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We propose HiLo-RD (High-to-Low Budget Representation Distillation) to improve masked diffusion language models (MDLMs) under a limited generation budget. Unlike autoregressive models, MDLMs choose both what to generate and where to commit it. This creates two coupled challenges: predicting useful content before reason...
**CRAFT** (Consensus Reasoning knowledge graph Aggregation for Flaw-aware Trace synthesis), which aggregates the consensus components shared across multiple candidate reasoning traces to synthesize improved ones, consistently improves label-prediction accuracy on both logical and mathematical reasoning benchmarks.
EviAnchor is proposed, a training-free and single-branch inference framework that preserves and reactivates visual evidence throughout generation in large vision-language models and demonstrates consistent improvements in visual grounding.
SemTrace is introduced, a source-grounded semantic watermark for detecting whether a generated review was influenced by a known protected manuscript copy, and targets model-agnostic, assigned-copy exposure detection while keeping the watermark semantically tied to the source document.
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