The advent of Large Language Models (LLMs) and Generative AI has
revolutionized natural language applications across various domains. However,
high-stakes decision-making tasks in fields such as medical, legal and finance
require a level of precision, comprehensiveness, and logical consistency that
pure LLM or Retrieval-Augmented-Generation (RAG) approaches often fail to
deliver. At Elemental Cognition (EC), we have developed a neuro-symbolic AI
platform to tackle these problems. The platform integrates fine-tuned LLMs for
knowledge extraction and alignment with a robust symbolic reasoning engine for
logical inference, planning and interactive constraint solving. We describe
Cora, a Collaborative Research Assistant built on this platform, that is
designed to perform complex research and discovery tasks in high-stakes
domains. This paper discusses the multi-step inference challenges inherent in
such domains, critiques the limitations of existing LLM-based methods, and
demonstrates how Cora's neuro-symbolic approach effectively addresses these
issues. We provide an overview of the system architecture, key algorithms for
knowledge extraction and formal reasoning, and present preliminary evaluation
results that highlight Cora's superior performance compared to well-known LLM
and RAG baselines.

通过整合精细调整的大型语言模型与具有逻辑推理、规划和交互式约束求解能力的强大符号推理引擎，本研究介绍了 Cora，一个基于神经符号人工智能平台构建的协作研究助手，该助手旨在高风险领域执行复杂的研究和探索任务。本研究讨论了此类领域中的多步推理挑战，批评了现有的基于大型语言模型的方法的局限性，并展示了 Cora 的神经符号方法如何有效应对这些问题。我们提供了系统架构概述、知识提取和形式推理的关键算法，并展示了初步评估结果，突显了 Cora 与知名的大型语言模型和 RAG 方法相比的优越性能。

多步骤的无结构数据知识检索与推理

Multi-step Knowledge Retrieval and Inference over Unstructured Data

Progress in continual reinforcement learning has been limited due to several
barriers to entry: missing code, high compute requirements, and a lack of
suitable benchmarks. In this work, we present CORA, a platform for Continual
Reinforcement Learning Agents that provides benchmarks, baselines, and metrics
in a single code package. The benchmarks we provide are designed to evaluate
different aspects of the continual RL challenge, such as catastrophic
forgetting, plasticity, ability to generalize, and sample-efficient learning.
Three of the benchmarks utilize video game environments (Atari, Procgen,
NetHack). The fourth benchmark, CHORES, consists of four different task
sequences in a visually realistic home simulator, drawn from a diverse set of
task and scene parameters. To compare continual RL methods on these benchmarks,
we prepare three metrics in CORA: Continual Evaluation, Isolated Forgetting,
and Zero-Shot Forward Transfer. Finally, CORA includes a set of performant,
open-source baselines of existing algorithms for researchers to use and expand
on. We release CORA and hope that the continual RL community can benefit from
our contributions, to accelerate the development of new continual RL
algorithms.

本文介绍了 CORA 平台，该平台针对 Continual Reinforcement Learning 智能体提供了基准、基线和度量标准，其中基准旨在评估连续 RL 挑战的不同方面，而度量标准提供连续评估、隔离遗忘和零射前传递。同时，该平台还包括一组表现良好、开源的现有算法基线，旨在加速新 Continual Reinforcement Learning 算法的开发。