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.

Incorporating user preferences into large language models (LLMs) can enhance the personalization and reliability of model outputs and facilitate the application of LLMs to real-world scenarios. However, leveraging user preferences can be a double-edged sword. Recent studies have found that improper utilization can incur sycophancy, where LLMs prioritize alignment with user preferences over the correctness of their outputs. To address sycophancy in LLMs, we analyze and model the problem through the lens of structured causal models (SCMs). We attribute sycophancy to LLMs' reliance on spurious correlations between user preferences and model outputs in this paper. Based on the proposed SCMs, we develop a novel framework, termed **CAUSM**, to mitigate sycophancy in LLMs by exploiting a significant causal signature. Specifically, we eliminate the spurious correlations embedded in the intermediate layers of LLMs through causally motivated head reweighting, and then calibrate the intra-head knowledge along the causal representation direction. Extensive experiments are conducted across diverse language tasks to demonstrate the superiority of our method over state-of-the-art competitors in mitigating sycophancy in LLMs.

Despite the significant breakthrough of Mixture-of-Experts (MoE), the increasing scale of these MoE models presents huge memory and storage challenges. Existing MoE pruning methods, which involve reducing parameter size with a uniform sparsity across all layers, often lead to suboptimal outcomes and performance degradation due to varying expert redundancy in different MoE layers. To address this, we propose a non-uniform pruning strategy, dubbed Differentiable Expert Pruning (DiEP), which adaptively adjusts pruning rates at the layer level while jointly learning inter-layer importance, effectively capturing the varying redundancy across different MoE layers. By transforming the global discrete search space into a continuous one, our method handles exponentially growing non-uniform expert combinations, enabling adaptive gradient-based pruning. Extensive experiments on five advanced MoE models demonstrate the efficacy of our method across various NLP tasks. Notably, \textbf{DiEP} retains around 92\% of original performance on Mixtral 8$\times$7B with only half the experts, outperforming other pruning methods by up to 7.1% on the challenging MMLU dataset.

Cross-age face recognition has remained a popular research topic as most regular facial recognition systems have failed in dealing with facial changes through age. In order to enhance the system's capability of discriminating facial identity features in spite of age changes, this paper proposes a novel deep convolutional network method for cross-age face recognition called age-related factor guided joint task modeling convolutional neural networks, which combines an identity discrimination network with an age discrimination network that shares the same feature layers. By alternatively training the fusion networks and the combined factor model, the cross-age identity features and cross-identity age features can be effectively separated with high inter-class distension and intra-class compactness. Extensive experiments have been performed on the benchmark aging data sets, including MORPH, CACD-VS, and Cross Age LFW. The results have demonstrated the superiority and effectiveness of our model.

Cross-age face recognition has remained a popular research topic because the sophisticated facial change across age disables regular face recognition systems. Widely applied in age-related tasks, the hidden factor analysis (HFA) model decomposes face feature into independent age and identity factors. However, the hypothesis that the identity and age factors are independent is not in accordance with the fact that aging has different appearance changes on different people's faces. To address this problem, this letter presents a novel method for cross-age face recognition, called age-identity modified HFA, which exploits a new latent factor modeled as a linear combination with the age factor and the identity factor. Hence, the cross-age identity information can be extracted and separated preferably. A maximum likelihood strategy is proposed to judge which gallery face has the same identity with the probe image, while we do not need to know what the probe identity is. Extensive experiments are performed on the benchmark aging datasets MORPH and FG-Net, and the recognition rate of our method outperforms HFA by 10.4% and 1.15%, respectively.

In the above paper [1] the authors designed an IRGSC architecture for face recognition with pixel corruption and occlusion. We have discovered that the calculation of the weights vector s in the original paper is flawed. In this comment we demonstrate the correct calculations, and further analysed some of the results in the paper.

Sparse representation based classification method has been successfully applied to face recognition in recent years. However, it is still a problem in the scenario of pose variation in face recognition with single sample per person. In this paper, we propose a novel alignment-free model, called Gabor-based Partial Face Sparse Representation (GPFSR), to solve the problem of pose variation in face recognition with single sample per person by using partial face. In our method, we firstly locate five facial landmarks in different images. Then partial face is obtained, which is used to construct Gabor-based local dictionary and compute the weights of each patch. Our classification principle is based on sparse representation. The experimental results on the Multi-PIE and FERET show that GPFSR is robust to pose variation in FR with single sample per person.