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| title: Inkling | |
| emoji: 🌐 | |
| colorFrom: indigo | |
| colorTo: yellow | |
| # python_version: 3.10 | |
| sdk: gradio | |
| sdk_version: 5.29.0 | |
| app_file: app.py | |
| pinned: true | |
| license: agpl-3.0 | |
| short_description: Use AI to find obvious research links in unexpected places. | |
| datasets: | |
| - nomadicsynth/arxiv-dataset-abstract-embeddings | |
| models: | |
| - nomadicsynth/research-compass-arxiv-abstracts-embedding-model | |
| # Inkling: Bridging the Unconnected in Scientific Literature | |
|  | |
| **Inkling** is an experimental bridge-finding engine for scientific literature, built to uncover *latent connections* between research papers—relationships that are obvious in hindsight but buried under the sheer volume of modern research. It’s inspired by the work of **Don R. Swanson**, the visionary who discovered the link between *fish oil* and *Raynaud’s syndrome* using nothing but manual literature analysis. Today, we call this approach **Literature-Based Discovery** - and Inkling is our attempt to automate it with modern NLP. | |
| --- | |
| ## The Problem: Lost in the Literature | |
| The scientific literature is growing exponentially, but human researchers can only read so much. As Sabine Hossenfelder explained in her 2024 YouTube video ["AIs Predict Research Results Without Doing Research"](https://www.youtube.com/watch?v=Qgrl3JSWWDE), even experts miss critical connections because no one has time to read everything. Swanson’s 1986 discovery of the fish oil–Raynaud’s link was a wake-up call: the knowledge existed in plain sight, but the papers were siloed. Inkling is our attempt to fix that. | |
| --- | |
| ## The Vision: A Bridge-Finding Machine | |
| Inkling isn’t just a search engine. It’s a **hypothesis generator**. It learns to recognize *intermediate concepts* that connect seemingly unrelated papers—like Swanson’s "blood viscosity" bridge. The model is built to: | |
| - **Find indirect links** between papers that don’t cite each other. | |
| - **Surface connections** that feel obvious once explained but are buried in the noise. | |
| - **Scale** to the entire arXiv corpus and beyond. | |
| --- | |
| ## How It Works | |
| ### Model Architecture | |
| - **Base Model**: A `SentenceTransformer` using **Llama-7B** as its base (with frozen weights) and a dense embedding head. | |
| - **Training**: | |
| - v1: Trained on a synthetic dataset of randomly paired papers, rated for conceptual overlap. | |
| - v2 (in progress): Focused on *bridge detection*, using prompts to explicitly identify intermediate concepts (e.g., "What connects these two papers?"). | |
| - **Embedding Strategy**: | |
| - Dense vector representations of abstracts. | |
| - FAISS for fast approximate nearest-neighbor search. | |
| ### Dataset Philosophy | |
| - v1: Random paper pairs rated for generic "relevance" (too broad, limited bridge detection). | |
| - v2: Focus on **explicit bridge extraction** using LLM-generated triplets (e.g., "Paper A → Bridge Concept → Paper B"). | |
| --- | |
| ## The Inspiration | |
| This project was born from a **nerd-sniping moment** after watching Sabine Hossenfelder’s video on AI’s ability to predict neuroscience results without experiments. That led to three key influences: | |
| ### 1. **Swanson’s "Undiscovered Public Knowledge"** | |
| Swanson’s 1986 paper showed that the fish oil–Raynaud’s link existed in the literature for decades—it just took a human to connect the dots. Inkling automates this process. | |
| ### 2. **Tshitoyan et al. (2019): Word Embeddings in Materials Science** | |
| Their work demonstrated that unsupervised embeddings could predict future material discoveries from latent knowledge. Inkling applies this idea to *conceptual bridges* in all scientific fields. | |
| ### 3. **Luo et al. (2024): LLMs Beat Human Experts** | |
| This study showed that a 7B LLM (like Mistral) could outperform neuroscientists in predicting experimental outcomes. Inkling leverages this power to find connections even domain experts might miss. | |
| --- | |
| ## What It Can Do (and What’s Next) | |
| ### Current Capabilities | |
| - Embed arXiv abstracts into dense vectors. | |
| - Search for papers with conceptual overlap (50% relevance in top-10/25 queries, per manual testing). | |
| - Visualize results in a Gradio interface with FAISS-powered speed. | |
| ### Roadmap | |
| - **v2**: Train on LLM-generated bridge triplets (e.g., "Paper A → Blood Viscosity → Paper B"). | |
| - **Gradio Enhancements**: | |
| - Interactive bridge visualization (D3.js or Plotly). | |
| - User feedback loop for improving the model. | |
| - **Automated Updates**: Embed new arXiv papers nightly. | |
| - **Domain-Specific Tools**: | |
| - Drug repurposing mode (e.g., "Find new uses for aspirin"). | |
| - Interdisciplinary connection finder (e.g., "How does physics inform AI research?"). | |
| --- | |
| ## Why This Matters | |
| Inkling is **not** a polished product—it’s a chaotic, ADHD-fueled experiment in democratizing scientific discovery. It’s for: | |
| - Researchers drowning in paper overload. | |
| - Interdisciplinary thinkers who thrive on unexpected connections. | |
| - Anyone who’s ever thought, *"I could’ve thought of that!"* after a breakthrough. | |
| As Sabine Hossenfelder put it: *"The future of research isn’t in doing more experiments—it’s in connecting the dots we already have."* - Citation needed. | |
| --- | |
| ## Status | |
| - **Model**: v1 (proof of concept, 50-50 if it does anything or my brain is just playing tricks). | |
| - **Dataset**: v1 (random pairs, too broad). v2 (in planning, focused on bridge detection). | |
| - **Interface**: Gradio-powered demo with FAISS backend. | |
| - **Next Steps**: Refine training data, automate updates, and scale to all of arXiv. | |
| --- | |
| ## Credits | |
| - **Inspiration**: Sabine Hossenfelder’s ["AIs Predict Research Results" video](https://www.youtube.com/watch?v=Qgrl3JSWWDE). | |
| - **Foundational Work**: Don R. Swanson, V. Tshitoyan, X. Luo. | |
| - **Model Architecture**: Llama-7B + SentenceTransformer. | |
| --- | |
| ## Try It | |
| [**Live Demo**](https://nomadicsynth-research-compass.hf.space) | |
| *Paste an abstract, find a bridge, and see if the connection feels obvious in hindsight.* 🚀 | |
| --- | |
| **This is a work in progress. Feedback, ideas, and nerd-sniped collaborators are welcome.** | |