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Evaluation-driven Scaling for Scientific Discovery (Apr 2026)

Title: Evaluation-driven Scaling for Scientific Discovery (Apr 2026)
Link: http://arxiv.org/abs/2604.19341v1
Date: April 2026

Summary:
This paper introduces Simple Test-time Evaluation-driven Scaling (SIMPLETES), a framework that scales evaluation-driven discovery loops for LLMs. By strategically combining parallel exploration, feedback-driven refinement, and local selection, SIMPLETES discovers state-of-the-art solutions to 21 scientific problems across six domains, including quantum circuit compilation and GPU kernel optimization, while demonstrating generalizable discovery behaviors through trajectory-level post-training.

Key Topics:
- Scientific Discovery
- Large Language Models (LLMs)
- Test-time Scaling
- Evaluation-driven Loops
- Quantum Circuit Compilation
- GPU Kernel Optimization
- Algorithm Engineering
- Combinatorial Construction

Chapters:
00:00 - Introducing SIMPLETES Framework
01:34 - Scaling the Discovery Pillars
03:33 - Implementing Context Compression
05:04 - Optimizing Hardware Kernels
06:41 - Refining Quantum Circuit Gates
07:44 - Solving Complex Math Problems
09:44 - Predicting Hyperparameter Grids
10:46 - Balancing Depth and Width
12:14 - Mastering Meta-discovery Skills
12:57 - Avoiding Evaluator Reward Hacking
14:45 - Summary and Final Thoughts

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