| title: RECON | |
| emoji: 🔍 | |
| colorFrom: blue | |
| colorTo: indigo | |
| sdk: gradio | |
| sdk_version: 6.10.0 | |
| app_file: app.py | |
| pinned: true | |
| license: mit | |
| short_description: Multi-agent ML literature research with staleness detection | |
| > For full documentation, architecture details, and eval results: [GITHUB_README.md](./GITHUB_README.md) | |
| # RECON — Temporally-Aware Scientific Retrieval | |
| A multi-agent RAG system that detects when retrieved scientific evidence has been superseded by newer work. | |
| **Try it:** Enter any research question. RECON retrieves papers from Semantic Scholar and OpenAlex, scores their reliability using a three-signal formula (citation centrality + recency + content coherence), and flags stale or contradicted evidence before synthesizing an answer. | |
| Each paper in the results shows a reliability label: FOUNDATIONAL, CURRENT, DECLINING, or SUPERSEDED. | |
| **Evaluation:** 44% staleness catch rate on a 130-question benchmark of real scientific supersession chains. Single-pass RAG baseline: 0%. | |
| --- | |
| Built by Mukul Ray | [GitHub](https://github.com/MukulRay1603/project-recon) | |