# ShadowMiner v1 - An Experience Report on Implementing and Measuring a Problem-and-Hypothesis Discovery Engine

arXiv: https://arxiv.org/abs/2610.04339 · HTML: https://mlsanity.com/paper/2610.04339/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 34 of its 44 references that are in the map.
- First posted on arXiv 3 Oct 2026.

## Builds on (most cited first, 30 of 34)

- [The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery](https://mlsanity.com/paper/2408.06292/) (2024)
- [Accelerating scientific discovery with Co-Scientist](https://mlsanity.com/paper/2502.18864/) (2025)
- [ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models](https://mlsanity.com/paper/2404.07738/) (2024)
- [SciMON: Scientific Inspiration Machines Optimized for Novelty](https://mlsanity.com/paper/2305.14259/) (2023)
- [SciREX: A Challenge Dataset for Document-Level Information Extraction](https://mlsanity.com/paper/2005.00512/) (2020)
- [Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents](https://mlsanity.com/paper/2410.13185/) (2024)
- [SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning](https://mlsanity.com/paper/2409.05556/) (2024)
- [Hypothesis Generation with Large Language Models](https://mlsanity.com/paper/2404.04326/) (2024)
- [Large Language Models can Learn Rules](https://mlsanity.com/paper/2310.07064/) (2023)
- [Nova: An Iterative Planning and Search Approach to Enhance Novelty and Diversity of LLM Generated Ideas](https://mlsanity.com/paper/2410.14255/) (2024)
- [Dated Data: Tracing Knowledge Cutoffs in Large Language Models](https://mlsanity.com/paper/2403.12958/) (2024)
- [AI Idea Bench 2025: AI Research Idea Generation Benchmark](https://mlsanity.com/paper/2504.14191/) (2025)
- [TDMSci: A Specialized Corpus for Scientific Literature Entity Tagging of Tasks Datasets and Metrics](https://mlsanity.com/paper/2101.10273/) (2021)
- [Towards Execution-Grounded Automated AI Research](https://mlsanity.com/paper/2601.14525/) (2026)
- [AutoSOTA: An End-to-End Automated Research System for State-of-the-Art AI Model Discovery](https://mlsanity.com/paper/2604.05550/) (2026)
- [HindSight: Evaluating LLM-Generated Research Ideas via Future Impact](https://mlsanity.com/paper/2603.15164/) (2026)
- [Is this Idea Novel? An Automated Benchmark for Judgment of Research Ideas](https://mlsanity.com/paper/2603.10303/) (2026)
- [Autonomous Research Agents: A Survey of AI Scientists and the Verification Gap](https://mlsanity.com/paper/2608.05179/) (2026)
- [Training AI Scientists to Replicate Research](https://mlsanity.com/paper/2608.13331/) (2026)
- [Graph-Native Reinforcement Learning Enables Traceable Scientific Hypothesis Generation through Conceptual Recombination](https://mlsanity.com/paper/2607.00924/) (2026)
- [Examining and Addressing Barriers to Diversity in LLM-Generated Ideas](https://mlsanity.com/paper/2602.20408/) (2026)
- [EvoSci: A Bio-Inspired Multi-Agent Framework for the Evolution of Scientific Discovery](https://mlsanity.com/paper/2605.24018/) (2026)
- [Towards Diverse Scientific Hypothesis Search with Large Language Models](https://mlsanity.com/paper/2606.10587/) (2026)
- [Agentic-Ideation: Sample Efficient Agentic Trajectories Synthesis for Scientific Ideation Agents](https://mlsanity.com/paper/2606.31229/) (2026)
- [Scaling Scientific Discovery Environments for Turn-Level Agentic RL](https://mlsanity.com/paper/2607.28990/) (2026)
- [Multi-Persona Debate System for Automated Scientific Hypothesis Generation](https://mlsanity.com/paper/2605.23917/) (2026)
- [VERITAS: Towards a General-Purpose Replication Tool for Scientific Research](https://mlsanity.com/paper/2607.02931/) (2026)
- [What Proves You Wrong: Benchmarking Language Models on Falsifiable Research Ideation](https://mlsanity.com/paper/2608.22948/) (2026)
- [HypoForge: A Self-Improving Multi-Agent Framework for Automated Hypothesis Generation and Testing via Scientific Skill Learning](https://mlsanity.com/paper/2608.25770/) (2026)
- [ResearchEVO: An End-to-End Framework for Automated Scientific Discovery and Documentation](https://mlsanity.com/paper/2604.05587/) (2026)

## Abstract (arXiv)

ShadowMiner v1 is a system that automatically discovers research problems and generates hypotheses from AI papers. It is a nine-stage pipeline. It structures documents into a knowledge graph and finds graph gaps in it - structural blind spots in research. These graph gaps are included in the LLM generation prompt. Each generated hypothesis is then verified by checking whether it is already covered by existing research, scoring its quality, and checking that the facts it relies on are accurately drawn from its sources. This report does not propose a new generation or evaluation technique. It describes our experience of implementing and applying ideas from prior work, and measuring whether each one actually contributed.