arXiv AI
InsightRAG: Aggregating Distributed Scholarly Judgments for Scientific Survey Generation
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arXiv AI
Jul 9
RWGBench: Evaluating Scholarly Positioning in Related Work Generation
arXiv:2606. 24894v2 Announce Type: replace-cross Abstract: Large language models have shown strong fluency in scientific writing, yet the evaluation of related work generation (RWG) remains limited.
arXiv AI
Sep 30
DeepSurvey: Agent-Oriented Automated Survey Generation with Analytical Depth and Citation Reliability
arXiv:2605.29522v2 Announce Type: replace Abstract: As scientific literature grows rapidly and research increasingly involves AI agents, automated survey generation has become a key capability for bo...
arXiv AI
Aug 20
Self-prompting and cross-model consensus enable reproducible data extraction from scientific literature with large language models
The paper investigates how large language models can extract contextualized data from scientific literature. It presents four workflows: expert‑written prompts, self‑generated prompts, autonomous literature discovery, and dataset creation from guidelines. While models perform well with prompts, they struggle with context, hallucinate references, and still need human oversight for final validation.
arXiv Computation and Language
3d ago
SurveyGraph: Generating Academic Surveys via Methods-Entity Knowledge Graphs
SurveyGraph introduces a framework that uses a Methods‑Entity Knowledge Graph (MEKG) to encode relationships such as follow‑on, competition, and shared‑resource links between research methods. The graph enables core‑paper discovery, hybrid outlining, and evidence‑gated narrative synthesis, outperforming flat lexical baselines in recall and achieving strong citation support and semantic edge validation. The study demonstrates MEKG’s effectiveness through controlled benchmarks, diagnostic tests, and human adjudication, positioning it as an auditable structural layer for survey generation.
Hugging Face Trending Papers
Aug 19
Self-prompting and cross-model consensus enable reproducible data extraction from scientific literature with large language models
The paper evaluates browser-based large language models (LLMs) for extracting detailed, contextualized data from scientific papers. It presents four workflows: (1) expert-curated prompts yield good extraction but struggle with nuance; (2) LLMs can generate effective prompts from simple instructions; (3) autonomous literature discovery is challenging, with missing or hallucinated references; (4) LLMs can build new datasets from guidelines that align closely with human experts, yet still need human oversight. The study outlines a practical, auditable workflow where experts set standards, models cross-check extractions, and researchers resolve disputes, enabling scalable scientific data curation.
arXiv AI
Oct 19
Optimist Deep Research: An Iterative, State-Centric Agent for Autonomous Literature Research
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.