Mammalian brains handle complex reasoning by integrating information across
brain regions specialized for particular sensory modalities. This enables
improved robustness and generalization versus deep neural networks, which
typically process one modality and are vulnerable to perturbations. While
defense methods exist, they do not generalize well across perturbations. We
developed a fusion model combining background and foreground features from CNNs
trained on Imagenet and Places365. We tested its robustness to
human-perceivable perturbations on MS COCO. The fusion model improved
robustness, especially for classes with greater context variability. Our
proposed solution for integrating multiple modalities provides a new approach
to enhance robustness and may be complementary to existing methods.

哺乳动物的大脑通过整合特定感官模式的脑区的信息来处理复杂推理，从而提高鲁棒性和泛化能力。我们开发了一个融合模型，结合了在 Imagenet 和 Places365 上训练的 CNN 的背景和前景特征，测试了它对人可感知变化的抵抗能力，并在 MS COCO 上进行了验证。融合模型提高了鲁棒性，特别适用于具有更大上下文变异性的类别。我们提出的多模态整合解决方案为增强鲁棒性提供了一种新方法，并可能与现有方法相辅相成。

上下文融合增强图像模糊鲁棒性

Contextual fusion enhances robustness to image blurring

In order to thrive in hostile and ever-changing natural environments,
mammalian brains evolved to store large amounts of knowledge about the world
and continually integrate new information while avoiding catastrophic
forgetting. Despite the impressive accomplishments, large language models
(LLMs), even with retrieval-augmented generation (RAG), still struggle to
efficiently and effectively integrate a large amount of new experiences after
pre-training. In this work, we introduce HippoRAG, a novel retrieval framework
inspired by the hippocampal indexing theory of human long-term memory to enable
deeper and more efficient knowledge integration over new experiences. HippoRAG
synergistically orchestrates LLMs, knowledge graphs, and the Personalized
PageRank algorithm to mimic the different roles of neocortex and hippocampus in
human memory. We compare HippoRAG with existing RAG methods on multi-hop
question answering and show that our method outperforms the state-of-the-art
methods remarkably, by up to 20%. Single-step retrieval with HippoRAG achieves
comparable or better performance than iterative retrieval like IRCoT while
being 10-30 times cheaper and 6-13 times faster, and integrating HippoRAG into
IRCoT brings further substantial gains. Finally, we show that our method can
tackle new types of scenarios that are out of reach of existing methods. Code
and data are available at this https URL

本研究介绍了一种灵感来源于人类长期记忆的大脑索引理论的新型检索框架 HippoRAG，通过协同作用 LLMs、知识图谱和 Personalized PageRank 算法模拟新经历的深度高效知识整合。与 RAG 方法进行多跳问答比较后表明 HippoRAG 方法表现出色，并超过先进方法 20%。与 IRCoT 相比，HippoRAG 的单步检索具有可比或更好的性能，并且成本更低、速度更快。此外，将 HippoRAG 引入 IRCoT 可以进一步获得显著的收益，同时展示了该方法可以处理现有方法无法解决的新式场景。

HippoRAG：针对大型语言模型的神经生物学启发式长期记忆

HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language  Models

I propose that the labyrinthine patterns of the cortices of mammalian brains
may be formed by a Turing instability of interacting axonal guidance species
acting together with the mechanical strain imposed by the interconnecting
axons.

此研究提出哺乳动物的大脑皮层迷宫样式可能由相互作用的轴突引导物种和互连轴突施加的机械应变形成的图灵不稳定性所形成。