Hallucination

Timeline

1. Oct 8, 2026 · Research paper · 1 source
   Hybrid Cinematography: Previsualizing and Managing Hallucination Risk in Generative Video Reshooting
   We present Hybrid Cinematography, a workflow that bridges physical capture and generative reshooting to manage hallucination risk while filmmakers can still act on it.
2. Oct 8, 2026 · Research paper · 1 source
   Beyond Visual Enhancement: Adaptive Multi-Context Steering to Mitigate LVLM Hallucinations
   Hallucination remains a significant challenge in Large Vision-Language Models (LVLMs).
3. Oct 8, 2026 · Research paper · 1 source
   From Suppression to Repair: Mitigating Object Hallucination in Large Vision-Language Models via Localized Distribution Alignment
   Object hallucination remains a major obstacle for large vision-language models (LVLMs) to generate reliable content.
4. Oct 8, 2026 · Research paper · 1 source
   SAGE: Sink-Aware Guided Emphasis for Visual Grounding in Vision-Language Decoders
   Building on this insight, we propose SAGE (Sink-Aware Guided Emphasis), a lightweight intervention that steers decoder attention away from PIS and toward query-dependent regions of interest (ROIs) using token-aligned ROI masks derived from standard vision backbones such as CLIP, ViT, and DINOv3.
5. Oct 8, 2026 · Research paper · 1 source
   Fact over Fiction: Detection of Pathological Hallucinations in Sinhala-to-English Neural Machine Translation
   Neural Machine Translation (NMT) models, while capable of producing highly fluent outputs, remain vulnerable to hallucinations, which are translations that are natural yet semantically unrelated to the source.
6. Oct 7, 2026 · Research paper · 1 source
   When Citations Mislead? A Claim-Level Benchmark for Legal Hallucination Detection
   We introduce PARCEL, a benchmark for checking whether a legal claim is supported by the underlying authority.
7. Oct 7, 2026 · Research paper · 1 source
   PHRBench: A Behavioral Evaluation of Post-Hallucination Reasoning in LLMs
   Existing studies of post-hallucination reasoning (PHR) mainly characterize changes in final outcomes and aggregate reasoning dynamics, leaving how models resolve hallucinated premises at the response level insufficiently understood.
8. Oct 7, 2026 · Research paper · 1 source
   Agentic AI-Assisted Modeling for Production Scheduling: Assessment in Constraint Programming
   Developing optimization models for production scheduling requires substantial expert effort.
9. Oct 7, 2026 · Research paper · 1 source
   A Tale of Two Error Categories: Exploring Concealed Trade-Offs in the Errors of Automated Judges in Evaluation of Uncertainty Quantifiers
   In a meta-analysis of published work, we show that automated judgement is the present norm.
10. Oct 7, 2026 · Research paper · 1 source
    Sparse Feature Policy Unlearning Mitigates State Hallucination in Vision-Language-Action Models
    Vision-Language-Action (VLA) models have shown strong generalization in robotic manipulation by leveraging rich representations from pretrained vision-language models.
11. Oct 7, 2026 · Research paper · 1 source
    Package Hallucination Attacks on Coding Agents through Prompt Injection in Rule Files
    To bridge this gap, we introduce the package hallucination attack, where an attacker injects malicious prompts into benign rule files to induce coding agents to replace legitimate dependencies with attacker-controlled packages.
12. Oct 6, 2026 · Research paper · 1 source
    Trustworthy Domain-Specific AI for Structured Knowledge Retrieval and Reasoning
    This dissertation presents a scalable architecture for transforming unstructured, domain-specific text into structured knowledge for retrieval and reasoning.
13. Oct 6, 2026 · Research paper · 1 source
    Foresight-over-Graph: Reasoning Beyond Local Horizons for Knowledge Base Question Answering
    To address this limitation, we propose Foresight-over-Graph (FoG), a foresight-aware evidence retrieval framework for knowledge base question answering (KBQA).
14. Oct 6, 2026 · Research paper · 1 source
    The Labeling Problem in Hallucination Detection Benchmarks: An Empirical Evaluation
    In recent years, several methods for detecting when large language models (LLMs) hallucinate have been developed.
15. Oct 6, 2026 · Research paper · 1 source
    Rethinking Faithfulness in LLMs: A Pairwise Context-Sensitive Perspective
    In this work, we propose a Pairwise Faithfulness Benchmark (PFaithBench) that evaluates whether a model can switch between answering and abstaining for the same question under supporting versus non-supporting contexts.

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