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cs.CL2026
Information-Guided Frontier Decoding: Contextual Utility-Driven Commitment in dMLLMs
Xingyou Fang, Jingxing Zhong, Xiaosong Yuan +1
Decoding quality in diffusion multimodal language models (dMLLMs) depends heavily on the order in which masked tokens are committed. Existing confidence-based strategies prioritize…
cs.CL2026
Context-Aware Cluster Decoding: Semantic Anchor-Driven Coherence in dMLLMs
Yikai Zhao, Qiyan Zhao, Jiaquan Zhang +3
Diffusion multimodal large language models (dMLLMs) frequently produce long-form outputs marred by semantic drift and repetition, with quality generally degrading as output length…
cs.CL2026
Unlocking the Black Box of Latent Reasoning: An Interpretability-Guided Approach to Intervention
Shuochen Chang, Tong Bai, Xiaofeng Zhang +5
Latent reasoning enables Large Language Models (LLMs) to perform multi-step inference within continuous hidden states, offering efficiency gains over explicit Chain-of-Thought (CoT…