HalluWorld is a controlled benchmark using explicit reference world models to automatically label and disentangle hallucinations in LLMs across synthetic environments with varying complexity and observability.
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Survey of Hallucination in Natural Language Generation
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- background [315, 361]. Furthermore, Liu et al. [185], Zong et al. [395] and Liu et al. [184] show that LVLMs can be easily fooled and experience a severe performance drop due to their over-reliance on the strong language prior, as well as its inferior ability to defend against inappropriate user inputs [112, 134]. Jiang et al. [138], Wang et al. [315] and Jing et al. [141] took a step forward to holistically evaluate multi-modal hallucination. What's more, when presented with multiple images, LVLMs sometim
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A new benchmark with cognitive traps shows frontier deep research agents achieve only 13-16% acceptance on expert consulting tasks under combined verifier and rubric criteria.
Empirical audit finds hallucinated citations in roughly 5% of 2025 NeurIPS and USENIX Security papers, with post-ChatGPT increases and failures even in award papers.
LibEvoBench benchmark shows LLMs are version-oblivious on evolving APIs, with documentation helping but version specification not.
MedHal-Loc benchmark shows KG-triple hallucination detectors localize errors no better than chance on controlled medical statements due to entity extraction limits, while NLI and consistency methods succeed above chance, and real hallucinations are mostly diffuse conclusion changes.
Empirical study of 2,214 MCP servers finds 9.93% of 19,200 description-code pairs inconsistent via a new static-analysis-plus-LLM-prompting framework, with security implications.
Locate-then-edit succeeds at the same early-to-mid MLP locations in masked diffusion models as in autoregressive models, but requires optimization over intermediate partial-mask states to handle multi-token targets.
Randomized experiment finds AI draft assistance raises feedback provision by teaching assistants 10.8 percentage points without harming quality.
Reflexive agents confabulate incorrect task interpretations in memory, detected via Reflection Repetition Rate metric, with a programmatic mitigation raising correct object mentions from 0% to 86% in frozen ALFWorld cases.
QAOD projects away question-aligned directions from answer representations to isolate domain-agnostic factuality signals, enabling efficient hallucination detection with top in-domain AUROC and up to 21% better OOD transfer.
Malicious Skills induce coding agents to hallucinate and import attacker-controlled packages at high rates while evading detection.
LLMs routinely produce unsupported causal stories for personal sensing anomalies, and richer evidence or constrained prompts do not reliably eliminate this epistemic overreach.
Indirect elicitation via triplet comparisons recovers meaningful association structures from LLMs and supports conservative causal candidate links across prompted subpopulations.
Post-Reasoning boosts LLM accuracy by reversing the usual answer-after-reasoning order, delivering mean relative gains of 17.37% across 117 model-benchmark pairs with zero extra cost.
A graphlet-anchored framework generates 119,856 factually grounded biomedical QA pairs that improve accuracy on PubMedQA and MedQA benchmarks.
CyberCertBench shows frontier LLMs reach human-expert performance on general IT and networking security but drop on vendor-specific and formal standards questions such as IEC 62443, with a new framework for producing interpretable explanations.
Frontier LLMs generate BibTeX entries at 83.6% field accuracy but only 50.9% fully correct; two-stage clibib revision raises accuracy to 91.5% and fully correct entries to 78.3% with 0.8% regression.
A study of seven LLMs finds that realistic prompt variations such as one-character misspellings trigger library hallucinations in up to 26% of cases, fabricated names in up to 99%, and time-based prompts in up to 85%, and introduces LibHalluBench for evaluation.
Hallucinations arise from biased latent inference paths rather than missing knowledge, demonstrated via a new diagnostic testbed TrapQA that isolates task-retrieval and key-selection biases.
Introduces loop engineering as a distinct practice layer for coding agents, supplies a taxonomy and verification ladder, and analyzes a hand-coded corpus of fifty real loops.
Introduces a protocol scoring AI investment advisors on validity under constraints, stability, and agreement with a deterministic baseline, showing agreement often masks invalid actions.
Hallucination in world models is a data coverage issue predictable by three signals and preventable through targeted training sampling and online data collection.
Knowledge editing methods redistribute and suppress rather than overwrite facts in LLMs, creating narrow vulnerable regions in representation space that adversarial prompts can exploit.
Vaani Benchmark V1.0 is a multimodal Hindi ASR dataset from 104 districts featuring spontaneous speech recordings in real-world conditions and three independent transcriptions per segment for robust multi-reference evaluation.
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