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cs.CL2026
Rewarding Efficient Reasoning Improves Abstention on Underspecified Tasks in Reasoning Models
Polina Tsvilodub, Max Höth, Michael Franke +2
While modern large reasoning models (LRMs) excel at providing correct answers in many tasks, we provide additional evidence that they often struggle with a critical capability: kno…
cs.CL2025
Bounding Hallucinations: Merlin-Arthur Protocols for Mutual-Information Bounds in Language Models
Björn Deiseroth, Max Henning Höth, Kristian Kersting +1
Retrieval-augmented generation (RAG) relies on retrieved context to guide large language models (LLM), yet treats the retrieval as a heuristic rather than verifiable evidence -- le…