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MarsRAG/MarsRAG.tex
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@@ -629,18 +629,6 @@ AreoRAG's effectiveness in multi-source planetary data integration is demonstrat
This case study exemplifies AreoRAG's core advantage: while MultiRAG filters one of the conflicting orbital observations as "unreliable," AreoRAG recognizes this as a scale-dependent conflict between sensors operating at different resolutions, preserves both observations, and generates a scientifically meaningful explanation (spatial mixing effect at different scales). The answer includes provenance metadata (DataIDs) for scientific traceability, and proactively recommends follow-up data to resolve the ambiguity — a capability enabled by the PICT module's conflict-aware context construction.
\subsection{Limitations}
We acknowledge several limitations inherent in the current framework:
1) **Dataset scale**: The planetary datasets are constructed from publicly available archives and may not cover the full diversity of Mars exploration scenarios. Larger-scale evaluation with comprehensive PDS holdings is planned as future work.
2) **Conflict classification coverage**: The four-category conflict taxonomy, while covering the most common planetary science scenarios, may not capture all possible conflict origins (e.g., processing artifact conflicts, calibration drift). Extending the taxonomy is a natural direction.
3) **LLM dependency**: The cross-source interaction entropy computation (Eq. 14) and conflict classification (Eq. 18) both rely on LLM forward passes, introducing potential biases from the base model's parametric knowledge about planetary science. Fine-tuning on domain-specific corpora may mitigate this issue.
4) **Generalization to other planetary bodies**: While designed for Mars, the framework's principles (hyperbolic scale hierarchy, physics-informed conflict triage) are applicable to other planetary bodies (Moon, Venus, icy moons). Validation on non-Mars datasets remains future work.
\section{Related Work}
\subsection{Graph-Structured Retrieval Augmented Generation}