#decoding-strategies News & Analysis

4 articles tagged with #decoding-strategies. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AINeutralarXiv – CS AI · Jun 106/10

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Mitigating Manifold Departure: Uncertainty-Aware Subspace Rectification for Trustworthy MLLM Decoding

Researchers propose MGAP, a training-free decoding method that reduces hallucinations in multimodal large language models (MLLMs) by selectively suppressing language priors while preserving semantic structure. Unlike previous approaches that blindly penalize language biases, MGAP uses geometry-aware subspace projection to distinguish between helpful and harmful language priors, achieving improved hallucination suppression without degrading model coherence.

AINeutralarXiv – CS AI · Jun 16/10

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What Gets Unmasked First? Trajectory Analysis of Diffusion Models for Graph-to-Text Generation

Researchers present the first systematic study of masked diffusion language models (MDLMs) for graph-to-text generation, revealing that these models naturally prioritize entities before relational words and structural tokens. The study identifies a failure mode in supervised fine-tuning that prematurely anchors structural tokens, and proposes lambda-scaled structural decoding to recover performance gains while introducing Graph-LLaDA for improved generalization across datasets.

AINeutralarXiv – CS AI · May 286/10

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Can I Have Your Order? Monte-Carlo Tree Search for Slot Filling Ordering in Diffusion Language Models

Researchers introduce McDiffuSE, an MCTS-based framework that optimizes slot-filling order in Masked Diffusion Models to improve performance on mathematical and code reasoning tasks. The approach achieves 3.2% improvement over autoregressive baselines and up to 19.5% gains on specific benchmarks by strategically exploring generation orderings rather than following sequential patterns.

AINeutralarXiv – CS AI · Apr 146/10

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Early Decisions Matter: Proximity Bias and Initial Trajectory Shaping in Non-Autoregressive Diffusion Language Models

Researchers identify a critical failure mode in non-autoregressive diffusion language models caused by proximity bias, where the denoising process concentrates on adjacent tokens, creating spatial error propagation. They propose a minimal-intervention approach using a lightweight planner and temperature annealing to guide early token selection, achieving substantial improvements on reasoning and planning tasks.