Nomad is an autonomous system designed to explore and discover insights within large data corpora. It builds an explicit Exploration Map to systematically traverse a domain, generating and testing hypotheses with an explorer agent that leverages document, web, and database searches. After verification, it produces cited reports and meta-reports, and its evaluation framework assesses trustworthiness, quality, and diversity, showing superior performance over baselines on UN, WHO, and arXiv datasets.
arXiv:2609.06366v1 Announce Type: new Abstract: Scientific discovery in data-rich domains is currently constrained by human bandwidth: the growth in the volume and complexity of real-world data far o...
Optimist Deep Research is an open‑source agent that conducts literature research as an iterative, state‑centric process. It breaks a research question into a tree of subquestions, uses search intents to query multiple scholarly databases, and merges results into a provenance‑preserving document registry. The agent iteratively expands the registry via citation snowballing, evaluates evidence, and refines the research plan through a critic that can add, split, rephrase, deprioritize, or close subquestions, ultimately producing a report with every citation linked to a retrieved document.
arXiv:2607. 20926v1 Announce Type: new Abstract: Scientific research involves complex information-seeking and reasoning workflows across heterogeneous sources.
arXiv:2609.37226v1 Announce Type: cross Abstract: Answering questions and completing tasks over large document collections often requires connecting evidence spread across multiple documents, such as...
DataSTORM is an LLM‑based agentic system designed to conduct deep research over large‑scale structured databases and internet sources. It applies principles of Exploratory Data Analysis and Data Storytelling to frame research as a thesis‑driven analytical process, iteratively generating hypotheses, performing quantitative reasoning, and crafting coherent narratives. Evaluations on InsightBench and a new ACLED‑based dataset show that DataSTORM surpasses existing systems, achieving significant improvements in insight‑level recall and summary‑level scores.
Scientific datasets are commonly organized as hierarchical repositories containing heterogeneous and interdependent files, making their inspection, integration, and analysis labor-intensive and reliant on domain expertise. Although large language model (LLM) agents have advanced substantially in planning, reasoning, and tool use, existing research has largely overlooked their ability to interact with real scientific data assets through executable environments.
arXiv:2607. 28229v1 Announce Type: cross Abstract: The web is increasingly accessed by AI agents rather than humans.
JevGPT is a dual‑system architecture for autonomous research that combines fast evidence processing (System One) with deliberative research planning (System Two). System One quickly locates and evaluates relevant passages, while System Two sets evidence objectives and integrates findings into subsequent research steps. Experiments on question answering and document ranking demonstrate that JevGPT outperforms comparable agents, achieving faster completion, lower cost, and broader source coverage.
arXiv:2606. 15367v1 Announce Type: new Abstract: Deep research agents aim to solve complex knowledge-intensive tasks through long-horizon planning, evidence gathering, reasoning, and report generation.
ScholarCatalyst is a new benchmark that evaluates how well AI systems can retrieve research papers that inspire new work. The dataset was created by having 184 lead authors of 207 recent computer science papers annotate which earlier papers helped their projects, providing detailed rationales. The benchmark tests retrieval from the literature available at the start of a project, revealing that current agentic search and even advanced models like Claude Fable 5.1 perform only modestly better than simple embedding retrieval.
arXiv:2607. 06482v1 Announce Type: cross Abstract: Current benchmarks for evaluating Large Language Models (LLMs) in data analysis often fail to reflect real-world settings.
arXiv:2606. 10460v1 Announce Type: cross Abstract: Recent large language models (LLMs) have shown rapid progress in reading-based question answering (QA), where evidence is explicitly provided or can be trivially retrieved.