# AI-generated research reports Long-form syntheses of multiple sources produced by search-augmented large-language-model agents. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Science One Framework: A verifiable autonomous research framework via Chain-of-Evidence DevFeed: [Science One Framework: A verifiable autonomous research framework via Chain-of-Evidence]() Original publisher: [Read original article]() Published: 2026-07-30T20:36:36Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [Google](), [AI-generated research reports](), [Large language models (LLMs)](), [Hallucination detection](), [LLM evaluation / benchmarking]() Tags: [agents](), [ai](), [autonomous-agents](), [benchmarks](), [evaluation](), [general-science](), [google](), [hallucinations](), [language-models](), [machine-intelligence](), [natural-language-processing](), [research](), [research-prototype]() ### AI overview Google Research introduces the Science One Framework, an experimental autonomous research prototype built around Chain-of-Evidence. It is designed to make AI-generated research verifiable by linking claims to supporting evidence and by auditing papers against their code and evidence. The article reports that the framework eliminates phantom references and produces fully verifiable scores in the described evaluations. ### Source excerpt General Science ## Building Reliable Agentic AI Systems DevFeed: [Building Reliable Agentic AI Systems]() Original publisher: [Read original article]() Author: Martin Fowler (martin@martinfowler.com) Published: 2026-06-16T12:11:00Z Content type: article Language: en Sources: [Martin Fowler]() Topics: [Artificial Intelligence](), [Retrieval Augmented Generation (RAG)](), [text2sql](), [AI search](), [Generative AI](), [AI-generated research reports](), [data](), [Orchestration](), [observability](), [Prompt Engineering]() Tags: [agentic-ai](), [ai](), [building](), [case-study](), [data](), [drug-discovery](), [efficiency](), [engineering](), [generative-ai](), [information-retrieval](), [language-models](), [llms](), [orchestration](), [production](), [rag](), [research](), [retrieval](), [safety](), [sql](), [technical]() ### AI overview This case study describes PRINCE, a cloud-hosted platform developed by Bayer AG with Thoughtworks for pharmaceutical research. It combines Agentic Retrieval-Augmented Generation and Text-to-SQL to help researchers query decades of safety study reports, answer complex questions, and draft regulatory documents. The article focuses on context engineering, orchestration, recovery, observability, transparency, explainability, human oversight, governance, and compliance in production-ready agentic AI systems. ### Source excerpt One of the most interesting projects my colleagues have done with LLMs has been building a system with Bayer to allow pharmaceutical researchers to query decades of information about studies buried in PDF reports. Sarang Sanjay Kulkarni describes its evolution from keyword-based search to an intelligent research assistant capable of answering complex questions and drafting regulatory documents. more... ## Research with ChatGPT DevFeed: [Research with ChatGPT]() Original publisher: [Read original article]() Published: 2026-04-10T00:00:00Z Content type: tutorial Language: en Sources: [OpenAI News]() Topics: [AI Chat](), [AI-generated research reports]() Tags: [ai](), [chatgpt](), [how-to](), [openai-academy](), [research](), [search]() ### AI overview A guide to using ChatGPT search and deep research to gather current web information, analyze sources, and create cited summaries. ### Source excerpt Learn how to research with ChatGPT using search and deep research to find up-to-date information, analyze sources, and generate structured insights. ## Building a Deep Research Agent with Neon and Durable Endpoints DevFeed: [Building a Deep Research Agent with Neon and Durable Endpoints]() Original publisher: [Read original article]() Author: Charly Poly Published: 2026-02-24T17:14:34Z Content type: tutorial Language: en Sources: [Blog -- Neon Docs]() Topics: [AI research agents](), [AI-generated research reports](), [web applications](), [AI-assisted coding]() Tags: [agent](), [ai](), [building](), [claude-code](), [community](), [embeddings](), [next-js](), [postgres](), [product](), [research](), [serverless]() ### AI overview A tutorial on building a recursive AI research agent with Neon, Inngest durable endpoints, semantic memory, and a Next.js progress UI. ### Source excerpt Every AI lab is shipping research agents. OpenAI's Deep Research, Perplexity, and Gemini's research mode. These products are not simple RAG pipelines. Recent papers like DeepResearcher and Step-DeepResearch formalize what makes them work: a recursive loop of planning, searching,... ## Building Deep Research: How we Achieved State of the Art DevFeed: [Building Deep Research: How we Achieved State of the Art]() Original publisher: [Read original article]() Author: Michael Griff; Dean Sacoransky; Noah Nefsky Published: 2025-11-24T17:40:14Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [AI Bots](), [AI Chat](), [AI-generated research reports]() Tags: [agent](), [agents](), [architecture](), [building](), [compression](), [engineering](), [hallucinations](), [latency](), [llms](), [optimization](), [orchestration](), [research](), [search](), [tools](), [workflows]() ### AI overview The article outlines principles for building deep-research agent harnesses that remain effective as models improve. It emphasizes simplified orchestration, reliable tool calling, and context engineering to reduce context overload, hallucinations, and latency. ### Source excerpt The task of building an agent harness is to create a software layer that enhances a model's runtime execution through context management, tool invocations, loop control, orchestration, and error handling. Building applications on top of rapidly improving models is, however, a modern engineering challenge. How can we design software today that absorbs the performance gains from future model releases? ## Le Chat dives deep. DevFeed: [Le Chat dives deep.]() Original publisher: [Read original article]() Published: 2025-07-17T12:00:00Z Content type: release Language: en Sources: [Mistral AI Blog]() Topics: [AI Chat](), [AI-generated research reports]() Tags: [agents](), [ai](), [images](), [models](), [multimodal](), [research](), [search](), [tools](), [voice]() ### AI overview Le Chat adds Deep Research, voice interaction through Voxtral, multilingual reasoning through Magistral, projects for organizing chats, and image editing with Black Forest Labs. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models.