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arXiv:2608. 03450v1 Announce Type: cross Abstract: Reasoning in Multimodal Large Language Models (MLLMs) requires both fine-grained visual perception and rigorous logical deduction.
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:2607. 27248v1 Announce Type: cross Abstract: While large language models excel in reasoning, these generalists often lack knowledge for specialized scientific domains.
arXiv:2609.24196v1 Announce Type: new Abstract: Looped Language Models (LoopLMs) perform "latent reasoning" by recursively refining internal latent representations with shared weights, offering a mor...
arXiv:2606. 16847v1 Announce Type: cross Abstract: Diffusion Large Language Models (dLLMs) offer a promising avenue for parallel generation but face a trade-off between decoding speed and quality.
arXiv:2609.13481v1 Announce Type: new Abstract: Large language models have made abstractive summarization remarkably fluent, but generated summaries can hallucinate facts, posing serious risks in bio...