Kestrel: Grounded Self-Refinement for LVLM Hallucination Mitigation

Large vision-language models (LVLMs) have become increasingly strong and perform better than earlier ones, yet their hallucination issues are not completely solved. Previous training-free hallucination reduction methods are mostly developed for earlier LVLMs and rely on correcting internal representation, which we show that they can bring no or negative gain when applied on stronger LVLMs. On the other hand, introducing complementary external visual evidence can provide additional grounding beyond the LVLM itself, but its potential unreliability motivates conservative evidence verification and refinement rather than direct correction. Therefore, we propose **Kestrel**, a training-free framework for stronger LVLM hallucination mitigation that operationalizes conservative external-evidence intervention through explicit visual grounding and evidence-verified iterative self-refinement. In detail, Kestrel first collects explicit visual evidence and converts tool outputs into structured textual evidence. Second, to take full advantage of this evidence, Kestrel verifies it via the same LVLM judge for evidence checking, then iteratively self-refines answers based on verified evidence to reduce the risk of over-correction. Extensive experiments on stronger LVLMs show that Kestrel consistently improves performance across hallucination benchmarks. In particular, Kestrel improves Qwen3-VL by an average of +3.82% on POPE and +3.87% on MME-Hallucination, and improves InternVL3.5 by an average of +3.49% and +2.69%, respectively. The grounding agent contributes a relative gain of +2.14%, while the integrated self-refinement module contributes +2.23% on POPE. To conclude, our Kestrel effectively alleviates the hallucination in stronger LVLMs with transparent verification traces for diagnosis and analysis.