This paper introduces RAISE (Reasoning and Acting through Scratchpad and
Examples), an advanced architecture enhancing the integration of Large Language
Models (LLMs) like GPT-4 into conversational agents. RAISE, an enhancement of
the ReAct framework, incorporates a dual-component memory system, mirroring
human short-term and long-term memory, to maintain context and continuity in
conversations. It entails a comprehensive agent construction scenario,
including phases like Conversation Selection, Scene Extraction, CoT Completion,
and Scene Augmentation, leading to the LLMs Training phase. This approach
appears to enhance agent controllability and adaptability in complex,
multi-turn dialogues. Our preliminary evaluations in a real estate sales
context suggest that RAISE has some advantages over traditional agents,
indicating its potential for broader applications. This work contributes to the
AI field by providing a robust framework for developing more context-aware and
versatile conversational agents.

该论文介绍了 RAISE（Reasoning and Acting through Scratchpad and Examples），这是一种增强了 GPT-4 等大型语言模型与对话代理之间整合的先进架构。RAISE 具有双组件记忆系统，与人类的短期和长期记忆镜像，以维持对话的上下文和连续性。这种方法似乎增强了代理在复杂的多回合对话中的可控性和适应性，初步评估结果表明 RAISE 在房地产销售背景中比传统代理有一些优势，显示出更广泛应用的潜力。该研究通过提供一个强大的框架来开发更具上下文感知和多用途的对话代理，为人工智能领域做出了贡献。

从 LLM 到对话代理：带有增强记忆的架构和大型语言模型的精细调整

From LLM to Conversational Agent: A Memory Enhanced Architecture with  Fine-Tuning of Large Language Models

This paper aims to achieve universal segmentation of arbitrary semantic
level. Despite significant progress in recent years, specialist segmentation
approaches are limited to specific tasks and data distribution. Retraining a
new model for adaptation to new scenarios or settings takes expensive
computation and time cost, which raises the demand for versatile and universal
segmentation model that can cater to various granularity. Although some
attempts have been made for unifying different segmentation tasks or
generalization to various scenarios, limitations in the definition of paradigms
and input-output spaces make it difficult for them to achieve accurate
understanding of content at arbitrary granularity. To this end, we present
UniLSeg, a universal segmentation model that can perform segmentation at any
semantic level with the guidance of language instructions. For training
UniLSeg, we reorganize a group of tasks from original diverse distributions
into a unified data format, where images with texts describing segmentation
targets as input and corresponding masks are output. Combined with a automatic
annotation engine for utilizing numerous unlabeled data, UniLSeg achieves
excellent performance on various tasks and settings, surpassing both specialist
and unified segmentation models.

实现任意语义级别的通用分割模型 UniLSeg，通过语言指导实现各种任务和设置的优秀性能。

用语言指导进行任意粒度的通用分割

Universal Segmentation at Arbitrary Granularity with Language  Instruction

Text-to-image diffusion models have recently received increasing interest for
their astonishing ability to produce high-fidelity images from solely text
inputs. Subsequent research efforts aim to exploit and apply their capabilities
to real image editing. However, existing image-to-image methods are often
inefficient, imprecise, and of limited versatility. They either require
time-consuming fine-tuning, deviate unnecessarily strongly from the input
image, and/or lack support for multiple, simultaneous edits. To address these
issues, we introduce LEDITS++, an efficient yet versatile and precise textual
image manipulation technique. LEDITS++'s novel inversion approach requires no
tuning nor optimization and produces high-fidelity results with a few diffusion
steps. Second, our methodology supports multiple simultaneous edits and is
architecture-agnostic. Third, we use a novel implicit masking technique that
limits changes to relevant image regions. We propose the novel TEdBench++
benchmark as part of our exhaustive evaluation. Our results demonstrate the
capabilities of LEDITS++ and its improvements over previous methods. The
project page is available at this https URL .

文本到图像扩散模型对于仅使用文本输入产生高保真度图像的惊人能力近来引起了 increasing interest。然而，现有的图像到图像方法常常低效、不精确且使用范围有限。为了解决这些问题，我们介绍了一种高效且多用途的精确文本图像操作技术 LEDITS++。