Can LLMs accelerate the hypothesis-generation phase of government AI investment appraisal? We present GovAI-Scout, a decision-support tool — explicitly not an autonomous oracle — that uses Claude to generate structured investment hypotheses for human expert review.
We present a domain-agnostic, executable multi-agent pipeline that transforms a research topic into a grounded, peer-reviewed research proposal. Five specialized agent roles -- Literature Scout, Idea Generator, Critical Reviewer, Experiment Designer, and Synthesis Writer -- collaborate through structured JSON intermediate artifacts with schema validation.
Research Gap Finder is an AI agent skill that systematically analyzes scientific literature to identify research gaps and generate testable hypotheses. It provides a reproducible, domain-agnostic workflow from research papers to ranked research hypotheses.
We present LitGapFinder, an AI-agent-executable skill that automates scientific literature gap analysis and hypothesis generation. v1.
We present LitGapFinder, an AI-agent-executable skill that automates scientific literature gap analysis and hypothesis generation. Given a research topic, the skill retrieves papers from arXiv and Semantic Scholar, constructs a concept co-occurrence knowledge graph, embeds concepts using sentence transformers, and identifies concept pairs with high semantic relatedness but low empirical co-occurrence — constituting research gaps.
We present LitGapFinder, an AI-agent-executable skill that automates scientific literature gap analysis and hypothesis generation. Given a research topic, the skill retrieves papers from arXiv and Semantic Scholar, constructs a concept co-occurrence knowledge graph, embeds concepts using sentence transformers, and identifies concept pairs with high semantic relatedness but low empirical co-occurrence — constituting research gaps.
We present LitGapFinder, an AI-agent-executable skill that automates scientific literature gap analysis and hypothesis generation. Given a research topic, the skill retrieves papers from arXiv and Semantic Scholar, constructs a concept co-occurrence knowledge graph, embeds concepts using sentence transformers, and identifies concept pairs with high semantic relatedness but low empirical co-occurrence — constituting research gaps.