Fantastic Scientific Agents and How to Build Them: AgentBuild for Rietveld Refinement
Quick Answer
AgentBuild introduces a structured approach to building scientific agents for Rietveld refinement, utilizing a contract authored by scientists.
Quick Take
AgentBuild introduces a structured approach to building scientific agents for Rietveld refinement, utilizing a contract authored by scientists. This method incorporates a rubric-driven judge and meta-optimizer, enabling efficient agent construction while preserving scientific judgment, particularly in X-ray diffraction data analysis with GSAS-II.
Key Points
- AgentBuild treats agent construction as a workflow stage, enhancing scientific rigor.
- Utilizes a version-controlled rubric and curated knowledge base for agent development.
- Demonstrated with lithium lanthanum zirconium oxide (LLZO) in X-ray diffraction analysis.
- Re-running AgentBuild allows for re-tuning without complete rebuilds, preserving contracts.
- The approach highlights workflow limits and distinguishes between contract and pattern-fitting failures.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 12834v1 Announce Type: new Abstract: As scientific workflows shift from deterministic executables to LLM-based agents, the development practices on offer, such as fine-tuning, reinforcement learning, and prompt-and-go, bury the scientist's judgment. We propose treating agent construction as a workflow stage and introduce AgentBuild, which builds a scientific agent from a contract the scientist authors.
The contract is a version-controlled rubric, a difficulty-graded curriculum, and a curated external knowledge base. A rubric-driven judge gates a meta-optimizer coding agent that edits the agent within a declared boundary, so the build compiles the agent, not the scientist's judgment.
We instantiate this for Rietveld refinement of X-ray diffraction data through GSAS-II behind and A2A, where a blank-harness construction run progresses through a lithium lanthanum zirconium oxide (LLZO) signal-to-noise ladder, reaches the 4 hour scan as a frontier case, and exposes the workflow-scope limits that remain. The same rubric that rewards credible fits also scores trajectory scope, making the frontier a contract failure rather than a pattern-fitting failure.
As base models evolve, re-running AgentBuild is a re-tune, not a rebuild, and the scientist's authored contract remains the durable asset.
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