Hallucination Across the Reasoning Lifecycle: Interface Visibility, Causal Evidence, and Release Control in Large Reasoning Models
On the map: LLM reasoning → Hallucination detection and mitigation
Abstract (arXiv)
Reasoning errors can propagate into later decisions and memory. This survey synthesizes 312 papers and first-party reports on text-based reasoning hallucinations around three questions: what evidence is observable, what study designs establish, and which corrective actions the evidence supports. UIPCA records unsupported premises (U), invalid inferences (I), dependent reuse (P), visible answer-trace consistency (C), and action-policy failures (A). Across 58 reviewed sources, no comparison establishes that a specified intervention improves reasoning while reducing factual reliability under matched conditions. The synthesis connects diagnosis to verification, repair, selective release, and persistent-state control across memory, tools, and training feedback.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Hallucination detection and mitigation, one of 17 directions in LLM reasoning. First posted on arXiv 4 Oct 2026.
Builds on
No reference list yet.
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2025Detection and Mitigation of Hallucination in Large Reasoning Models: A Mechanistic Perspective
- 2025Auditing Meta-Cognitive Hallucinations in Reasoning Large Language Models
- 2026REIN: Bridging the Gap between Reasoning and Reliability via Reflection and Abstention Alignment
- 2025Are Reasoning Models More Prone to Hallucination?
- 2025More Thinking, Less Seeing? Assessing Amplified Hallucination in Multimodal Reasoning Models
- 2026MM-THEBench: Do Reasoning MLLMs Think Reasonably?
- 2025Joint Evaluation of Answer and Reasoning Consistency for Hallucination Detection in Large Reasoning Models
- 2025The Reasoning Trap: How Enhancing LLM Reasoning Amplifies Tool Hallucination