EvoMaster: A Foundational Evolving Agent Framework for Agentic Science at Scale

Key points

• Problem: Scientific agents rebuild model access, tool orchestration, context management, trajectories and error recovery for each domain, which limits reuse across disciplines; the authors call this loop fragmentation. Evidence, rejected hypotheses and failure experience stay confined to one run or research campaign (loop discontinuity), and unfiltered memory can retain noise alongside useful experience.
• Input and output: It takes a scientific problem defined by a research question, a space of mutable research artifacts, executable tools or environments, an external evaluation protocol and budget and stopping settings. It returns artifacts such as code, model checkpoints, mathematical constructions or data-analysis reports plus a final answer, and keeps each run's configuration, trajectory and workspace artifacts as a persistent laboratory notebook. This is a framework paper that also reports agents built on the framework tested on 10 benchmarks.
• Method: The Execution loop repeats Reason, Act and Observe within a run, and the Exploration loop forms and executes hypotheses within a study, using external evaluator evidence to retain, reject or revise candidates. The Evolution loop distills a finished study record into reusable knowledge, skills, prompts, memories or workflow components, and an Admit policy (an autonomous evidence rule, an evaluator gate or a human decision) decides whether they enter the capability store for later studies. When the context fills up, the system message prefix and recent turns are kept verbatim and older dialog is summarized, while workspace files, evaluator outputs and tool records stay outside this compression.
• Implementation: Three software layers (Playground, Exp, Agent) and registries make models, tools, skills and agents independently replaceable, and MCP (Model Context Protocol) servers attach through the same tool interface. In the experiments EvoMaster ran X-Master and X-Master 2.0 on HLE (Humanity's Last Exam) and Frontier Science, ML-Master 2.0 on MLE-Bench Lite (the 22 low-complexity Kaggle competitions of MLE-Bench), Browse-Master on BrowseComp and BrowseComp-ZH (a Chinese web browsing benchmark), PhysMaster on PRL-Bench (a physics research benchmark built from Physical Review Letters papers), and its minimal agent on the remaining 4 benchmarks. Per the official README, the GitHub repository holds the framework code (evomaster/), agent examples (playground/) and configs (configs/), and among SciMaster agents ML-Master 2.0, ML-Master, X-Master and Browse-Master are marked Available while PhysMaster and EmboMaster are marked Coming Soon.
• Results: With every agent on GPT-5.4 (medium reasoning effort) under the same benchmark-specific time limits, scoring scripts and task splits, EvoMaster scored highest on 9 of 10 benchmarks, all except PostTrainBench (Codex 15.92%, EvoMaster 13.83%), and against the highest-scoring other agent on each benchmark it gained 41.75 points on BrowseComp and 34.85 points on MLE-Bench Lite. Mean scores over the 10 benchmarks were 58.02% for EvoMaster, 40.29% for Codex, 39.84% for OpenHands and 32.39% for OpenClaw, and EvoMaster's mean per-benchmark LLM inference cost was $9.56, 35.6% below Codex's $14.85. Keeping tools, prompts, orchestration and benchmark protocols fixed and replacing only the backend gave mean scores of 63.65% with GLM-5.2 max (mean cost $14.00) and 55.66% with DeepSeek-V4-Pro max (34.4% of the GPT-5.4 mean cost).
• Limits: The authors state that EvoMaster is primarily optimized for in silico and computational research workflows, and they leave direct manipulation of physical apparatus such as cloud laboratories or robotic synthesis hardware to future work. The cost comparison counts LLM inference only, and because means are sensitive to costly benchmarks such as Combinatorial Construction, the median cost of EvoMaster ($4.57) is higher than that of Codex ($3.00), OpenHands ($2.78) and OpenClaw ($1.43). EvoMaster used SciMaster agents tailored to each benchmark or its minimal agent with only minor input and output changes, against three general-purpose agents, and the authors say the benchmark suite primarily demonstrates breadth across workflows.

Paper links

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