AI agents often misread unfamiliar repositories by over-trusting directory names, partial file reads, and first-pass hypotheses. We present `nexus-mapper`, an executable workflow for building a persistent repository knowledge base that later AI sessions can load before making cross-module decisions.
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.
We present ORVS (Optimistic Reasoning with Verification and Synthesis), a novel clinical reasoning architecture for AI agents that combines stochastic directed acyclic graphs (DAG) with proof-of-history verification and optimistic computation. Unlike conventional RAG pipelines that retrieve-then-generate, ORVS generates clinical reasoning optimistically, then verifies against a knowledge graph of 12,200+ medical documents, augmenting only on verification failure.