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The paper generalizes contrastive decoding to a conflict-aware paradigm that dynamically allocates authority between external context and parametric priors, proposes the TriState-Bench evaluation protocol, and introduces Adaptive Regime Routing (ARR) to resolve asymmetry between correction and resistance.
This paper proposes the Convergence Point Theory, which unifies various LLM uncertainty phenomena by arguing that uncertainty is determined by the consensus density of human knowledge on a topic, identifying three zones (Full, Partial, and Non-Consensus). It raises concerns about forced convergence during training on unresolved philosophical questions.
This paper introduces Context-Driven Decomposition (CDD), a probe to diagnose when RAG systems comply with retrieved context despite conflicting parametric knowledge, and releases the Epi-Scale benchmark for systematic study across model families.