Aster: Autonomous Scientific Discovery over 20x Faster Than Existing Methods

Key points

• Problem: The author argues that existing autonomous discovery frameworks, which use LLMs to improve code iteratively, often need hundreds to thousands of iterations per discovery, which restricts them to problems with short evaluations. For tasks with long evaluations, such as multi-hour model training, running thousands of iterations becomes prohibitive.
• Input and output: This is a system paper that introduces Aster, an AI agent available through a web interface and API at asterlab.ai (the author is at Aster AI Labs), and reports its results on several tasks. The user provides a task prompt, an initial program and an evaluation script, and Aster returns an improved program. The appendix lists the programs Aster produced (the overlap optimizer, the single-cell denoiser, the TriMul (Triangular Matrix Multiplication) kernel, the NanoGPT diff and the ZAPBench (Zebrafish Activity Prediction Benchmark) model).
• Method: The loop described in the paper has an LLM write a new program from the existing program and the prompt, an evaluator score it, and a database store the result, then repeat. Aster takes the same inputs as existing frameworks (initial program, evaluator, prompt) and aims to cut the number of iterations needed so that evaluations lasting hours become tractable. The paper describes the method only at the overview level of Figure 2.
• Implementation: The paper states model settings for two circle packing runs: the speed comparison with OpenEvolve used the same model mix (80% Gemini 2.0 Flash, 20% Claude 3.7 Sonnet), and the run that reached the known best used Aster's default model configuration, whose models are not named. Many evaluation scripts and prompts were adapted from the repository of TTT-Discover (the work behind the previous AI records in Table 1), TriMul ran on an NVIDIA H100, and the ZAPBench evaluation ran on an NVIDIA T4 GPU. The access route the paper gives is the web interface and API at asterlab.ai.
• Results: On packing 26 circles, Aster with the same model mix passed the 2.634 that OpenEvolve reached after 115 iterations with 2.6353 in 5 iterations, and Aster with its default configuration hit the known best of 2.635983 in 6 iterations. It reached 0.380874 on the Erdős minimum overlap problem in 40 iterations (TTT-Discover 0.380876), 0.711 on single-cell denoising in 30 iterations (TTT-Discover 0.709) and 1114 µs on the TriMul kernel in 70 iterations (TTT-Discover 1161 µs), and it cut the NanoGPT Speedrun record from 96.8 to 95.2 seconds in 8 iterations. On ZAPBench it matched the best human model's MAE (mean average error) of 0.0182 after 34 iterations, which the author reports as 190 times less compute.
• Limits: The speed comparison covers one task, circle packing, against one framework, OpenEvolve, whose 460 attempts generated 4 at a time were counted as 115 iterations. On ZAPBench Aster only matched the best human result, and the paper notes that the tree search system of Aygün et al. reached an MAE of 0.0176 under similar runtime constraints. The 70 TriMul iterations started from the best program of an earlier 94-iteration run on a different GPU image.

Paper links

External research summaries. These are not HDATF publications or measured product results.