arXiv AI
DeepSurvey: Agent-Oriented Automated Survey Generation with Analytical Depth and Citation Reliability
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arXiv AI
Jul 8
Prompt-to-Paper: Agentic AI System for Bioinformatics
arXiv:2607. 05456v1 Announce Type: new Abstract: While recent advances in large language models have enabled end-to-end automated manuscript generation, existing systems suffer from three critical deficiencies: (i) generated claims are not deterministically grounded in verifiable literature, (ii) experimental results are frequently fabricated rather than executed, and (iii) there exists no standardized, multi-dimensional framework to assess whether AI-generated manuscripts meet the quality and rigor required for real-world publication.
arXiv Computation and Language
Sep 22
BizSage: A Self-Evolving Multi-Agent Framework for Business Research with Efficient Knowledge Retrieval
arXiv:2609.22235v1 Announce Type: new Abstract: While multi-agent systems based on large language models (LLMs) have shown promise in automating the progressive workflow of academic research, extendi...
arXiv Computation and Language
Sep 1
Extending AI for Research to the Humanities: A Multi-Agent Framework for Evidence-Grounded Scholarship
arXiv:2605.30947v4 Announce Type: replace Abstract: LLM-based research agents have advanced rapidly in science and engineering, where research is organized around executable experiments, code, and qu...
arXiv Computation and Language
Aug 28
DataSTORM: Deep Research on Large-Scale Databases using Exploratory Data Analysis and Data Storytelling
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.
arXiv Computation and Language
Sep 22
DeepInstructor: An Agentic AI Instructor for Experience-Driven Idea Evaluation
arXiv:2609.22104v1 Announce Type: new Abstract: As automated scientific discovery advances, Large Language Models (LLMs) can now generate research ideas at an unprecedented scale, shifting the bottle...
arXiv AI
Aug 20
DeepWeaver: Bridging the Evidence Synthesis Gap in Open-Ended Question Answering
DeepWeaver is a framework designed to improve open‑ended question answering by weaving noisy retrieved evidence into comprehensive, well‑cited answers. It introduces Thought Block Chains (TBCs) that organize claims, key information, and supporting evidence, and uses subordinate TBCs to refine and expand the evidence before final generation. Evaluations on LoQA and DeepResearch Bench show that DeepWeaver enhances content sufficiency, citation grounding, and detail preservation across multiple LLMs.