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  1. Home
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  3. Kestrel: Grounding Self-Refinement for LVLM Hallucination Mi
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Kestrel: Grounding Self-Refinement for LVLM Hallucination Mitigation

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Evidence Receipt

Freshness: 2026-04-02T02:30:40.136932+00:00

Claims: 0

References: 0

Proof: pending

Distribution: unknown

Source paper: Kestrel: Grounding Self-Refinement for LVLM Hallucination Mitigation

PDF: https://arxiv.org/pdf/2603.16664v1

First buyer signal: unknown

Distribution channel: unknown

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Dimensions overall score 7.0

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Builds On This
Revis: Sparse Latent Steering to Mitigate Object Hallucination in Large Vision-Language Models
Score 6.0down
Prior Work
HALP: Detecting Hallucinations in Vision-Language Models without Generating a Single Token
Score 7.0stable
Prior Work
Mitigating Object Hallucinations in LVLMs via Attention Imbalance Rectification
Score 7.0stable
Prior Work
Revealing Multi-View Hallucination in Large Vision-Language Models
Score 7.0stable
Prior Work
First Logit Boosting: Visual Grounding Method to Mitigate Object Hallucination in Large Vision-Language Models
Score 7.0stable
Prior Work
HalluGuard: Demystifying Data-Driven and Reasoning-Driven Hallucinations in LLMs
Score 7.0stable
Higher Viability
Fighting Hallucinations with Counterfactuals: Diffusion-Guided Perturbations for LVLM Hallucination Suppression
Score 8.0up
Competing Approach
Vision-Language Introspection: Mitigating Overconfident Hallucinations in MLLMs via Interpretable Bi-Causal Steering
Score 3.0down

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