Entropic Claim Resolution: Uncertainty-Driven Evidence Selection for RAG
Summary: arXiv:2603.28444v1 Announce Type: new
Abstract
Current Retrieval-Augmented Generation (RAG) systems predominantly rely on relevance-based dense retrieval, sequentially fetching documents to maximize semantic similarity with the query. However, in knowledge-intensive and real-world scenarios characterized by conflicting evidence or fundamental query ambiguity, relevance alone is insufficient for resolving epistemic uncertainty. We introduce Entropic Claim Resolution (ECR), a novel inference-time algorithm that reframes RAG reasoning as entropy minimization over competing semantic answer hypotheses.
Unlike action-driven agentic frameworks (e.g., ReAct) or fixed-pipeline RAG architectures, ECR sequentially selects atomic evidence claims by maximizing Expected Entropy Reduction (EER), a decision-theoretic criterion for the value of information. The process dynamically terminates when the system reaches a mathematically defined state of epistemic sufficiency (H).
Key Features of Entropic Claim Resolution
- Entropy Minimization: ECR transforms the RAG paradigm by focusing on reducing uncertainty rather than merely increasing relevance. This approach better addresses situations where multiple interpretations or conflicting evidence exist.
- Expected Entropy Reduction (EER): This criterion allows the system to evaluate the potential value of new information, enabling it to make informed decisions about which evidence to pursue next.
- Dynamic Termination: ECR does not rely on a pre-defined set of documents or evidence. Instead, it adapts its selection process based on the evolving state of knowledge, stopping when sufficient certainty is achieved.
Advantages of ECR Over Traditional RAG Systems
- Improved Accuracy: By focusing on epistemic sufficiency, ECR can yield more accurate results in complex scenarios where traditional methods might struggle.
- Flexibility: The ability to dynamically adjust evidence selection allows ECR to be more responsive to the specific nature of a query, which can vary significantly across different domains.
- Reduction of Conflicting Evidence: ECR’s methodology helps clarify ambiguous situations by systematically evaluating and resolving competing hypotheses.
Conclusion
Entropic Claim Resolution represents a significant advancement in the field of Retrieval-Augmented Generation. By incorporating principles of entropy minimization and expected value of information, ECR addresses key limitations of existing systems that rely solely on relevance-based approaches. This novel framework not only enhances the accuracy and reliability of information retrieval but also paves the way for more sophisticated AI systems capable of navigating the complexities of real-world knowledge.
As RAG technologies continue to evolve, the introduction of ECR could redefine how AI systems approach knowledge-intensive tasks, ultimately leading to more informed and nuanced outputs.