Language models have demonstrated impressive capabilities across various
natural language processing tasks, yet they struggle with planning tasks
requiring multi-step simulations. Inspired by human cognitive processes, this
paper investigates the optimal planning power of language models that can
construct a cognitive map of a given environment. Our experiments demonstrate
that cognitive map significantly enhances the performance of both optimal and
reachable planning generation ability in the Gridworld path planning task. We
observe that our method showcases two key characteristics similar to human
cognition: \textbf{generalization of its planning ability to extrapolated
environments and rapid adaptation with limited training data.} We hope our
findings in the Gridworld task provide insights into modeling human cognitive
processes in language models, potentially leading to the development of more
advanced and robust systems that better resemble human cognition.

本文研究了语言模型的最佳规划能力，可以构建给定环境的认知地图，实验证明认知地图显著提高了网格世界路径规划任务中的规划生成能力，同时展示了与人类认知类似的两个关键特征：将规划能力泛化到外推环境和有限训练数据下的快速适应。希望本研究结果能够为基于语言模型的模拟人类认知流程提供启示，从而开发出更先进、更健壮的系统，更好地模拟人类思维。

语言模型的认知地图：通过动词表示世界模型的最优规划

Cognitive Map for Language Models: Optimal Planning via Verbally  Representing the World Model

We tackle the task of scalable unsupervised object-centric representation
learning on 3D scenes. Existing approaches to object-centric representation
learning show limitations in generalizing to larger scenes as their learning
processes rely on a fixed global coordinate system. In contrast, we propose to
learn view-invariant 3D object representations in localized object coordinate
systems. To this end, we estimate the object pose and appearance representation
separately and explicitly map object representations across views while
maintaining object identities. We adopt an amortized variational inference
pipeline that can process sequential input and scalably update object latent
distributions online. To handle large-scale scenes with a varying number of
objects, we further introduce a Cognitive Map that allows the registration and
query of objects on a per-scene global map to achieve scalable representation
learning. We explore the object-centric neural radiance field (NeRF) as our 3D
scene representation, which is jointly modeled within our unsupervised
object-centric learning framework. Experimental results on synthetic and real
datasets show that our proposed method can infer and maintain object-centric
representations of 3D scenes and outperforms previous models.

我们提出了一种可伸缩的无监督面向对象中心的 3D 场景表示学习方法，通过学习在局部对象坐标系统中视角不变的 3D 对象表示，从而推断和维护 3D 场景的对象中心表示，并优于以前的模型。

可扩展的三维对象为中心学习的变异推断

Variational Inference for Scalable 3D Object-centric Learning

The hippocampal-entorhinal complex plays a major role in the organization of
memory and thought. The formation of and navigation in cognitive maps of
arbitrary mental spaces via place and grid cells can serve as a representation
of memories and experiences and their relations to each other. The multi-scale
successor representation is proposed to be the mathematical principle
underlying place and grid cell computations. Here, we present a neural network,
which learns a cognitive map of a semantic space based on 32 different animal
species encoded as feature vectors. The neural network successfully learns the
similarities between different animal species, and constructs a cognitive map
of 'animal space' based on the principle of successor representations with an
accuracy of around 30% which is near to the theoretical maximum regarding the
fact that all animal species have more than one possible successor, i.e.
nearest neighbor in feature space. Furthermore, a hierarchical structure, i.e.
different scales of cognitive maps, can be modeled based on multi-scale
successor representations. We find that, in fine-grained cognitive maps, the
animal vectors are evenly distributed in feature space. In contrast, in
coarse-grained maps, animal vectors are highly clustered according to their
biological class, i.e. amphibians, mammals and insects. This could be a
possible mechanism explaining the emergence of new abstract semantic concepts.
Finally, even completely new or incomplete input can be represented by
interpolation of the representations from the cognitive map with remarkable
high accuracy of up to 95%. We conclude that the successor representation can
serve as a weighted pointer to past memories and experiences, and may therefore
be a crucial building block for future machine learning to include prior
knowledge, and to derive context knowledge from novel input.

该研究使用神经网络学习 32 种动物的特征向量，并基于继承表示原理构建了一个 “动物空间” 的认知地图，该地图能够使用插值技术准确地表示完全新的或不完整的输入，并在不同的认知地图级别中呈现出不同的特征。