arXiv (All)
open access
Beyond Benchmark Accuracy: A Diagnostic Test for LLM Chemical Prediction
LLMs have achieved strong performance on chemical prediction benchmarks. However, existing evaluation methods typically provide target entity identity information directly, making it difficult to determine whether model predictions arise from transferable structure-property reasoning capabilities or
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- The Artificial Intelligence Explanatory Trade-Off on the Logic of Discovery in Chemistry#94938
- MolLean: A Process-Verifiable Language for Chemical Reasoning#1355380
- Empowering federated learning for robust compound-protein interaction prediction across heterogeneous cross-pharma domains#625648
- Empowering federated learning for robust compound-protein interaction prediction across heterogeneous cross-pharma domains#625625
- Harnessing Surrogate Models for Data-efficient Predictive Chemistry: Descriptors vs. Learned Hidden Representations#1373479
Record · ID 1354588
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive