Liu, Zujing, Pan, Junwen, She, Qi, Gao, Yuan, Xia, Guisong · Oct 27, 2025
On the Faithfulness of Visual Thinking: Measurement and Enhancement
Markdown · JSON · Original source
Authors Zujing Liu, Junwen Pan, Qi She, Yuan Gao and Guisong Xia argue that large vision-language models can produce vision-text (MCoT) traces whose visual details do not meaningfully guide their answers. In their tests, predictions changed little when visual thoughts were intervened on, but changed significantly when textual thoughts were intervened on. The authors attribute this gap to reinforcement (RFT) rewards that encourage interleaved vision-text cues without checking whether the visual information is correct. The paper introduces an automated LVLM-based metric to assess relevance and sufficiency, then proposes Sufficient-Component Cause Model (SCCM) learning. SCCM aims to produce sufficient yet minimal visual components that can independently lead to the correct answer. The authors report that their annotation-free, plug-and-play approach improves visual faithfulness across fine-grained perception and reasoning . The central concern is that a correct answer may conceal visual reasoning that is irrelevant or insufficient. The abstract reports benchmark results, but does not specify their size or whether improved visual faithfulness leads to more reliable performance outside those evaluations. What results, if any, show that SCCM's gains carry over to real-world visual tasks?
Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.
Original source
cs.AI · Article
Source details
Original source date: Oct 27, 2025 (2025-10-27).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-07T07:47:10.542Z. First observed: 2026-11-02T05:03:58.702Z. Retrieved: 2026-11-03T06:31:19.987Z.
Referenced by
Recorded citations and public file attachments. A reference alone does not establish agreement or influence.
No public references recorded here yet.
Limits
The evidence is the paper's abstract; it does not provide names, sizes, numerical results, or evaluation procedures. The abstract reports benchmark improvements but does not establish whether they translate into more reliable performance on real-world visual tasks.
Open question
What evidence, beyond results, shows whether SCCM's gains in visual faithfulness carry over to real-world visual tasks?
Source-stated
The authors report that visual information in MCoT can be inaccurate even when answers are correct, and that task accuracy remains nearly unchanged when visual information is corrupted.
the visual information in MCoT is often inaccurate even when answers are correct, and task accuracy remains nearly unchanged despite the visual information being corrupted
Source-stated
The authors report that predictions changed little under visual intervention but changed significantly under textual intervention, suggesting the model largely ignored visual evidence in their tests.
predictions remain nearly unchanged under visual intervention but change significantly under textual intervention, indicating that the visual evidence is largely ignored
Source-stated
The authors attribute the problem to RFT reward design that incentivizes interleaved vision-text cues without considering whether the visual information is correct.
solely incentivizes the format of interleaved vision-text cues, encouraging the model to incorporate visual information into its text reasoning steps without considering its correctness
Source-stated
The authors introduce an automated LVLM-based metric that evaluates visual cues for relevance and sufficiency.
an automated LVLM-based evaluation metric that quantifies the faithfulness of visual cues from two perspectives: relevance and sufficiency
Source-stated
The authors propose SCCM learning, which encourages MCoT to generate minimal visual components sufficient to lead independently to the correct answer.
generate sufficient yet minimal visual components that are independently capable of leading to the correct answer
Source-stated
The authors report that SCCM improves visual faithfulness across fine-grained perception and reasoning benchmarks.
Empirical results demonstrate that SCCM substantially improves the visual faithfulness across a suite of fine-grained perception and reasoning benchmarks.
Source and contributor context
Liu, Zujing, Pan, Junwen, She, Qi, Gao, Yuan, Xia, Guisong · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Multimodal model
A model that works with more than one kind of input or output, such as text, images and audio.
Chain of thought
The step-by-step working a model writes out before giving its final answer.
Fine-tuning
Further training of an existing model to adapt it to a particular task or use.
Benchmark
A defined set of tests used to compare systems. Results depend on the tasks and measures selected.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: 87b5f108-49fd-4963-8ff0-854bc4fe82e2.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-07T07:47:10.542Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 99dc5ec190b6c3d26c3642347d94ec2af7fef9c26864199c83593ac683bafe77. Source text hash: 4c22004795c21332c548a0bd729fc71df41ccf53998601d6f34277041f0dfeb4.
Revision and source-check history
Version 1 · 2026-11-07T07:47:10.542Z
On the Faithfulness of Visual Thinking: Measurement and Enhancement
Authors Zujing Liu, Junwen Pan, Qi She, Yuan Gao and Guisong Xia argue that large vision-language models can produce vision-text (MCoT) traces whose visual details do not meaningfully guide their answers. In their tests, predictions changed little when visual thoughts were intervened on, but changed significantly when textual thoughts were intervened on. The authors attribute this gap to reinforcement (RFT) rewards that encourage interleaved vision-text cues without checking whether the visual information is correct. The paper introduces an automated LVLM-based metric to assess relevance and sufficiency, then proposes Sufficient-Component Cause Model (SCCM) learning. SCCM aims to produce sufficient yet minimal visual components that can independently lead to the correct answer. The authors report that their annotation-free, plug-and-play approach improves visual faithfulness across fine-grained perception and reasoning . The central concern is that a correct answer may conceal visual reasoning that is irrelevant or insufficient. The abstract reports benchmark results, but does not specify their size or whether improved visual faithfulness leads to more reliable performance outside those evaluations. What results, if any, show that SCCM's gains carry over to real-world visual tasks?
Original source
cs.AI · Article
Source details
Original source date: Oct 27, 2025 (2025-10-27).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-07T07:47:10.542Z. First observed: 2026-11-02T05:03:58.702Z. Retrieved: 2026-11-03T06:31:19.987Z.
Limits
The evidence is the paper's abstract; it does not provide names, sizes, numerical results, or evaluation procedures. The abstract reports benchmark improvements but does not establish whether they translate into more reliable performance on real-world visual tasks.
Open question
What evidence, beyond results, shows whether SCCM's gains in visual faithfulness carry over to real-world visual tasks?
Source-stated
The authors report that visual information in MCoT can be inaccurate even when answers are correct, and that task accuracy remains nearly unchanged when visual information is corrupted.
the visual information in MCoT is often inaccurate even when answers are correct, and task accuracy remains nearly unchanged despite the visual information being corrupted
Source-stated
The authors report that predictions changed little under visual intervention but changed significantly under textual intervention, suggesting the model largely ignored visual evidence in their tests.
predictions remain nearly unchanged under visual intervention but change significantly under textual intervention, indicating that the visual evidence is largely ignored
Source-stated
The authors attribute the problem to RFT reward design that incentivizes interleaved vision-text cues without considering whether the visual information is correct.
solely incentivizes the format of interleaved vision-text cues, encouraging the model to incorporate visual information into its text reasoning steps without considering its correctness
Source-stated
The authors introduce an automated LVLM-based metric that evaluates visual cues for relevance and sufficiency.
an automated LVLM-based evaluation metric that quantifies the faithfulness of visual cues from two perspectives: relevance and sufficiency
Source-stated
The authors propose SCCM learning, which encourages MCoT to generate minimal visual components sufficient to lead independently to the correct answer.
generate sufficient yet minimal visual components that are independently capable of leading to the correct answer
Source-stated
The authors report that SCCM improves visual faithfulness across fine-grained perception and reasoning benchmarks.
Empirical results demonstrate that SCCM substantially improves the visual faithfulness across a suite of fine-grained perception and reasoning benchmarks.
Source and contributor context
Liu, Zujing, Pan, Junwen, She, Qi, Gao, Yuan, Xia, Guisong · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Multimodal model
A model that works with more than one kind of input or output, such as text, images and audio.
Chain of thought
The step-by-step working a model writes out before giving its final answer.
Fine-tuning
Further training of an existing model to adapt it to a particular task or use.
Benchmark
A defined set of tests used to compare systems. Results depend on the tasks and measures selected.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: 87b5f108-49fd-4963-8ff0-854bc4fe82e2.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-07T07:47:10.542Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 99dc5ec190b6c3d26c3642347d94ec2af7fef9c26864199c83593ac683bafe77. Source text hash: 4c22004795c21332c548a0bd729fc71df41ccf53998601d6f34277041f0dfeb4.
uncertain · 2026-11-07T07:47:10.542Z · anthropic/claude-opus-5.5
Publication: 87b5f108-49fd-4963-8ff0-854bc4fe82e2
attribution — pass: The reading names the five listed authors and consistently frames the findings, causal explanation, metric and SCCM results as the authors' claims rather than established facts or EM opinion.
Authors:Zujing Liu, Junwen Pan, Qi She, Yuan Gao, Guisong Xia
summary — uncertain: The structure is faithful, but three repairs would improve fidelity and accessibility. 1) The source says SCCM 'substantially improves' faithfulness, but the reading says only 'improves'; restore 'substantially' as the authors' reported claim. 2) Claim 1 says visual information 'can be' inaccurate; the source says 'often'. 3) The acronym LVLM is used without being tied to 'large vision-language models', and 'intervened on' and 'faithfulness' are unexplained for general readers. Keep the terms, define LVLM inline, and add tooltips for chain-of-thought, RFT, intervention and faithfulness.
SCCM substantially improves the visual faithfulness across a suite of fine-grained perception and reasoning benchmarks
date — pass: The displayed original date, 2025-10-27, matches the v1 submission date. It is not the later v2 revision date of 30 Oct 2026 or the feed date.
Submitted on 27 Oct 2025 (v1), last revised 30 Oct 2026
support — pass: Every claim matches an exact abstract excerpt. The limitations correctly note that the abstract gives no benchmark names, sizes or numbers. The closing question about real-world transfer is framed as an open question, not as a claim. The summary's 'central concern' follows from the abstract's statement that the visual information is irrelevant and insufficient even when answers are correct.
the visual information in current MCoT traces is simultaneously irrelevant and insufficient
media — pass: The selected media is the verified arXiv abstract page for this paper. It is a direct original source and the most direct way to access the paper.
Challenge or correct this reading
Send a correction or an opposing source with this reading’s URL. Disagreement and uncertainty can remain unresolved.
A model that works with more than one kind of input or output, such as text, images and audio.
Definition referenceThe step-by-step working a model writes out before giving its final answer.
Definition referenceFurther training of an existing model to adapt it to a particular task or use.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivarXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceA model that works with more than one kind of input or output, such as text, images and audio.
Definition referenceThe step-by-step working a model writes out before giving its final answer.
Definition referenceFurther training of an existing model to adapt it to a particular task or use.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivarXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition reference