AI Research: Hallucination & Safety Papers (Nov 2025)

Stay up-to-date with the latest advancements in Artificial Intelligence with our roundup of the top research papers published on November 27, 2025. This article, inspired by the DailyArxiv project, focuses on two critical areas: hallucination in AI models and AI safety. We'll delve into the key findings and insights from these papers, providing you with a concise overview of the cutting-edge research in these fields.

Hallucination in AI

Hallucination in AI, particularly in large language models (LLMs), refers to the phenomenon where the model generates outputs that are nonsensical, factually incorrect, or not grounded in the input data. This is a significant challenge as it can lead to unreliable and misleading outputs, hindering the deployment of AI systems in critical applications. Researchers are actively exploring various techniques to mitigate hallucinations and improve the reliability of AI models.

Tell Model Where to Look: Mitigating Hallucinations in MLLMs by Vision-Guided Attention

This paper, published on November 25, 2025, and currently under review, proposes a novel approach to mitigate hallucinations in Multimodal Large Language Models (MLLMs). The core idea is to guide the model's attention towards relevant visual cues, thereby reducing the likelihood of generating hallucinated content. The researchers introduce a vision-guided attention mechanism that helps the model focus on the most informative parts of the visual input. By explicitly directing the model's focus, this method aims to improve the grounding of the generated text in the visual context, leading to more accurate and reliable outputs. This is particularly important in applications where the model needs to integrate information from both text and images, such as image captioning and visual question answering.

Alternating Perception-Reasoning for Hallucination-Resistant Video Understanding

Another paper from November 25, 2025, consisting of 32 pages and 36 figures, introduces an alternating perception-reasoning framework to address hallucinations in video understanding. The authors argue that hallucinations often arise from a disconnect between the model's perception of the video content and its reasoning process. To bridge this gap, they propose a framework that alternates between perceiving the video and reasoning about its content. This iterative process allows the model to refine its understanding of the video, reducing the risk of generating hallucinated outputs. The alternating perception-reasoning approach offers a promising direction for enhancing the reliability of video understanding systems.

"AGI" team at SHROOM-CAP: Data-Centric Approach to Multilingual Hallucination Detection using XLM-RoBERTa

The "AGI" team at SHROOM-CAP presents a data-centric approach to multilingual hallucination detection in this paper from November 23, 2025. Accepted to the 1st Workshop on Confabulation, Hallucinations & Overgeneration in Multilingual and Practical Settings (CHOMPS) at AACL-IJCNLP 2025, the research leverages the XLM-RoBERTa model to identify hallucinations across multiple languages. The authors emphasize the importance of high-quality training data for effective hallucination detection. Their data-centric approach focuses on curating a dataset that accurately reflects the nuances of multilingual text, enabling the model to better distinguish between factual and hallucinated content. This work highlights the crucial role of data in building robust and reliable AI systems.

Measuring the Impact of Lexical Training Data Coverage on Hallucination Detection in Large Language Models

This paper, published on November 22, 2025, investigates the impact of lexical training data coverage on hallucination detection in LLMs. The researchers explore how the diversity and comprehensiveness of the vocabulary used in the training data affect the model's ability to identify hallucinations. Their findings suggest that models trained on datasets with broader lexical coverage are better equipped to detect and mitigate hallucinations. This underscores the importance of carefully selecting and curating training data to ensure that LLMs are exposed to a wide range of linguistic expressions and concepts.

Intervene-All-Paths: Unified Mitigation of LVLM Hallucinations across Alignment Formats

Accepted to NeurIPS 2025, this paper from November 21, 2025, introduces Intervene-All-Paths, a unified approach to mitigate hallucinations in Large Vision-Language Models (LVLMs) across various alignment formats. The researchers propose a novel intervention strategy that targets the underlying causes of hallucinations, regardless of the specific alignment technique used to train the model. The project page for this research can be found at https://github.com/SooLab/AllPath. This unified approach offers a promising solution for improving the reliability of LVLMs in diverse applications.

AI Safety

AI safety is a critical field that focuses on ensuring that AI systems are developed and deployed in a way that aligns with human values and avoids unintended negative consequences. As AI models become increasingly powerful, it is essential to address potential safety risks and ensure that these systems are beneficial to society. Research in AI safety encompasses a wide range of topics, including preventing harmful behavior, ensuring robustness, and maintaining transparency and accountability.

Predictive Safety Shield for Dyna-Q Reinforcement Learning

This paper, published on November 26, 2025, introduces a predictive safety shield for Dyna-Q reinforcement learning. Reinforcement learning (RL) algorithms can be prone to unsafe actions during the learning process. To address this, the researchers propose a safety shield that predicts potential unsafe states and prevents the agent from entering them. This predictive safety mechanism enhances the safety and reliability of RL algorithms, making them more suitable for real-world applications where safety is paramount. The integration of safety shields into RL systems is a crucial step towards deploying these algorithms in safety-critical domains.

Self-Guided Defense: Adaptive Safety Alignment for Reasoning Models via Synthesized Guidelines

Another paper from November 26, 2025, presents a self-guided defense mechanism for reasoning models. This approach aims to improve the safety alignment of AI systems by using synthesized guidelines. The model learns to generate its own safety guidelines and uses these guidelines to evaluate its actions. This self-guided defense mechanism allows the model to adapt to new situations and maintain safety alignment without relying on external supervision. The ability of AI systems to self-regulate their behavior is a key aspect of ensuring long-term safety.

Breaking the Safety-Capability Tradeoff: Reinforcement Learning with Verifiable Rewards Maintains Safety Guardrails in LLMs

This paper, also from November 26, 2025, and presented at the AAAI-26 Workshop on Post-AI Formal Methods, explores how to break the safety-capability tradeoff in LLMs. The researchers propose a reinforcement learning framework with verifiable rewards that maintains safety guardrails while preserving the model's capabilities. This approach allows LLMs to learn complex tasks without compromising safety. The ability to balance safety and capability is crucial for deploying powerful AI systems in a responsible manner.

GuardTrace-VL: Detecting Unsafe Multimodel Reasoning via Iterative Safety Supervision

GuardTrace-VL, published on November 26, 2025, introduces a method for detecting unsafe multimodel reasoning through iterative safety supervision. This approach focuses on identifying potential safety violations in systems that combine multiple AI models. The iterative safety supervision process allows the system to continuously monitor its reasoning process and detect unsafe actions. This is particularly important in complex AI systems where interactions between multiple models can lead to unforeseen safety risks. Robust safety monitoring mechanisms are essential for ensuring the reliability and trustworthiness of multimodel AI systems.

Conformal Safety Monitoring for Flight Testing: A Case Study in Data-Driven Safety Learning

This paper, published on November 25, 2025, and presented at the ICRA 2025 Workshop on Robot safety under uncertainty from intangible specifications, presents a case study in data-driven safety learning for flight testing. The researchers apply conformal safety monitoring techniques to ensure the safety of autonomous flight systems. Conformal prediction provides a rigorous framework for quantifying uncertainty and making safety-critical decisions. This work demonstrates the practical application of safety learning methods in a real-world setting, highlighting the importance of data-driven approaches to safety in robotics and autonomous systems.

Conclusion

The research papers discussed in this article represent the cutting edge of AI research in hallucination mitigation and safety. These studies offer valuable insights into the challenges and potential solutions in these critical areas. As AI continues to advance, it is essential to prioritize research that promotes the reliability, safety, and trustworthiness of AI systems.

For more in-depth information on AI safety, consider exploring resources from organizations like the Alignment Research Center, which is dedicated to ensuring AI systems align with human values.