Recent Large Reasoning Models (LRMs) excel at complex reasoning tasks but often suffer from overthinking, generating overly long and redundant reasoning trajectories.To explore its essence, our empirical analysis reveals that LRMs are primarily limited to recognizing task properties (i.e., difficulty levels) like humans before solving the problem, leading to a one-size-fits-all reasoning strategy.This observation motivates a fundamental question: Can we explicitly bootstrap such ability to alleviate overthinking in LRMs?To this end, we propose Think-How-to-Think (TH2T), a novel two-stage fine-tuning strategy that progressively inspires LRMs' difficulty cognition and redundancy cognition of LRMs.Specifically, we first inject Difficulty Dypnosis into output prefixes as cues for global, prospective reasoning strategy selection, stimulating the model's sharper sensitivity to task complexity and adaptive control of reasoning depth.Then, we incorporate Redundancy Hypnosis into inprogress reasoning steps, which serve as local, retrospective signals for behavior correction by identifying and eliminating superfluous reasoning detours.Experiments across 7B/14B/32B models demonstrate that TH2T significantly reduces inference costs by over 70% on easy tasks and 40% on complex ones without compromising performance.The resultant models exhibit a nascent ability for difficulty-aware reasoning, effectively mitigating behaviors like excessive reflection and looping, thereby paving the way for more cognitively efficient LRMs.

Co-salient object detection aims to detect co-salient objects in a group of images, combining collaborative segmentation and saliency detection, which is more challenging. Recent deep learning approaches identify co-salient objects by capturing the attention of consistent patterns within a group of images. However, due to limited semantic discriminability, these approaches often generate redundant attention unrelated to the co-salient object, resulting in inaccuracies. To address this, we refer to prototypical contrastive learning, and propose a prototype generation module to create discriminative prototypes representing both intra-group consistency and inter-group variance. These prototypes guide our proposed collaborative attention generation module, effectively enhancing co-salient object detection by highlighting relevant deep features. To ensure prototype’s discriminability, we add contrastive supervision for multi-task learning. Additionally, we design a position-independent contrast loss function to enhance intra-group consistency representation. Experiments demonstrate the superiority of our approach over existing state-of-the-art approaches on three challenging benchmarks, i.e., CoCA,CoSOD3k,and CoSal2015.