ReGround: Restoring Visual Grounding in Multi-Step Reasoning through Self-Diagnosis and Visual Re-Examination
Quick Answer
ReGround introduces a two-stage framework for Vision-Language Models (VLMs) to self-diagnose grounding failures and re-examine visual evidence, enhancing performance on multi-step reasoning tasks.
Quick Take
Experiments show significant gains across eight benchmarks, particularly in visually intensive scenarios, without major inference overhead.
Key Points
- Attention entropy decreases during initial reasoning but increases after image re-injection.
- Targeted self-diagnosis is crucial for effective visual re-examination.
- ReGround achieves substantial performance gains on key benchmarks.
- The framework incurs only modest inference overhead compared to tool-augmented methods.
- Accepted for presentation at ACM Multimedia 2026.
DeepSignal Analysis
What happened
ReGround presents a two-stage framework aimed at improving Vision-Language Models (VLMs) by addressing visual grounding failures during multi-step reasoning. The framework incorporates self-diagnosis and visual re-examination, leading to notable performance improvements across multiple benchmarks.
Key evidence
- The study analyzed 2,510 samples from four benchmarks, revealing that attention entropy over image tokens decreases during initial reasoning and increases after visual re-injection.
- The framework requires both image re-injection and targeted self-diagnosis, as ineffective diagnosis can degrade performance, while accurate self-diagnosis can enhance it significantly.
- Experiments demonstrated consistent performance gains across eight benchmarks, particularly in visually intensive tasks, with only modest increases in inference overhead compared to tool-augmented baselines.
Why it matters
This research highlights the challenges VLMs face in maintaining visual grounding during complex reasoning tasks. By introducing a systematic approach to self-diagnosis and re-examination, ReGround could enhance the reliability and effectiveness of VLMs in practical applications, such as image captioning and visual question answering.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Vision-Language Models (VLMs) often lose visual grounding during multi-step reasoning: as reasoning chains grow longer, later inference steps rely increasingly on language priors rather than image evidence. We identify a consistent benchmark-level signature associated with this degradation: across 2,510 re-examined samples from four benchmarks, attention entropy over image tokens typically decreases during Round 1 and rises again after image re-injection.
However, we find that effective visual re-examination requires two complementary ingredients: image re-injection and targeted self-diagnosis. Without targeted diagnosis, re-examination can even hurt performance, whereas accurate self-diagnosis yields substantial gains -- a swing of several points on key benchmarks, indicating that diagnostic quality is a key factor in whether re-examination helps or hurts in our setting. We present ReGround, a two-stage framework that teaches VLMs to self-diagnose grounding failures and selectively re-examine visual evidence, without architectural modifications or external tools. Through capability bootstrapping, a stronger variant from the same model family provides diagnostic scaffolding only during data construction, while the policy model learns to diagnose autonomously at inference time and retains most of the assisted gains.
Experiments on eight benchmarks across two VLM backbones demonstrate consistent gains, especially on visually intensive multi-step reasoning tasks, while incurring only modest inference overhead relative to tool-augmented baselines. Project page: this https URL . Code: this https URL .
| Comments: | Accepted to ACM Multimedia 2026 (MM '26). 8 pages main text, 4 figures, plus appendix |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| ACM classes: | I.2.10; I.2.7; I.4.8 |
| Cite as: | arXiv:2608.04385 [cs.CV] |
| (or arXiv:2608.04385v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2608.04385 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Lei Peng [view email]
[v1]
Wed, 5 Aug 2026 02:41:53 UTC (3,681 KB)
— Originally published at arxiv.org
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