	$+5.9$	93.22	52.26	$+41.0$	73.86	47.79	$+26.1$
ADR- $\pi_{d}$	90.22	49.44	$+40.8$	94.06	52.72	$+41.3$	41.18	25.25	$+15.9$	98.02	50.00	$+48.0$	74.94	47.88	$+27.1$
LAION-2B	49.13	49.77	$-0.6$	43.07	47.28	$-4.2$	27.70	26.23	$+1.5$	50.00	50.28	$-0.3$	51.96	52.96	$-1.0$

Table 2: Random guess, deployed readouts and ADR across nine model families on five direction-balanced sets. Each subset reports accuracy with the original images (acc.), derangement-null accuracy (n.acc.), and grounded gain (gain). Accuracies are in percent (%) and gains in percentage points. Grey rows show deployed readouts, followed by ADR-

\tilde{\pi}

and ADR-

\pi_{d}

on the same encoder and items. Red highlights grounded gain, the main metric for comparing readouts.

Readout	VG-Rel L/R			COCO			WU-A			WU-B			VSR
Readout	acc.	n.acc.	gain	acc.	n.acc.	gain	acc.	n.acc.	gain	acc.	n.acc.	gain	acc.	n.acc.	gain
Deployed readouts
CLIP	49.64	49.69	$-0.1$	45.30	47.28	$-2.0$	26.96	25.98	$+1.0$	53.67	52.83	$+0.8$	53.87	53.21	$+0.7$
SigLIP2	49.95	50.54	$-0.6$	49.01	45.05	$+4.0$	29.17	24.75	$+4.4$	51.69	48.87	$+2.8$	54.70	52.96	$+1.7$
Half-crop
CLIP	83.67	49.16	$+34.5$	82.92	55.45	$+27.5$	44.61	22.30	$+22.3$	100.00	50.00	$\mathbf{+50.0}$	70.52	47.88	$+22.6$

Table 3: Deployed readouts, alternative readouts and ADR on five direction-balanced sets. Each subset reports accuracy with the original images (acc.), derangement-null accuracy (n.acc.), and grounded gain (gain). Accuracies in percent (%) and gains in percentage points. All readouts use the same evaluation items as Tab. 2 . Red highlights grounded gain. Bold marks the highest gain.

Figure 4: ADR predictions and reliability. (a) Correct predictions for horizontal, vertical and depth relations. Red and blue green maps show subject and object excess mass. Circles mark centroids, and arrows point from object to subject. Margin is the score difference between correct and incorrect captions, $D(C^{+})-D(C^{-})$ for ADR. (b) A localization failure of ADR- $\tilde{\pi}$ . Curves show the fraction of each word’s excess mass at each image height. The diffuse mass for "dining" produces an incorrect centroid order and prediction. (c) Accuracy on the binary direction-balanced sets after retaining samples with the largest absolute margins. Increasing the margin threshold retains fewer samples but yields higher ADR accuracy, showing that its margin helps select reliable predictions.

Appendix figures & tables12 assets

Supplementary material from the paper’s appendix.

Appendix

	CLIP ViT-B/16	SigLIP2-So400m
Median text difference	0.2251	0.2494
Pairs with identical text features	0	0
Global score ties	0	0
Global real / null accuracy	49.64 / 49.69	49.95 / 50.54
Global wrong, ADR- $\tilde{\pi}$ correct, item count	1785	1799
Median text difference, global wrong and ADR- $\tilde{\pi}$ correct	0.2264	0.2505

Appendix

Table 4: Global cosine diagnostics on VG-Relation left/right (

n=3918

). Text differences are Euclidean distances between unit-norm caption features. The last two rows restrict the comparison to items correctly ranked by ADR-

\tilde{\pi}

. Accuracies are percentages.

Direction-balanced subsets
Subset	random	metric	CLIP	NegCLIP	SigLIP2	LLaVA-1.5	Qwen3.6
Subset	random	metric	CLIP	NegCLIP	SigLIP2	likelihood	likelihood
VG-Rel left/right	50.01	acc.	49.64	50.26	49.95	65.80	74.76
		n.acc.	49.69	49.67	50.54	49.44	49.29
		dev.	$-0.3$	$-0.3$	$+0.5$	$-0.6$	$-0.7$
		gain	$-0.1$	$+0.6$	$-0.6$	$+16.4$	$+25.5$

Appendix

Table 5: Prior effects across five readouts on all eight evaluation subsets. The five direction-balanced subsets are used for the main comparisons, and the three prior-biased subsets are reported separately. Acc., n.acc., dev. and gain follow Tab. 1 . Random is accuracy from uniformly random predictions. Accuracies are in percent (%), and dev. and gain in percentage points.

Readout	VG-Rel L/R			COCO			WU-A			WU-B			VSR
Readout	acc.	n.acc.	gain	acc.	n.acc.	gain	acc.	n.acc.	gain	acc.	n.acc.	gain	acc.	n.acc.	gain
	$n{=}3918$			$n{=}404$			$n{=}408$			$n{=}354$			$n{=}1201$
random	50.01	50.01	0.0	49.99	49.99	0.0	25.02	25.02	0.0	50.01	50.01	0.0	50.01	50.01	0.0
frozen dual encoders, deployed score and ADR
CLIP ViT-B/16	49.64	49.69	$-0.1$	45.30	47.28	$-2.0$	26.96	25.98	$+1.0$	53.67	52.83	$+0.8$	53.87	53.21	$+0.7$
ADR- $\tilde{\pi}$	90.45	49.03	$+41.4$	92.33	51.73	$+40.6$	31.62	25.74	$+5.9$	93.22	52.26	$+41.0$	73.86	47.79	$+26.1$

Appendix

Table 6: Readouts on the five direction-balanced subsets. Each subset reports acc., n.acc., and gain as defined in Tab. 2 . Accuracies are in percent and gains in percentage points. Grey rows show deployed scores, followed by ADR with four choices of alignment weights (App. D.2 ). VSR exceptions are marked and explained below the table. The random-guess row averages

10{,}000

uniform draws.

(a) AUROC (derangement null)
readout	VG-Rel L/R	COCO	WU-B	VSR	pooled
$n$	$3918$	$404$	$354$	$1201$	$5877$
ADR- $\tilde{\pi}$	0.891 (0.510)	0.904 (0.516)	0.888 (0.449)	0.713 (0.484)	0.863 (0.504)
ADR- $\pi_{d}$	0.880 (0.509)	0.899 (0.483)	0.970 (0.506)	0.694 (0.478)	0.844 (0.504)
deployed cosine	0.509 (0.505)	0.461 (0.461)	0.512 (0.481)	0.504 (0.487)	0.514 (0.504)
MLLM likelihood, Qwen3.6-27B	0.717 (0.496)	0.726 (0.555)	0.829 (0.508)	0.568 (0.461)	0.661 (0.498)

Appendix

Table 7: Risk and coverage on the four binary direction-balanced sets. The pooled results use only the four direction-balanced sets (

5877

items). The dual-encoder readouts use CLIP ViT-B/16, and the likelihood readout uses Qwen3.6-27B. (a) AUROC of the absolute margin as a ranking of correct above wrong items, with the same statistic under the derangement null in parentheses. (b) Accuracy of the most confident

30\%

of the items. Pooling ranks the items of the four direction-balanced sets by one margin, so it is the accuracy a single threshold would obtain. The four-way What’sUp-A is excluded from this binary-set analysis.

subject noun	items	ViT-B/16	SigLIP2
bottle	28	8	2
bowl	12	6	3
cup	12	6	0
orange	12	5	2
pillow	12	5	2
plate	12	5	0

Appendix

Table 8: Relative-position failures grouped by subject noun on What’sUp-A, with both encoders evaluated on the same items. Rows show the thirteen nouns with at least twelve items, ordered by the ViT-B/16 failure count; the remaining forty-two are pooled. Counts indicate failures of the estimated relative positions, without identifying which object is mislocalized. SigLIP2 has no such failures for 29 of the fifty-five subject-noun groups.

	CLIP B/16	null	NegCLIP
ADR- $\tilde{\pi}$ , accuracy	0.7386 [0.714, 0.763]	0.4779	0.7394
global, oracle threshold	0.5387	0.5321	0.5287
viewer-frame consistency ( $n{=}507$ )	0.9290 [0.905, 0.951]	0.5069	0.9172
object-frame consistency ( $n{=}67$ )	0.2239 [0.134, 0.328]	0.4776	0.2239
both frames ( $n{=}30$ )	0.8667	0.3333	0.9667
specificity on false statements ( $n{=}597$ )	0.6281	0.4606	0.6348

Appendix

Table 9: VSR covered horizontal subset (

n{=}1201

, whole images, third-party labels) and its decomposition by the benchmark’s reference-frame annotation. Frame rows are true-item consistency rates, the false-statement row is the shared specificity, and balanced accuracy pairs each frame’s consistency with that specificity.

$\varepsilon$	ADR- $\tilde{\pi}$			ADR- $\pi_{d}$			ADR- $\tilde{\pi}$			ADR- $\pi_{d}$
	VG-Rel L/R, $n{=}3918$						What’sUp-A, $n{=}408$
$\varepsilon$	acc.	n.acc.	gain	acc.	n.acc.	gain	acc.	n.acc.	gain	acc.	n.acc.	gain
$0.02$	90.35	49.26	$+41.1$	88.72	50.66	$+38.1$	29.41	25.25	$+4.2$	60.78	25.00	$+35.8$
$0.05$	90.45	49.03	$+41.4$	90.22	49.44	$+40.8$	31.62	25.74	$+5.9$	41.18	25.25	$+15.9$
$0.1$	90.40	48.90	$+41.5$	90.25	48.80	$+41.5$	31.86	24.75	$+7.1$	33.82	25.00	$+8.8$
$0.2$	90.40	48.88	$+41.5$	90.30	48.26	$+42.0$	32.35	24.75	$+7.6$	32.11	25.00	$+7.1$

Appendix

Table 10: Temperature sensitivity on frozen CLIP ViT-B/16 features. Each variant reports acc., n.acc., and gain as defined in Tab. 2 . Accuracies are in percent and gains in percentage points. The default temperature is

\varepsilon=0.05

Readout	param.	param. vs.	FLOPs	FLOPs vs.	mem. (GiB)	mem. vs.	time (ms)	time vs.
Readout	param.	CLIP	FLOPs	CLIP	mem. (GiB)	CLIP	time (ms)	CLIP
deployed / CLIP	0.15 B	1.0 $\times$	$3.45{\times}10^{10}$	1.0 $\times$	0.60	1.0 $\times$	9.28	1.0 $\times$
ADR / CLIP	0.15 B	1.0 $\times$	$1.15{\times}10^{11}$	3.3 $\times$	0.66	1.1 $\times$	20.78	2.2 $\times$
half-crop / CLIP	0.15 B	1.0 $\times$	$1.38{\times}10^{11}$	4.0 $\times$	0.60	1.0 $\times$	43.83	4.7 $\times$
deployed / SigLIP2	1.14 B	7.6 $\times$	$7.39{\times}10^{11}$	21.4 $\times$	4.35	7.3 $\times$	35.97	3.9 $\times$
ADR / SigLIP2	1.14 B	7.6 $\times$	$1.05{\times}10^{12}$	30.4 $\times$	4.40	7.3 $\times$	52.98	5.7 $\times$

Appendix

Table 11: Computational cost per item, averaged over the same 50 items on one NVIDIA H200 after one warm-up call. All ratios are relative to the deployed CLIP.

ADR- $\tilde{\pi}$ , the default
definition	the excess $\tilde{\pi}=[\pi_{0}-\frac{1}{Nm}]_{+}$ of the Sinkhorn coupling
what it buys	The underlying coupling $\pi_{0}$ constrains both marginals, so words share a patch budget. Subtracting the uniform baseline removes its contribution to the centroid. The retained weights $\tilde{\pi}$ no longer preserve either marginal. Their column masses can be inspected alongside the localization maps, but do not by themselves establish localization quality.
what it costs	Sinkhorn scaling requires iteration. The shared patch budget in $\pi_{0}$ couples word localizations and can limit how well objects of different sizes are represented. A noun can lose all its excess mass and then has no centroid (App. D.3 ).
ADR- $\pi_{d}$
definition	the softmax of $\mathbf{S}/\varepsilon$ over patches, computed independently for each word
what it buys	Closed form with no iteration. Each word is localized on its own, so a large object cannot drain mass from a small one. The column mass is fixed, so every centroid is defined.

Appendix

Table 12: The column weights of ADR. Every weight feeds the same relation axis, sign rule and zero threshold and differs only in how patch mass is assigned to words. Their accuracies are reported in Tab. 6 .

Subset	Dataset	Relations	Classes	Question type	Items
(a) Direction-balanced subsets (Tabs. 2 and 3 )
VG-Relation left/right	ARO	Horizontal	2	Multiple choice	3918
COCO-spatial	What’sUp	Horizontal, vertical	2	Multiple choice	404
What’sUp-A	What’sUp	Horizontal, vertical	4	Multiple choice	408
What’sUp-B	What’sUp	Horizontal, depth	2	Multiple choice	354
VSR horizontal	VSR	Horizontal	2	True/false	1201

Appendix

Table 13: Evaluation subsets from ARO ( Yuksekgonul et al., 2023 ) , What’sUp ( Kamath et al., 2023 ) , VSR ( Liu et al., 2023 ) and VALSE ( Parcalabescu et al., 2022 ) . Classes gives the number of possible answers, and Items counts evaluated examples. Data processing details are given in App. E.1 .

evaluation set	source	selection	items
VG-Relation left/right, 6000-item cell	ARO	relation field, 6000-item pick	3918
VG-Relation vertical, 6000-item cell	ARO	relation field, 6000-item pick	800
COCO-spatial two-object	What’sUp	all two-object pairs	440 (404 covered)
What’sUp-A	What’sUp	full set, 4-way	412 (408 covered)
What’sUp-B	What’sUp	front/behind and left/right pairs	354
VSR horizontal	VSR	horizontal subset	1224 (1201 covered)

Appendix

Table 14: Evaluation sets. Each set is a deterministic selection from a public benchmark. The VG-Relation results use the 6000-item sample. Sources are ARO VG-Relation ( Yuksekgonul et al., 2023 ) , the What’sUp release ( Kamath et al., 2023 ) , which also supplies COCO-spatial, and VSR ( Liu et al., 2023 ) . Covered counts the items whose caption the fixed parsing rule covers, and VSR carries the benchmark’s own reference-frame labels. COCO-spatial’s

2247

single-object captions lie outside the two-argument readout.

(a) frozen dual encoders, deployed pooled score and ADR (Tabs. 2 and 6 )
model	note
CLIP ViT-B/32
CLIP ViT-B/16 ⋆
CLIP ViT-L/14
LAION-2B ViT-B/16 ⋆	App. A.3
DFN5B ViT-H/14 ⋆

Appendix

Table 15: Every model evaluated in this paper.

\star

marks the nine score families of Table 2 , where BLIP enters through the ITC branch of its base checkpoint. Loader settings are in the released code.

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