aiqbench analysis
This paper is squarely about AI: it introduces DRBench, a 14,573-question benchmark for dense-scene reasoning in vision-language models, and DRScaffold, a supervised fine-tuning method that splits training targets into four ordered stages to tie reasoning steps to visual entities. The headline result is that a 3B-parameter model fine-tuned this way beats a frozen 32B model on DRBench under zero-shot direct-answer prompting, though that comparison is limited to one benchmark and the gains rely on the authors' own task design. Readers should watch whether the approach generalises beyond their benchmark and whether the released code and data allow independent replication.
DRScaffold: Boosting Dense-Scene Reasoning in Lightweight Vision Language Models
arXiv:2605.26038v2 Announce Type: replace-cross Abstract: Lightweight vision-language models perform competitively on standard benchmarks yet fail systematically in dense-scene reasoning, where multiple objects, attributes, and relations must be jointly grounded and resolved through multi-step inference. Such capability is critical for real-world applications where models must reliably interpret cluttered environments. Yet existing training signals provide no explicit grounding between reasoning steps and the underlying visual entities and relations, leaving lightweight models free to generate fluent but visually unanchored reasoning chains. To address this gap, we first introduce DRBench, a benchmark of 14,573 questions across 2,943 images, organized into five task categories spanning three progressive reasoning layers. Building on DRBench, we propose DRScaffold, a supervised fine-tuning framework that decomposes the supervision target into four causally ordered stages, promoting grounded reasoning without architectural modification. Experiments on three lightweight VLMs demonstrate substantial gains on DRBench while preserving or improving performance on general-purpose benchmarks. Notably, Qwen2.5-VL-3B trained with DRScaffold surpasses the frozen Qwen2.5-VL-32B under zero-shot direct-answer prompting on DRBench, demonstrating that structured supervision can substitute for a significant portion of model scale in dense-scene reasoning. Our code is available at https://github.com/irene-shi/DRScaffold.
Abstract:Lightweight vision-language models perform competitively on standard benchmarks yet fail systematically in dense-scene reasoning, where multiple objects, attributes, and relations must be jointly grounded and resolved through multi-step inference. Such capability is critical for real-world applications where models must reliably interpret cluttered environments. Yet existing training signals provide no explicit grounding between reasoning steps and the underlying visual entities and relations, leaving lightweight models free to generate fluent but visually unanchored reasoning chains. To address this gap, we first introduce DRBench, a benchmark of 14,573 questions across 2,943 images, organized into five task categories spanning three progressive reasoning layers. Building on DRBench, we propose DRScaffold, a supervised fine-tuning framework that decomposes the supervision target into four causally ordered stages, promoting grounded reasoning without architectural modification. Experiments on three lightweight VLMs demonstrate substantial gains on DRBench while preserving or improving performance on general-purpose benchmarks. Notably, Qwen2.5-VL-3B trained with DRScaffold surpasses the frozen Qwen2.5-VL-32B under zero-shot direct-answer prompting on DRBench, demonstrating that structured supervision can substitute for a significant portion of model scale in dense-scene reasoning. Our code is available at this https URL.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2605.26038 [cs.CV]
(or arXiv:2605.26038v2 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.26038 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 280 words