国家预印本平台

第三方干预是维护公平规范的重要机制，但个体在执行规范时并非一视同仁，而是表现出显著的群体差异。本研究通过两个实验，探讨错误信念心理理论 (False Belief Theory of Mind, FB ToM) 如何影响4～6岁儿童 (n = 212, Mage = 5.57 ± 0.54岁, 107名男孩) 在分配双方来自同一群体 (分配者和接受者均为内群体成员，以及分配者和接受者均为外群体成员)，及分配双方来自不同群体情境下的第三方干预行为及其行为原因。结果发现：通过 FB ToM 任务的儿童表现出显著的规范边界敏感性，会减少对纯外群体情境下不公分配的第三方干预；在双方均为外群体情境中，通过 FB ToM 的儿童的第三方干预行为显著少于未通过 FB ToM 的儿童。当互动中存在内群体成员时，通过 FB ToM 的儿童的第三方干预显著多于其在不包含内群体成员情境中的第三方干预。原因分析表明，这种第三方干预的减少并非出于对外群体的冷漠或忽视公平，而是出于对规范适用范围的区分。研究表明，错误信念心理理论促使儿童形成情境敏感的规范边界意识：更严格维护涉及内群体成员的共享规范，更克制介入外群体内部情境。因此，心理理论的发展可能帮助儿童从应用普遍公平原则，转向基于群体边界的规范执行，从而更好地完成社会化进程。

人工智能（AI）正从算法工具向自主智能体演进，标志着“智能文明”这一新型文明形态的兴起。文章从文明演化的大历史视角，梳理了从语言、文字到人工智能的智能爆炸演化序列，揭示了智能文明对个体心智、社会关系、价值实践与生产方式的深层重构，分析了思想社会、人机混合体、制度对齐等全球前沿态势及其在社交、教育、科研、医学、企业管理和社会治理等领域的发展前景。在此基础上，文章进一步探讨智能文明的文化维度与文明互鉴可能，指出中华文明中的和合、共生理念为构建包容性智能文明治理框架提供了独特资源；同时，智能文明也带来劳动力替代、算法权力集中和认知主体性侵蚀等严峻挑战。最后，文章提出以制度对齐为核心，通过制度创新、理论建构、多元文化评测和国际合作，推动建立包容、公正、可持续的智能文明新秩序。

I don't think I have ever done anything as peculiar in my life. Among other things, it shows a young man looking with interest at a print on the wall of an exhibition that features himself. How can this be? Perhaps I am not far removed from Einstein's curved universe.'' So wrote M.C. Escher about his 1956 lithograph Print Gallery. Nearly half a century later, a mathematical analysis related its geometry to an untwisted source image through a conformal power map $z \mapsto z^α$, $α\in \mathbb{C}$. Building on this construction, we use a frozen text-to-image diffusion model to generate new self-referential scenes. Prompting alone does not enforce the recursion, while a post-hoc transformation can leave structures poorly connected. Applying the transformation during sampling is also insufficient: the denoiser may "repair" the intended distortion or drift out of the prescribed geometry. We construct a generalized inverse $T^\dagger$ of the non-invertible image transformation $T$, adapted to its recursive constraint. In the idealized formulation, the Penrose identity $TT^\dagger T = T$ makes $TT^\dagger$ an idempotent projection onto geometrically admissible images. Yet denoising only the transformed image remains an out-of-distribution task, even with projection. We therefore braid denoising steps with $T$ and $T^\dagger$: source-space steps develop the untwisted scene, while transformed-space steps refine its appearance and connections in the final geometry. We generate Print Gallery-like compositions and explore further transformations. Rather than distorting a finished image, we let the scene and its distortion develop together.

3D Gaussian avatars support fast rendering, however, their real-time animation is often challenged by the costly neural inference. We address this bottleneck and show that the animation of pretrained avatar models can be closely approximated by a linear combination of identity-independent blendshapes. Building on this finding, we introduce GALA (Gaussian Animation via Linear Approximation), a distillation method that replaces per-frame heavy neural decoding with a shallow coefficient predictor and a linear blend. To improve fidelity and reduce memory requirements, we propose to construct the basis using block-local PCA under a rendering-aware metric and a memory budget. Our method learns a shallow MLP network to predict blendshape coefficients and applies to various animation architectures without retraining original models. We validate GALA by accelerating the inference of three distinct avatar models for 3D animation of facial expressions and full-bodies with clothing dynamics. Across these models, our distillation generalizes to held-out identities and reduces CPU animation cost by up to three orders of magnitude while preserving most of the rendering quality. Excellent results of our method confirm the shared linear structure of learned avatar representations and enable highly efficient and accurate animation at frame rates reaching up to 60fps on mobile devices. Project page: https://ramazan793.github.io/gala/

Introducing new capabilities to frontier models has long been the goal of posttraining, which predominantly employs supervised finetuning (SFT) and reinforcement learning (RL) to this end. Conventional wisdom dictates that RL enables strong generalization on new tasks without losing existing capabilities, while SFT is prone to weak generalization and catastrophic forgetting. At the same time, SFT can learn from off-policy expert data, whereas RL must rely on a model's ability to find successful trajectories with repeated sampling. In our work, we seek to leverage the strength of on-policy learning while utilizing the privileged information contained in off-policy data. However, rather than modifying the learning objective to accommodate this data, we instead tailor the data distribution to better suit the learner. We introduce a Markov chain Monte Carlo (MCMC) sampling algorithm that progressively transforms off-policy traces to be more on-policy given a reference model for finetuning. Across tasks like scientific skill acquisition, mathematical reasoning, and open-ended expertise, our sampling algorithm enables SFT to rival prevailing posttraining techniques, often generalizing better and forgetting less than strong on-policy baselines. In addition, the resulting finetuned models exhibit strong distributional performance and are capable of learning beyond sharpening the base model distribution. At a higher level, our approach presents sampling as a model-native operator that shapes data for learnability, offering broader utility as a general-purpose primitive throughout the posttraining stack.