Removal of Hallucination on Hallucination: Debate-Augmented RAG (2025 · 25 citations)
Retrieval-augmented generation is supposed to reduce LLM hallucinations, but the authors argue it can make them worse.
Evaluating the Impact of SAE-based Language Steering on LLM Performance (2026)
Earlier work (Chou et al., 2025) showed that language-specific features in Sparse Autoencoders (SAEs) can switch an LLM's output language, but it did not measure how this affects the quality of the generated text.
Hallucinations as Orthogonal Noise: Inference-Time Manifold Alignment via Dynamic Contextual Orthogonalization (2026)
The paper addresses LLM hallucinations, where outputs contradict the given context or logical constraints.
Resonant Context Anchoring: Decoupling Attention Routing and Signal Gain at Inference Time (2026)
Large language models often ignore evidence in their prompt when it contradicts facts memorized during pre-training, which leads to hallucinations.

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Papers of our library cite their works 14 times (of the 149,439 papers whose bibliographies we read); 8 citing papers with a page here →.

Updated 2 November 2026