信息检索和推荐系统
Information Retrieval & Recommender Systems
百家 AI 在信息检索和推荐系统领域主要致力于信息检索系统全流程的优化工作，涵盖召回、精排、检索增强生成以及基于搜索智能体的复杂信息获取等方面，尤其侧重于基于大语言模型的信息检索技术研究。
Baijia AI is committed to optimizing the entire process of information retrieval and recommendation systems, covering recall, ranking, RAG, and complex information acquisition based on search agents, with a focus on LLM-based retrieval technology.
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信息检索：Information Retrieval:
涵盖全流程优化（召回、精排），重点包括基于大语言模型的检索技术（如稠密检索、生成式检索、重排序）、检索增强生成-RAG（搜索智能体、外部知识融合、开放域问答），以及模型泛化能力提升。
Covers full-process optimization (recall, ranking), LLM-based retrieval (dense/generative retrieval, re-ranking), Retrieval-Augmented Generation (search agents, knowledge fusion, open-domain QA), and model generalization.
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推荐系统：Recommender Systems:
关注多场景应用，包括传统方向（序列推荐、CTR 预估、跨域推荐、多模态推荐）和生成式创新（生成式推荐、大语言模型驱动推荐、对话式推荐），目标是通过更先进的模型实现精准信息匹配与用户偏好理解。
Focuses on multi-scenario applications including traditional (sequential, CTR, cross-domain, multi-modal) and generative innovations (generative, LLM-driven, conversational), aiming for precise matching and preference understanding.
垂域大模型微调和应用
Domain-Specific LLM Fine-tuning & Applications
百家 AI 研究聚焦于强化模型在下游任务的适配性与实用性，旨在通过高效策略提升任务执行能力，并探索轻量化部署路径。针对特定场景，采用高效微调和对齐手段，实现模型在垂直领域的轻量化与性能优化。
Focuses on enhancing model adaptability and practicality in downstream tasks, improving execution through efficient strategies and exploring lightweight deployment. Utilizing efficient fine-tuning and alignment for domain performance optimization.
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高效微调和对齐：Efficient Fine-tuning & Alignment:
研究高效训练方法，推进轻量化微调和对齐技术，利用合成数据优化训练过程，并强化领域对齐。
Researches efficient training methods, advances lightweight fine-tuning and alignment techniques, and optimizes training via synthetic data and domain alignment.
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可信大模型生成：Trustworthy LLM Generation:
引入大模型生成内容可信能力建设，追溯输出内容的生成源头，保障模型输出的可靠性、安全性与合规性，确保模型在各场景下稳定、可信运行。
Focuses on trustworthy generation and source traceability to ensure reliability, safety, and compliance, guaranteeing stable and credible operation across scenarios.
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智能体技术：AI Agent Technology:
聚焦于智能体知识与记忆管理领域，核心在于构建具备自主学习能力的个性化记忆系统MemoryOS。该系统通过多维度记忆建模机制，实现上下文语义的深度解析与动态关联，可精准捕捉用户历史交互中的偏好特征、场景需求，实现上下文的连贯处理与个性化交互，让模型交互更贴合用户需求。
Focuses on agent knowledge and memory management. The core is MemoryOS, a personalized memory system with autonomous learning, implementing deep semantic parsing and dynamic association to capture user preferences and enable coherent, personalized interaction.