# Accelerating Scientific Research with Gemini: Case Studies and Common Techniques

arXiv: https://arxiv.org/abs/2602.03837 · HTML: https://mlsanity.com/paper/2602.03837/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2026.

## Built on by (most cited first, 29 of 29)

- [Advancing Mathematics Research with AI-Driven Formal Proof Search](https://mlsanity.com/paper/2605.22763/) (2026)
- [AI co-mathematician: Accelerating mathematicians with agentic AI](https://mlsanity.com/paper/2605.06651/) (2026)
- [Measuring Progress in Reasoning Toward Mathematical Discovery with Automatic Verification](https://mlsanity.com/paper/2603.15617/) (2026)
- [Scaling Laws for Mixture Pretraining Under Data Constraints](https://mlsanity.com/paper/2605.12715/) (2026)
- [Agentic AI -- Physicist Collaboration in Experimental Particle Physics: A Proof-of-Concept Measurement with LEP Open Data](https://mlsanity.com/paper/2603.05735/) (2026)
- [Beyond the Library: An Agentic Framework for Autoformalizing Research Mathematics](https://mlsanity.com/paper/2606.31134/) (2026)
- [CausalEvolve: Towards Open-Ended Discovery with Causal Scratchpad](https://mlsanity.com/paper/2603.14575/) (2026)
- [SPIRAL: Learning to Search and Aggregate](https://mlsanity.com/paper/2606.23595/) (2026)
- [Decoding as Optimisation on the Probability Simplex: From Top-K to Top-P (Nucleus) to Best-of-K Samplers](https://mlsanity.com/paper/2602.18292/) (2026)
- [COMIC: Agentic Sketch Comedy Generation](https://mlsanity.com/paper/2603.11048/) (2026)
- [Polynomial-Time Algorithm for Thiele Voting Rules with Voter Interval Preferences](https://mlsanity.com/paper/2604.05953/) (2026)
- [Accelerating Scientific Research with Gemini in the Real-World](https://mlsanity.com/paper/2608.26701/) (2026)
- [Perfect score on IPhO 2025 theory by Gemini agent](https://mlsanity.com/paper/2603.03352/) (2026)
- [Early Discoveries of Algorithmist I: Promise of Provable Algorithm Synthesis at Scale](https://mlsanity.com/paper/2603.22363/) (2026)
- [Prompt-to-Paper: Agentic AI System for Bioinformatics](https://mlsanity.com/paper/2607.05456/) (2026)
- [From Solvers to Research: Large Language Model-Driven Formal Mathematics at the Research Frontier](https://mlsanity.com/paper/2607.07779/) (2026)
- [Stellar Colosseum: A Many-Agent Harness for Long-Horizon Research in Mathematics and Theoretical Computer Science](https://mlsanity.com/paper/2609.15983/) (2026)
- [Early Evidence of Vibe-Proving with Consumer LLMs: A Case Study on Spectral Region Characterization with ChatGPT-5.2 (Thinking)](https://mlsanity.com/paper/2602.18918/) (2026)
- [Efficient Reasoning on the Edge](https://mlsanity.com/paper/2603.16867/) (2026)
- [Grokability in five inequalities](https://mlsanity.com/paper/2605.05193/) (2026)
- [Agentic Discovery of Exchange-Correlation Density Functionals](https://mlsanity.com/paper/2605.05460/) (2026)
- [When Does Critique Improve AI-Assisted Theoretical Physics? SCALAR: Structured Critic--Actor Loop for Agentic Reasoning](https://mlsanity.com/paper/2605.06772/) (2026)
- [How AI settled the complexity of the oldest SGD algorithm](https://mlsanity.com/paper/2606.29593/) (2026)
- [Position: AI Agents in Scientific Teams Should Be Studied as Human-Agent Systems](https://mlsanity.com/paper/2608.14667/) (2026)
- [A Sharp Barrier for Consistent Submodular Maximization: Any Improvement over 2-√2 Entails Exponential Queries or Linear Recourse](https://mlsanity.com/paper/2609.09986/) (2026)
- [Can AI Scientists Change Their Minds? Prior-Evidence Conflict in Synthetic Universes](https://mlsanity.com/paper/2609.36726/) (2026)
- [Initialization Improves LLM-Driven Discovery](https://mlsanity.com/paper/2610.00707/) (2026)
- [Bolzano: From Expert-Guided Proof Search to Automated Open-Problem Solving](https://mlsanity.com/paper/2610.09769/) (2026)
- [One-Sided Verification Under Limited Expert Review](https://mlsanity.com/paper/2610.18089/) (2026)

## Abstract (arXiv)

Recent advances in large language models (LLMs) have opened new avenues for accelerating scientific research. While models are increasingly capable of assisting with routine tasks, their ability to contribute to novel, expert-level mathematical discovery is less understood. We present a collection of case studies demonstrating how researchers have successfully collaborated with advanced AI models, specifically Google's Gemini-based models (in particular Gemini Deep Think and its advanced variants), to solve open problems, refute conjectures, and generate new proofs across diverse areas in theoretical computer science, as well as other areas such as economics, optimization, and physics. Based on these experiences, we extract common techniques for effective human-AI collaboration in theoretical research, such as iterative refinement, problem decomposition, and cross-disciplinary knowledge transfer. While the majority of our results stem from this interactive, conversational methodology, we also highlight specific instances that push beyond standard chat interfaces. These include deploying the model as a rigorous adversarial reviewer to detect subtle flaws in existing proofs, and embedding it within a "neuro-symbolic" loop that autonomously writes and executes code to verify complex derivations. Together, these examples highlight the potential of AI not just as a tool for automation, but as a versatile, genuine partner in the creative process of scientific discovery.