# Streaming Hallucination Detection in Long Chain-of-Thought Reasoning

arXiv: https://arxiv.org/abs/2601.02170 · HTML: https://mlsanity.com/paper/2601.02170/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Hallucination detection and mitigation, one of 17 directions in LLM reasoning.
- Builds on 14 of its 50 references that are in the map.
- First posted on arXiv 5 Jan 2026.

## Placed in

- LLM reasoning / [Hallucination detection and mitigation](https://mlsanity.com/lr/hallucination-detection-and-mitigation/)

## Builds on (most cited first, 14 of 14)

- [Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them](https://mlsanity.com/paper/2210.09261/) (2022)
- [The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets](https://mlsanity.com/paper/2310.06824/) (2023)
- [Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study](https://mlsanity.com/paper/2404.10719/) (2024)
- [Reasoning Models Know When They're Right: Probing Hidden States for Self-Verification](https://mlsanity.com/paper/2504.05419/) (2025)
- [Are Reasoning Models More Prone to Hallucination?](https://mlsanity.com/paper/2505.23646/) (2025)
- [Thermometer: Towards Universal Calibration for Large Language Models](https://mlsanity.com/paper/2403.08819/) (2024)
- [Rethinking External Slow-Thinking: From Snowball Errors to Probability of Correct Reasoning](https://mlsanity.com/paper/2501.15602/) (2025)
- [Detection and Mitigation of Hallucination in Large Reasoning Models: A Mechanistic Perspective](https://mlsanity.com/paper/2505.12886/) (2025)
- [Auditing Meta-Cognitive Hallucinations in Reasoning Large Language Models](https://mlsanity.com/paper/2505.13143/) (2025)
- [Probing the Geometry of Truth: Consistency and Generalization of Truth Directions in LLMs Across Logical Transformations and Question Answering Tasks](https://mlsanity.com/paper/2506.00823/) (2025)
- [Explainable Chain-of-Thought Reasoning: An Empirical Analysis on State-Aware Reasoning Dynamics](https://mlsanity.com/paper/2509.00190/) (2025)
- [Temporalizing Confidence: Evaluation of Chain-of-Thought Reasoning with Signal Temporal Logic](https://mlsanity.com/paper/2506.08243/) (2025)
- [Cross-Layer Attention Probing for Fine-Grained Hallucination Detection](https://mlsanity.com/paper/2509.09700/) (2025)
- [PlanU: Large Language Model Reasoning through Planning under Uncertainty](https://mlsanity.com/paper/2510.18442/) (2025)

## Built on by (most cited first, 5 of 5)

- [Monitoring the Internal Monologue: Probe Trajectories Reveal Reasoning Dynamics](https://mlsanity.com/paper/2605.18549/) (2026)
- [The Reasoning Trap: An Information-Theoretic Bound on Closed-System Multi-Step LLM Reasoning](https://mlsanity.com/paper/2605.01704/) (2026)
- [Reasoning Denoiser: Denoising Reasoning Traces for Hallucination Detection in Large Reasoning Models](https://mlsanity.com/paper/2607.22098/) (2026)
- [Tool Specifications Matter: Uncovering and Mitigating Safety Risks in AI Agents](https://mlsanity.com/paper/2607.29254/) (2026)
- [Harnessing Reasoning Trajectories for Hallucination Detection via Answer-agreement Representation Shaping](https://mlsanity.com/paper/2601.17467/) (2026)

## Abstract (arXiv)

Long chain-of-thought (CoT) reasoning improves the performance of large language models, yet hallucinations in such settings often emerge subtly and propagate across reasoning steps. We suggest that hallucination in long CoT reasoning is better understood as an evolving latent state rather than a one-off erroneous event. Accordingly, we treat step-level hallucination judgments as local observations and introduce a cumulative prefix-level hallucination signal that tracks the global evolution of the reasoning state over the entire trajectory. Overall, our approach enables streaming hallucination detection in long CoT reasoning, providing real-time, interpretable evidence.