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The paper introduces Information-Guided Frontier Decoding (IGFD), a training‑free strategy for diffusion multimodal language models that prioritizes the commitment of reliable semantic tokens over fragile structural ones. IGFD ranks candidates by token confidence, neighborhood uncertainty, and structural commitment risk, and uses a dynamic frontier to limit selection to locally expandable regions. Across multiple multimodal benchmarks, IGFD consistently outperforms existing decoding methods while requiring no extra training or forward passes.
arXiv:2606. 07647v1 Announce Type: cross Abstract: Large vision language models (LVLMs) have made rapid advancements and are deployed across various applications, yet hallucinations remain a major challenge.
The paper introduces RVSD, a training‑free, plug‑and‑play decoding framework that combines token sparsification with Semantic‑Space Visual Retrieval (SSVR) to reduce visual hallucinations in large vision‑language models. RVSD employs a semantics‑directed token selection strategy to prune redundant tokens while preserving essential visual information, and uses SSVR to perform on‑demand cross‑modal retrieval within a shared semantic space. Experiments show that RVSD achieves state‑of‑the‑art performance in mitigating visual hallucinations while maintaining strong suppression in long‑context generation.
arXiv:2605. 18160v2 Announce Type: replace-cross Abstract: In recent years, multimodal large language models (MLLMs) have achieved remarkable progress, primarily attributed to effective paradigms for integrating visual and textual information.
ReWEIGH the Evidence is a training‑free decoding technique that calibrates token‑level ordinal visual evidence to reduce hallucinations in large vision‑language models. It aggregates vocabulary ranks across visual positions, compares candidates to a token‑specific reference derived from unlabeled images, and applies a bounded penalty only when evidence falls below this reference. Experiments on four 7B backbones show up to a 21.3% reduction in hallucinated object mentions while largely preserving or improving descriptive and general performance, with minimal added latency.
arXiv:2605. 25820v2 Announce Type: replace Abstract: Diffusion-based multimodal large language models (dMLLMs) decode by iteratively predicting tokens at multiple masked positions in parallel.