Adaptive scheduling of interventions in discrete diffusion language models, timed to attribute-specific commitment schedules discovered with sparse autoencoders, delivers precise multi-attribute steering up to 93% strength while preserving generation quality.
Tdgnet: Hallucination detection in diffusion language models via temporal dynamic graphs
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
In diffusion language models, coarse linguistic labels stabilize earlier than exact token identity, uncertainty tracks correctness, and mid-trajectory states are most sensitive to perturbations.
citing papers explorer
-
Steering Without Breaking: Mechanistically Informed Interventions for Discrete Diffusion Language Models
Adaptive scheduling of interventions in discrete diffusion language models, timed to attribute-specific commitment schedules discovered with sparse autoencoders, delivers precise multi-attribute steering up to 93% strength while preserving generation quality.
-
Measuring Temporal Linguistic Emergence in Diffusion Language Models
In diffusion language models, coarse linguistic labels stabilize earlier than exact token identity, uncertainty tracks correctness, and mid-trajectory states are most sensitive to perturbations.