arXiv subjects

Jaesung Jun

Publications and source records attributed to Jaesung Jun.

2 recordsLinked to original sources

arXiv2026

Attention Maps Know When to Think: Training-Free Difficulty-Aware Chain-of-Thought for Reasoning Segmentation

Chain-of-Thought (CoT) prompting has become a standard reasoning strategy in Multi-modal Large Language Models (MLLMs), showing strong performance in complex vision-language tasks such as reasoning segmentation. However, our analysis reveals that indiscriminate CoT usage can lead to degraded performance by inducing unnecessary reasoning, a phenomenon we term overthinking. In reasoning segmentation, overthinking often leads to misalignment between the query's intent and visual grounding. To address this, we propose Difficulty-Aware Chain-of-Thought (DA-CoT), a training-free adaptive framework that selectively triggers CoT based on the input's intrinsic difficulty. We find that the entropy of the model's attention maps reliably reflects task difficulty: low entropy indicates confident, localized grounding, while high entropy suggests uncertainty and potential benefit from CoT. DA-CoT uses this entropy signal to determine whether to apply CoT prompting. Additionally, we introduce a Bidirectional Rethinking mechanism that enables the model to automatically flip its reasoning decision when the initial output fails, enhancing robustness by recovering from failures induced by overthinking and underthinking. DA-CoT demonstrates its effectiveness by achieving state-of-the-art performance on the Reasoning Segmentation benchmarks while significantly improving inference efficiency.

cs.CV↗

arXiv2026

StAR: Segment Anything Reasoner

As AI systems are being integrated more rapidly into diverse and complex real-world environments, the ability to perform holistic reasoning over an implicit query and an image to localize a target is becoming increasingly important. However, recent reasoning segmentation methods fail to sufficiently elicit the visual reasoning capabilities of the base mode. In this work, we present Segment Anything Reasoner (StAR), a comprehensive framework that refines the design space from multiple perspectives-including parameter-tuning scheme, reward functions, learning strategies and answer format-and achieves substantial improvements over recent baselines. In addition, for the first time, we successfully introduce parallel test-time scaling to the segmentation task, pushing the performance boundary even further. To extend the scope and depth of reasoning covered by existing benchmark, we also construct the ReasonSeg-X, which compactly defines reasoning types and includes samples that require deeper reasoning. Leveraging this dataset, we train StAR with a rollout-expanded selective-tuning approach to activate the base model's latent reasoning capabilities, and establish a rigorous benchmark for systematic, fine-grained evaluation of advanced methods. With only 5k training samples, StAR achieves significant gains over its base counterparts across extensive benchmarks, demonstrating that our method effectively brings dormant reasoning competence to the surface.

cs.CV↗