Author

Paul J. Jünger

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Preprint Oct 2026

Retriever-Free Retrieval: Navigating Documents with Decision Models

While language models have become dramatically more capable, retrieval still largely relies on lexical matching or similarity in fixed embedding spaces. This creates a capability asymmetry: what the retriever fails to find, even the strongest generator cannot recover. We propose DecisionRAG, which brings the capabiliti...

Chao Wan, Paul J. Jünger, Bei-Xi Du et al. · 0 citations

#artificial intelligence Jun 2026

Self-Augmenting Retrieval for Diffusion Language Models

This work shows that intermediate states surface salient entities early in the denoising trajectory, making them a useful lookahead signal for retrieval-augmented generation (RAG), and demonstrates that grounding generation in retrieved evidence sharply reduces inter-token dependence, making RAG particularly well suite...

Paul J. Jünger, Justin Lovelace, Lin-Xi Zhao et al. · arXiv.org · 2 citations · ⚡1