#RAG

RAG is so back! This work shows that plain RAG over raw chat history beats complex agentic memory systems on most long-term memory benchmarks. (bookmark it) They tested Raw-RAG, which is just dense retrieval plus reranking over the raw conversation, feeding a reader LLM. Across LoCoMo, LongMemEval, MEME and MemoryAgentBench, it ranks first in six of seven benchmark and model settings, ahead of the strongest agentic memory system by 0.13 to 5.53 points. Ingestion on BEAM-1M takes 0.3 hours, against 3.2 to 27 hours for systems like Mem0, MemOS and Memora. Why does it hold up? Raw history keeps more of the evidence a question needs, including the relations and dependency paths behind an answer. Rewriting history into memory entries tends to drop those important bits. Even with complete access to the transformed memory stores, models answer less accurately than with raw evidence. Before adding a memory layer to your agent, at least run this baseline on your own data. https://arxiv.org/abs/2610.30755 Chat with Paper: https://academy.dair.ai/papers/is-complex-agentic-memory-always-needed-naive-rag-is-stronger-than-we-thought-2610.30755

LLMs deployed in specialized domains, from enterprise codebases to law firms, dig through a mountain of unstructured context! Our new paper, Librarian Agents, asks: can an agent adapt by curating its own long-context workspace, anticipating queries before they arrive? 🧵⬇️

CS 329Z: Engineering AI Agents Stanford / Fall 2026 @stanfordnlp https://cs329z.stanford.edu/ 课程定位：从"模型"到"系统"的工程学 覆盖：简单 LLM 流水线 → 复合 AI 系统 → 自主 Agent。三位讲师的背景也高度互补： @Diyi_Yang（斯坦福 NLP 教授，人机交互与社会计算方向） @michaelryan207（DSPy 核心贡献者，自动评估 AutoMetrics 作者） @jyangballin（SWE-agent / SWE-bench / SWE-smith 作者，软件工程 Agent 领域最重要的研究者之一） # 课程主线：三大工程挑战 贯穿全课的三个核心问题——分解（decomposition）、数据（data）、评估（evaluation）。11 周的内容基本围绕这三条线展开，可以分为五个模块： 模块 1：构建基元（Week 2–3） · LLM 作为构建材料：API/SDK（litellm）、结构化输出、约束生成、解码策略、test-time compute、上下文工程、模型选型与成本/延迟权衡 · RAG：embedding、向量库、分块策略、混合检索、cross-encoder 与 ColBERT 后期交互 · 工具调用：函数调用 API、MCP（Model Context Protocol）、工具设计、代码沙箱、错误处理与重试 模块 2：框架与设计模式（Week 3–5） · 框架层：DSPy（signature / module / optimizer）、LangChain/LangGraph、LlamaIndex，重点是"框架抽象了什么 vs. 你手写了什么" · 设计模式：workflow vs. agent 的分类学，五种可组合 workflow 模式，ReAct / plan-and-execute / reflection，"scaffold（脚手架）本身就是设计决策" · 记忆架构：短期/长期记忆、记忆作为工具动作、文件系统作为外化记忆、跨 Agent 记忆（MemGPT、Mem0、Generative Agents） · 多 Agent 系统：编排模式、handoff 与状态传递，以及一个很有态度的对照阅读——既读 AutoGen，也读《Why Do Multi-Agent LLM Systems Fail?》和 Neubig 的《Don't Sleep on Single-agent Systems》 模块 3：优化（Week 5） · 从提示词到微调的全景：GEPA、MIPROv2、OPRO、TextGrad（提示优化）；LoRA/QLoRA、蒸馏、RLHF/DPO（权重优化）；test-time scaling（推理算力） · 核心问题是决策框架：什么时候优化 prompt、什么时候优化 weights、什么时候堆推理算力 模块 4：数据与评估（Week 6–8）——最有分量的部分 · 数据：trace、demonstration、feedback 三类数据；训练数据 vs. 评估数据；数据飞轮；合成数据；从 Agent 轨迹构建数据集（SWE-smith） · 评估基础：为什么 eval 难；4 元组框架（request / environment / stopping criteria / scorer）；好 benchmark 的性质；tinyBenchmarks · 评估基础设施：三类 grader、LLM-as-judge 的 prompt 设计与已知偏差、pairwise vs. pointwise、非确定性指标 pass@k vs. pass^k、harness 设计 模块 5：安全与前沿（Week 8–11） · 安全：工具访问的隐私风险、prompt injection（含间接注入）、红队、沙箱与权限模型、输出护栏、human-in-the-loop · Coding Agent：SWE-agent、Claude Code、OpenHands 的端到端架构对比 · 主动式 Agent：从 reactive 到 proactive，General User Models（GUM）、Next Action Prediction，以及"Agent 何时应主动、何时应等待"的 mixed-initiative 问题 · 开放问题：多模态/web/计算机使用 Agent、科学 Agent、长时运行架构、生产可观测性（tracing、monitoring、成本管理） # 作业设计：一手建、一手评 HW1：从零构建 Agent 系统（10%） 给定论文库，构建能检索并推理回答科学问题的 Agent。 · Part A：只用 litellm 手写 RAG + 工具调用 + ReAct 式 Agent 循环 · Part B：用 DSPy 重建关键组件，并反思框架抽象了什么 HW2：评估一个 Agent（10%） 给定一个预构建 Agent，设计完整评估套件：代码型 grader、至少一个 LLM-as-judge、用 4 元组框架构建 benchmark 任务、错误分析。

https://x.com/i/article/2108311949260054528

Launching Higher-Order Extraction. It deterministically computes the numbers you need from the raw values on the page, even when the final answer isn't printed anywhere.

Inspired by @EGafni’s Twitter thread on combining Jev with PageIndex. We show how to build long-document search with @typesafeai Jev + PageIndex. No vector database. No embeddings. Open source: https://github.com/VectifyAI/jev-doc-search 🧵👇

We built a new way to train contextual embedding models, which encode each chunk of a document with the whole document in view. pplx-embed-v2-context-9b-preview sets a new state of the art on ConTEB and @turbopuffer's new, privately held context-bench. https://www.perplexity.ai/hub/blog/contextual-embedding-beyond-the-gold-passage

We used Jev to retrieve sales data 20x faster, 10x cheaper, and 12% more accurate than GPT-5 Mini. Every time a Rox agent answers a query, it pulls from relevant transcripts, emails, CRM notes, news, and documents. We benchmarked two ways of retrieving them: LLM-based reranking Jev classification The results speak for themselves. Jev was faster, cheaper, and more accurate. Revenue agents require thousands of points of context to understand accounts, chart relationships, and execute on sales. This research will help us serve that context with frontier-level performance for our customers.