🌳 AI is finally evolving from a tool that runs one-off experiments into something closer to a researcher itself, one that compounds knowledge across time. A new autonomous research framework grows hypotheses as a single living tree. Title: Toward Generalist Autonomous Research via Hypothesis-Tree Refinement URL: 🔍 Overview This work proposes Arbor, a framework for long-term autonomous research. Its core idea, Hypothesis-Tree Refinement, links hypotheses, the artifacts produced by experiments, the evidence gathered, and the insights distilled from them into a single persistent tree. With every experiment the tree is updated, continuously sharpening the search frontier that decides which direction to explore next. ❓ Challenges Solved Previous LLM research agents could barely manage single, isolated experiments. ・They could not maintain a big-picture strategy about which hypothesis to pursue across multiple attempts ・Lessons learned in one experiment were rarely carried into the next, so exploration started from scratch each time ・They lacked a mechanism to tell promising branches from dead ends and allocate limited compute accordingly In short, knowledge never compounded, and that was the real bottleneck. 💡 Methodology & Proposed Approach Arbor is built from two kinds of agents and a persistent tree that connects them. ・A long-lived coordinator acts as the strategist, surveying the hypothesis tree and choosing what to test next. Because it persists across sessions, it preserves long-term coherence ・Short-lived executors implement and test individual hypotheses in isolated environments, then retire once their job is done ・The hypothesis tree links hypotheses, evidence, artifacts, and insights over time, propagating reusable lessons across the whole effort This turns research from a bag of isolated experiments into a cumulative process where strategy, execution, and evidence build on each other. 🎯 Use Cases Promising applications include AutoML and automated machine learning optimization that improve through continuous experimentation, as well as automating the scientific discovery process itself. The appeal is running long stretches of trial and error strategically, without a human in the loop. 📊 Experimental Results Arbor was evaluated under an Autonomous Optimization setting across six real research tasks. ・It achieved the best held-out results on all six tasks ・It recorded over 2.5x average held-out gain compared to Codex and Claude Code ・On MLE-Bench Lite it reached 86.36% Any Medal when paired with GPT-5.5, the strongest result among the systems compared #AIAgents# #AutonomousResearch#

AI systems now write research papers autonomously — yet phantom references, method-code misalignment, and unreproducible scores have become endemic failures undermining scientific integrity. Title: Science One Framework: A verifiable autonomous research framework via Chain-of-Evidence Google Cloud's Science One Framework treats verifiability as a first-class architectural constraint through the Chain-of-Evidence principle, solving the trustworthiness crisis in autonomous AI research at its root. 🔍 Highlight 1 — Chain-of-Evidence (CoE): two foundational principles Completeness: every claim carries a recorded evidence chain. Correctness: each chain genuinely supports its claim. These two principles eliminate phantom references entirely — baseline systems showed rates up to 21% — while achieving best-in-class method-code alignment across all evaluated systems. The key difference from prior work: evidence chains are constructed at claim-generation time, not retrofitted as a post-hoc check. 🏗 Highlight 2 — Three-module architecture Problem Investigator builds citation graphs from up to 100 full-text PDFs via Semantic Scholar API, grounding every reference in retrieved data rather than model memory. Discovery Engine explores parallel solution branches while keeping immutable records of all raw evaluator outputs. Paper Writer and Claim Verifier binds every factual claim to specific workspace artifacts and conservatively reconciles misalignments rather than deleting them — preserving scientific transparency. 🏆 Highlight 3 — MLE-Bench and Parameter-Golf results Across five Kaggle competitions covering medical imaging, fine-grained recognition, and 3D perception: two Gold Medals and two Silver Medals. Won the 3D Object Detection task where every baseline system failed completely. On Parameter-Golf — a live LLM training competition under strict hardware and file-size constraints — achieved state-of-the-art as of April 27, 2026, while baselines could not produce valid submissions at all. Rigor and capability don't trade off. Science One outperformed five state-of-the-art systems including AI Scientist v2, AutoResearchClaw, and DeepScientist, setting a new standard for verifiable autonomous research. #AIResearch# #AutonomousScience#

A useful but little-known Gemini API feature 🔬 Research across multiple sources and compile a report. Gemini's agent can handle that. Gemini's "Deep Research" is an autonomous research agent that plans, gathers information, analyzes, and generates reports. It also supports MCP integration. 📌 Title: Deep Research (agent, preview) 🔗 URL: 🧩 Overview Research tasks like "investigate the latest trends in this technology and summarize" require exploring multiple sources, organizing information, and producing a coherent analysis. Deep Research does this autonomously: it creates a research plan, gathers information from multiple sources, analyzes and structures the findings, and generates a report. 🛠 How to use it Describe your research objective and scope in natural language. The agent creates an investigation plan, uses web search and tools to collect information, shows progress as it works, and outputs a structured report. Connect MCP-compatible tools to let it pull from your own data sources as well. 🏗 Building it into production ・Automated market research: generate periodic reports on competitive landscape and technology trends. ・Due diligence support: comprehensively gather and analyze publicly available information on investment or partnership targets. ・Technical investigation: auto-run comparative studies of new libraries and frameworks. ・Academic research assistance: semi-automate paper surveys on specific topics with organized summaries. 💡 Use cases 📊 Periodic competitive and market trend reports 💼 Due diligence and corporate research 🔧 Comparative studies for technology selection 📚 Academic paper surveys and organization ⚠️ Watch out As a preview feature, accuracy and comprehensiveness aren't perfect. Always have humans review agent output and fact-check before important decisions. Research depth trades off against time and cost, so set an appropriate scope. ✨ Automate the entire "research, organize, report" flow. Start with a routine research task and see what Deep Research can do. #Gemini# #LLM#