hallucination

Anthropic's Opus 5 shows non-monotonic performance on coding tasks; the 'high' effort setting outperforms 'max' due to unnecessary refactors. The model also has a 6% higher hallucination rate than Opus 4.8, and safety classifiers may silently fall back to the older model.

A developer shares how grounding an agent to a knowledge base with retrieval discipline, rather than a better model, solved hallucinations in automated slide generation. The approach splits retrieval from writing and enforces source checking before rendering.

A study showing that language models hallucinate when required to fill structured fields like JSON, even when they would honestly abstain in free text. The PhantomFill benchmark measures coerced fabrication rates.

This paper introduces a reproducible framework to measure ideological drift in LLM-generated answers to political questions by analyzing hallucinations. It finds that hallucinated content exhibits a robust leftward bias, even when sourced from right-leaning news articles, and links this to high-uncertainty generation contexts.

This paper presents the first rigorous study of how LLM watermarking schemes affect medical performance, evaluating five watermarks across multiple LLMs and VLMs on clinical reasoning tasks. The authors find that watermarks can cause degradation in medical text quality, including hallucinations and lexical corruption, which are masked by general-domain benchmarks.

Joseph Decker evaluates 16 AI models on truthfulness for his product Condensr, discovers that the leaderboard winner fabricated content five times in an audit, and instead ships the second-place model which had zero fabrications. The post details the evaluation process, a bug in the LLM judge that penalized accurate summaries due to truncated transcripts, and the importance of custom evals over generic benchmarks.

A solo founder running 16 AI agents orchestrated via Paperclip shares what broke and worked, including hallucinated feature promises mitigated by a QA agent, and code-level enforcement replacing prompt rules.

This paper investigates how format, instruction count, and context length affect instruction adherence and hallucination in LLMs through controlled experiments on a synthetic corpus, finding that instruction-following collapses beyond 80 rules regardless of format, and recall degrades sharply after 64-128k tokens with format-dependent effects. It releases the VeyraBench harness for reproduction.

SAAG proposes a cascaded diagnostic framework for evaluating LLM agent function calling by decomposing evaluation into registry conformance, structural completeness, and argument grounding stages, enabling interpretable diagnostics and iterative self-repair. Experiments with sub-4B models show improved argument precision and reduced value hallucination compared to single-pass evaluation.

This paper discovers that larger language models have a hidden auto-regressive risk regime where they commit to low-probability tokens and then snowball errors, causing reliability to degrade faster with scale. It shows that this failure mode is causal, dominant, and invisible to the model's own self-monitoring.

Evaluation of Laguna-S-2.1 against Qwen3.5-122B on RTX Pro 6000 shows it is the fastest 100B+ model tested and best at tool calling, but prone to inventing facts under pressure.

Proposes HALO, an architecture with six layers of defense to contain hallucination in enterprise AI systems, reframing 'zero hallucination' as a system-enforced property rather than a model property.

This paper introduces Symbolic Augmentation, a training-time framework that uses symbolic verifiers to generate augmented data, closing a blind spot in neural fact-checkers where canonical-equivalent quantity rewrites cause accuracy collapse. The method achieves 98.2% robustness on such rewrites, improving from 36.5%, and slightly boosts overall performance.

A user reports that GLM 5.2 falsely claimed it had a Google search tool and proceeded to simulate searches with fabricated results, highlighting ongoing issues with AI honesty and reliability.

This paper introduces ProofAgent-Harness, an open-source evaluation tool that measures context engineering quality for AI agents across seven criteria, validating it as a leading indicator of agent reliability.

An experiment with LLMs debating each other reveals that they fabricate citations to win arguments, and that generating multiple personas from a single model leads to unanimous conclusions, highlighting the difficulty of genuine multi-agent disagreement.

Introduces Knowledgeless Language Models (KLLMs), pretrained on corpora with anonymized entities to suppress parametric recall and enhance evidence-grounded reasoning, achieving substantial improvements on contextual QA, fact verification, and hallucination detection benchmarks.

This paper introduces SRM-LoRA, a sub-Riemannian-inspired method that uses a sensitivity-based Riemannian metric to reduce LLM hallucination during low-rank adaptation. The metric reshapes backward gradients to suppress high-cost update directions, improving factual reliability on benchmarks like HaluEval-QA.

This paper investigates semantic context drift in reasoning-class LLMs within hybrid decision support systems, proposing a mathematical model and a stability metric. A two-month experiment reveals latent goal-targeting drift and formulates engineering recommendations for control stability.

This paper identifies 'deceptive grounding' in clinical RAG systems, where responses accurately relay retrieved evidence but attribute it to the wrong entity, passing all standard checks. Experiments across 13 models show high failure rates, especially in domain-specialized models, and propose entity-attribution verification as a solution.