GradientWarrior

Scientific researchers spend hundreds of hours manually reviewing thousands of medical papers, often missing critical contradictions between studies—such as one paper finding that a drug inhibits a cellular pathway while another claims it activates it under different conditions. Aegis-Edge solves this by acting as an automated evidence extraction and cross-study audit engine. Powered by the Meta Model API (muse-spark-1.3), Aegis-Edge converts unstructured scientific text (PubMed abstracts, PDFs, or manual text feeds) into validated knowledge triples (Subject ➔ Predicate ➔ Object), constructs an interactive knowledge network, automatically detects opposing biological claims, and provides cited, context-aware answers to complex research questions. Key Features & Capabilities 📥 Multi-Modal & Batch Ingestion: Flexible ingestion supporting direct text input, batch PubMed ID fetching via NCBI Entrez E-Utilities, and multi-file research PDF uploads parsed in real-time. 📋 Structured Knowledge Extraction: Extracts precise biomedical relationships with confidence scores, exact verbatim evidence snippets, and finding statuses (SUPPORTED, CONTRADICTORY, CONDITIONAL, UNCERTAIN) backed by strict Pydantic schema validation. ⚠️ Automated Cross-Study Conflict Auditor: Identifies opposing mechanisms across separate studies (e.g., INHIBITS vs. ACTIVATES for the same biological interaction) and flags them with real-time warning cards alongside detailed AI reasoning reports. 🕸️ Interactive Knowledge Graph Network: Visualizes extracted entities and relationships using a dark-mode PyVis network graph, automatically highlighting disputed inter-study relationships in bright red. 💬 Evidence Synthesis & Scientific Q&A: Allows researchers to query the loaded evidence base in natural language, generating context-aware summaries that explicitly cite supporting and conflicting sources (e.g., [study-pd1-alpha], [study-pd1-beta]).