hallucination

The article highlights that an AI finance agent refusing to give a final answer may still hallucinate by inventing side information, questioning whether this constitutes a failure in uncertainty evaluation.

This paper introduces HalluPrism, a behavioral diagnostic method for multimodal large language models that uses visual perturbation probes to identify hallucination failure modes, improving failure-family classification over confidence-only methods.

The article describes a type of AI hallucination where claims are accurate but quotations are fabricated, evading standard fact-checking, and discusses implementation challenges in detecting such errors.

The article argues that vague legal disclaimers like 'AI can make mistakes' reduce incentives for AI companies to improve quality, leading to a race to the bottom in hallucination rates.

The paper audits large language models on their refusal and fabrication behavior in clinical pain speech transcripts, finding that authority-framed prompts lead to confident fabrication in models like Gemini 2.5 Flash and Llama 3.1 8B, while cooperative prompting shows robust abstention.

This survey paper presents a lifecycle-based framework for understanding hallucinations in LLMs, covering causes, detection, mitigation, and prevention across data, training, and inference stages.

An AI system in a multi-agent setup fabricated a detailed memory that was archived and used in manuscripts, caught through verification of claims. The author shares practical lessons to prevent such issues, like requiring evidence and cross-checking artifacts.

OpenAI claims it took a week to realize its models were hacked by Hugging Face, while mainstream media highlights enterprise struggles with AI reliability, token costs, and context management in multi-step tasks.

This paper introduces Gated Activation Steering, a method to reduce sycophancy and hallucination in large language models for medical question answering using inference-time interventions. Evaluated on clinical data, it demonstrates improved robustness under user pressure.

This paper audits scene-level confabulation in LLM-generated autobiography against a documented ground-truth corpus, finding a 96.7% verification-failure rate and contributing a reusable audit instrument and a grounding remedy.

The article explores how enhanced reasoning in AI models can increase hallucination rates, proposing a 'Reasoning Tax' concept and emphasizing the need for robust context governance in enterprise applications.

This paper proposes a Rust-based multi-agent architecture that uses LLM hallucinations as a feature to generate and evaluate scientific hypotheses, comparing its performance against direct prompting and other methods.

A benchmark test evaluates various AI models by prompting them to count marshmallows in an image, with results ranging from 472 to 539 counts.

The article catalogs ten documented failure modes in multimodal AI systems where models generate fluent answers that break correspondence with actual inputs, based on benchmark papers and research studies.

The article suggests using a smaller 4B LLM with Kiwix skill and local Wikipedia to avoid hallucinations about world knowledge, instead of relying on larger models.

Structured Prior Knowledge (SPK) is a framework that explicitly extracts latent semantic, geometric, and contextual priors from pretrained object detectors to achieve state-of-the-art out-of-distribution detection, improving interpretability and reliability.

The paper proposes a structural abstention pattern for AI systems to prevent hallucinations in text-to-SQL applications by separating a trusted kernel for deterministic execution from a generative shell for interpretation, enhancing reliability in enterprise deployments.

This research analyzes hallucination in legal RAG systems across eight models and two legal corpora, finding that hallucinations persist with rates ranging from under 10% to nearly half, particularly for false-premise questions.

This paper studies the trade-off between grounding and coverage in long-form hallucination reinforcement learning, proposing rubric-based rewards to represent required and optional information for questions. A soft combination of grounding, rubric coverage, and relevance yields the best balance between support and richness.

An AI scribe during a medical appointment falsely claimed a patient was taking illegal drugs, causing distress and raising concerns about AI accuracy and patient safety in healthcare.