Visual Abstention in Unified Multimodal Models

Chufan Shi; Cheng Yang; Tiannuo Yang; Isadora White; Yiwei Chen; Taylor Berg-Kirkpatrick; Xuezhe Ma

cs.CLOct 6, 2026

Visual Abstention in Unified Multimodal Models

Authors: Chufan Shi, Cheng Yang, Tiannuo Yang, Isadora White, Yiwei Chen, Taylor Berg-Kirkpatrick, Xuezhe Ma

Organizations: University of Southern California · University of California San Diego

Abstract

Unified multimodal models (UMMs) integrate understanding and generation, yet their generative behavior is rarely governed by what they understand about the task. We formalize visual abstention: when a requested visual transformation is impossible under the task's rules, the model should recognize that no valid solution exists, state this, and decline to generate. We introduce Draw-or-Decline (DoD), a benchmark of 1,050 feasible-infeasible request pairs across 7 task categories that jointly measures editing success and the refusal of infeasible requests. Evaluating 8 UMMs, we find that editing ability and abstention are distinct capabilities: even the strongest editor, at 68.4% editing accuracy, refuses only 0.4% of infeasible requests under ordinary instructions. Their reasoning shows why: the models rarely notice the conflict, and instead plan the edit as if the request were possible, often describing objects that are not in the image, or quietly change the request into one they can complete. Explicitly prompting these UMMs to report infeasibility increases textual refusals but reduces editing accuracy. We propose VisTA (Visual Transformation and Abstention), a training method that pairs feasible and infeasible examples so that a model judges feasibility before deciding whether to generate. We train VisTA-BAGEL to perform feasible edits and decline infeasible requests. Without any reminder, it refuses 93.0% of infeasible requests, up from 0.4% for the strongest editor, while falsely refusing only 0.8% of feasible ones. Unlike a reminder, this does not cost editing accuracy: VisTA-BAGEL completes 74.3% of feasible edits, more than any of the 8 evaluated UMMs.

Figures & tables

Figure 1: Visual abstention in a maze task. (a) A valid path exists, so the model should draw it. (b) No path exists, yet the model still draws through the wall. This model notices the wall first, but most responses never acknowledge the conflict at all (Sec. 4 ). (c) With visual abstention, the model states that no solution exists and ends its response without generating an image.

Figure 2: Examples across the $7$ DoD task categories. Real-world pairs share an image, with colored text marking differences between feasible and infeasible instructions. References show valid edits for feasible requests. Maze pairs share an instruction; changing $1$ corridor cell blocks the path.

Model	Maze		Mat.		Med.		Mot.		Pose		Spat.		Temp.		Overall
Model	$E$	$D$	$E$	$D$	$E$	$D$	$E$	$D$	$E$	$D$	$E$	$D$	$E$	$D$	$E$	$D$
UniREdit-BAGEL	$51.3$	$0.0$	$75.3$	$0.7$	$64.7$	$0.0$	$64.0$	$0.7$	$75.3$	$0.0$	$69.3$	$1.3$	$78.7$	$0.0$	$68.4$	$0.4$
SenseNova-U1.0	$44.0$	$0.0$	$74.0$	$0.0$	$70.0$	$0.0$	$53.3$	$0.7$	$65.3$	$0.7$	$66.0$	$2.0$	$75.3$	$0.0$	$64.0$	$0.5$
UniReason	$45.3$	$0.0$	$60.0$	$0.0$	$44.0$	$0.0$	$50.0$	$0.0$	$66.7$	$0.0$	$40.7$	$0.7$	$62.7$	$0.0$	$52.8$	$0.1$
Uni-CoT	$10.0$	$0.0$	$68.0$	$0.0$	$37.3$	$0.0$	$35.3$	$0.0$	$72.7$	$0.0$	$20.7$	$0.0$	$50.7$	$0.0$	$42.1$	$0.0$
BAGEL	$6.0$	$0.0$	$63.3$	$0.0$	$33.3$	$0.0$	$33.3$	$0.0$	$60.0$	$0.0$	$16.7$	$0.0$	$44.7$	$0.0$	$36.8$	$0.0$

Table 1: UMM results on DoD without the reminder (%).

E

is editing success, and

D

is refusal success under the lenient rule of Sec. 4.1 . Mat., Med., Mot., Spat., and Temp. denote material modification, medium interaction, motion state change, spatial arrangement, and temporal evolution.

Figure 3: How the reasoning of

5

UMMs handles infeasible requests. Each pie shows the

1{,}050

responses of one UMM to infeasible requests. We assign every response to at most

1

of the

4

groups by reading its reasoning. Below each pie, we give the shares of the

2

smallest groups, which are too thin to label. One UniReason response (

0.1\%

) fits no group and is not shown.

Model	Maze		Mat.		Med.		Mot.		Pose		Spat.		Temp.		Overall
Model	$E$	$D$	$E$	$D$	$E$	$D$	$E$	$D$	$E$	$D$	$E$	$D$	$E$	$D$	$E$	$D$
UniREdit-BAGEL	$50.0$	$0.0$	$77.3$	$5.3$	$65.3$	$6.7$	$64.7$	$4.0$	$74.7$	$7.3$	$70.0$	$9.3$	$76.0$	$4.0$	$68.3$	$5.2$
SenseNova-U1.0	$40.0$	$12.7$	$68.7$	$38.0$	$70.0$	$6.7$	$55.3$	$6.7$	$63.3$	$16.7$	$68.7$	$25.3$	$81.3$	$12.0$	$63.9$	$16.9$
UniReason	$46.7$	$0.0$	$64.0$	$4.7$	$42.7$	$4.0$	$44.7$	$5.3$	$64.0$	$6.0$	$36.7$	$24.7$	$64.7$	$4.7$	$51.9$	$7.0$
Uni-CoT	$8.0$	$28.0$	$62.7$	$27.3$	$40.7$	$20.7$	$32.7$	$62.7$	$64.7$	$50.0$	$18.0$	$70.7$	$51.3$	$23.3$	$39.7$	$40.4$
BAGEL	$1.3$	$37.3$	$55.3$	$68.7$	$36.0$	$44.7$	$31.3$	$60.7$	$59.3$	$62.0$	$16.7$	$72.7$	$42.7$	$38.7$	$34.7$	$55.0$

Table 2: UMM results on DoD with the reminder (%).

E

is editing success on feasible requests, and

D

is refusal success on infeasible requests. Mat., Med., Mot., Spat., and Temp. denote material modification, medium interaction, motion state change, spatial arrangement, and temporal evolution.

Model	$D\uparrow$	$D_{\mathrm{f}}\downarrow$
UniREdit-BAGEL	$5.2$	$0.1$
SenseNova-U1.0	$16.9$	$5.2$
UniReason	$7.0$	$0.4$
Uni-CoT	$40.4$	$25.0$
BAGEL	$55.0$	$28.9$
InternVL-U	$28.1$	$17.6$

Table 3: Refusals with the reminder (%), averaged over the

7

categories. Appx. B.7 gives the per-category

D_{\mathrm{f}}

	Without Reminder				With Reminder
VLM	$E\uparrow$	$D\uparrow$	Avg $\uparrow$	$D_{\mathrm{f}}\downarrow$	$E\uparrow$	$D\uparrow$	Avg $\uparrow$	$D_{\mathrm{f}}\downarrow$
Qwen2.5-VL-7B	$56.7$	$4.0$	$30.3$	$0.0$	$7.2$	$88.2$	$47.7$	$53.0$
Qwen3-VL-8B	$61.0$	$53.9$	$57.4$	$1.0$	$20.7$	$84.0$	$52.3$	$57.8$
Qwen3.5-9B	$62.4$	$32.3$	$47.3$	$0.4$	$35.5$	$81.2$	$58.4$	$40.7$
Qwen3.5-122B-A10B	$67.1$	$38.6$	$52.9$	$0.1$	$48.9$	$83.6$	$66.2$	$27.8$
UniREdit-BAGEL	$68.4$	$0.4$	$34.4$	$0.0$	$68.3$	$5.2$	$36.8$	$0.1$

Table 4: Decoupled pipelines on DoD (%). Avg is the mean of

E

and

D

. UniREdit-BAGEL is a UMM shown for reference.

Figure 4: Editing success versus refusal success on DoD, (a) without and (b) with the reminder. Circles are existing UMMs, triangles are decoupled pipelines, and the star is VisTA-BAGEL.

	No Reminder		Reminder
Model	$E$	$D$	$E$	$D$
VisTA-BAGEL	$74.3$	$93.0$	$73.6$	$93.4$
Feasible-only	$72.0$	$0.3$	$70.7$	$8.4$

Table 5: The feasible-only control and VisTA-BAGEL on DoD (%).

Appendix figures & tables10 assets

Supplementary material from the paper’s appendix.

Appendix

Model	Interface	Resolution	Max Tokens	Sampling	Image Steps / CFG
UniREdit-BAGEL	BAGEL	$640$	$1000$	Greedy	$50$ / $4.0$ , $2.0$
SenseNova-U1.0	Native editing with thinking	$640$	$1024$	Greedy	$50$ / $4.0$ , $1.0$
UniReason	Native reasoning edit	$640$	$2048$	$T=0.3$	$50$ / $4.0$ , $2.0$
Uni-CoT	Native reasoning edit	$640$	$1000$	$T=0.3$	$50$ / $4.0$ , $2.0$
BAGEL	BAGEL	$640$	$1000$	Greedy	$50$ / $4.0$ , $2.0$
InternVL-U	Native text and image	$640$	$1000$	Greedy	$20$ / $4.5$ , $2.0$

Appendix

Table 6: Inference settings of the evaluated models. All models use reasoning and the same settings under both prompts. Resolution is the longest side of the input image. Max Tokens is the budget for the reasoning text. Image Steps and CFG give the number of denoising steps and the guidance scales, listed as text guidance and image guidance. InternVL-U uses its own

2

scales. ThinkMorph may write reasoning and images for up to

3

rounds, with

4096

tokens per round.

Model	Repository	Revision
BAGEL	ByteDance-Seed/BAGEL-7B-MoT	5019f57d168e
UniREdit-BAGEL	maplebb/UniREdit-Bagel-bf16	2131cfdab2da
SenseNova-U1.0	sensenova/SenseNova-U1-8B-MoT	bfa9b436503c
UniReason	Alex11556666/UniReason	aa8aa793737b
Uni-CoT	Fr0zencr4nE/UniCoT-7B-MoT	63d778696497
InternVL-U	InternVL-U/InternVL-U	f012d760e697

Appendix

Table 7: Checkpoints of evaluated models on Hugging Face. Revision is the first

12

characters of the commit hash. Uni-CoT comes from the repository head, unchanged since September

2025

Figure 5: Reasoning of $3$ UMMs on the same infeasible request (Pose Adjustment). The image contains $1$ superhero, so the second superhero from the left does not exist. From top to bottom, the responses belong to Acknowledge then Comply, Premise Rewriting, and Premise Acceptance. Highlights mark the evidence for each group, and $[\ldots]$ marks omitted sentences. The image at the right of each response is the output of that UMM.

Figure 6: Reasoning of $3$ UMMs on the same infeasible request (Medium Interaction). The image contains $1$ wine glass, so the second wine glass from the right does not exist. From top to bottom, the responses belong to Acknowledge then Comply, Premise Rewriting, and Premise Acceptance. Highlights mark the evidence for each group, and $[\ldots]$ marks omitted sentences. The image at the right of each response is the output of that UMM.

Model	Maze	Mat.	Med.	Mot.	Pose	Spat.	Temp.	Overall
UniREdit-BAGEL	$0.0$	$0.0$	$0.0$	$0.0$	$0.0$	$0.7$	$0.0$	$0.1$
SenseNova-U1.0	$20.7$	$11.3$	$0.0$	$1.3$	$2.0$	$1.3$	$0.0$	$5.2$
UniReason	$0.0$	$0.0$	$0.7$	$2.0$	$0.0$	$0.0$	$0.0$	$0.4$
Uni-CoT	$19.3$	$17.3$	$16.0$	$47.3$	$24.0$	$38.0$	$13.3$	$25.0$
BAGEL	$31.3$	$46.7$	$20.0$	$35.3$	$26.0$	$28.7$	$14.0$	$28.9$
InternVL-U	$8.0$	$5.3$	$34.0$	$29.3$	$12.0$	$13.3$	$21.3$	$17.6$

Appendix

Table 8: False refusals of

8

UMMs with the reminder. Each entry is

D_{\mathrm{f}}

(%) over the

150

feasible requests of a category; Overall uses

1{,}050

. Mat., Med., Mot., Spat., and Temp. denote material modification, medium interaction, motion state change, spatial arrangement, and temporal evolution.

Category	Pairs	Examples
Maze	$5{,}200$	$10{,}400$
Material modification	$5{,}958$	$11{,}916$
Medium interaction	$5{,}573$	$11{,}146$
Motion state change	$5{,}512$	$11{,}024$
Pose adjustment	$5{,}695$	$11{,}390$
Spatial arrangement	$5{,}837$	$11{,}674$

Appendix

Table 9: Number of training pairs in each category. Each pair has

1

feasible and

1

infeasible example, so the number of examples is twice the number of pairs.

Without Reminder
Model	Maze	Mat.	Med.	Mot.	Pose	Spat.	Temp.	Overall
VisTA-BAGEL	$88.0$	$72.0$	$63.3$	$62.0$	$78.7$	$74.7$	$81.3$	$74.3$
With Reminder
VisTA-BAGEL	$88.0$	$74.7$	$61.3$	$66.7$	$74.0$	$72.0$	$78.7$	$73.6$

Appendix

Table 10: Per-category editing success of VisTA-BAGEL on DoD. Each entry is the percentage of correct edits over the

150

feasible requests in a category; Overall uses all

1{,}050

. Categories are maze solving, material modification, medium interaction, motion state change, pose adjustment, spatial arrangement, and temporal evolution. Category results of the

8

UMMs are reported in Tabs. 1 and 2 .

Without Reminder
Model	Maze	Mat.	Med.	Mot.	Pose	Spat.	Temp.	Overall
VisTA-BAGEL	$96.0$	$95.3$	$96.7$	$91.3$	$88.0$	$94.7$	$88.7$	$93.0$
With Reminder
VisTA-BAGEL	$97.3$	$96.0$	$96.7$	$92.7$	$87.3$	$94.0$	$90.0$	$93.4$

Appendix

Table 11: Per-category refusal success of VisTA-BAGEL on DoD. Each entry is the percentage of successful refusals over the

150

infeasible requests in a category; Overall uses all

1{,}050

	Without Reminder		With Reminder
Model	$E\uparrow$	$D\uparrow$	$E\uparrow$	$D\uparrow$
VisTA-BAGEL	$74.3$	$93.0$	$73.6$	$93.4$
No-reasoning ablation	$66.3$	$80.1$	$64.2$	$77.6$

Appendix

Table 12: The no-reasoning ablation and VisTA-BAGEL on DoD. All values are percentages.

E

uses the

1{,}050

feasible requests and

D

uses the

1{,}050

infeasible requests.

			Without Reminder		With Reminder
Held Out	Model	Categories	$E\uparrow$	$D\uparrow$	$E\uparrow$	$D\uparrow$
Maze	VisTA-BAGEL	Maze	$88.0$	$96.0$	$88.0$	$97.3$
	VisTA-BAGEL	Other six	$72.0$	$92.4$	$71.2$	$92.8$
	Without maze	Maze	$48.0$	$94.0$	$49.3$	$96.7$
	Without maze	Other six	$73.2$	$92.8$	$72.9$	$93.9$
Motion	VisTA-BAGEL	Motion	$62.0$	$91.3$	$66.7$	$92.7$

Appendix

Table 13: VisTA-BAGEL and

2

models that each leave

1

category out of training, evaluated on the held-out category and on the other six categories. All values are percentages.

E

uses the feasible requests and

D

the infeasible requests:

150

of each kind for the held-out category and

900

of each kind for the other six. UniREdit-BAGEL, the starting point of all models, scores

51.3

(

E

) and

0.0

(

D

) on maze and

64.0

and

0.7

on motion state change without a reminder.

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Knowing When Not to Answer: Evaluating Abstention in Multimodal Reasoning Systems

Nishanth Madhusudhan, Vikas Yadav, Alexandre Lacoste

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Multimodal Reasoning Abstention Answer Incomplete Evidence

Open paper AskPDF arXiv

Effective abstention (EA), recognizing evidence insufficiency and refraining from answering, is critical for reliable multimodal systems. Yet existing evaluation paradigms for vision-language models (VLMs) and multi-agent systems (MAS) assume answerability, pushing models to always respond. Abstention has been studied in text-only settings but remains underexplored multimodally; current benchmarks either ignore unanswerability or rely on coarse methods that miss realistic failure modes. We introduce MM-AQA, a benchmark that constructs unanswerable instances from answerable ones via transformations along two axes: visual modality dependency and evidence sufficiency. Evaluating three frontier VLMs spanning closed and open-source models and two MAS architectures across 2079 samples, we find: (1) under standard prompting, VLMs rarely abstain; even simple confidence baselines outperform this setup, (2) MAS improves abstention but introduces an accuracy-abstention trade-off, (3) sequential designs match or exceed iterative variants, suggesting the bottleneck is miscalibration rather than reasoning depth, and (4) models abstain when image or text evidence is absent, but attempt reconciliation with degraded or contradictory evidence. Effective multimodal abstention requires abstention-aware training rather than better prompting or more agents.

Abstract

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Knowing When Not to Answer: Evaluating Abstention in Multimodal Reasoning Systems

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