From Weak Task Specifications to Scientific Extraction Agents: Optimizing Task Construction

Abstract:Most methods that optimize LLM prompts and agent workflows assume that task-specific output schemas, extraction instructions, and evaluation criteria are predefined. For scientific extraction agents, however, a short task goal may not fully determine these components, while specifying them manually is costly. We study the upstream problem of constructing the task-specific configuration from a weak specification containing only a short goal and unannotated reference documents. Rather than treating automatic construction as a fixed preprocessing step, our framework constructs a task-specific schema, extraction instructions, and base training rubrics, then keeps schema construction and extraction instructions editable during optimization. Failure-focused updates concentrate textual-gradient feedback on lower-scoring documents, while training-time evaluation criteria adapt to recurring failures. On a heterogeneous-catalysis literature corpus, automatic construction remains improvable, and optimizing both schema construction and extraction instructions performs best across all four judge-rubric settings, with ablations and blinded human evaluation supporting the proposed formulation.

Computation and Language (cs.CL)

arXiv:2609.34829 [cs.CL]

(or arXiv:2609.34829v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.34829 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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