Recursive Masked Diffusion Models add recursive depth via repeated application of the same transformer to improve parameter efficiency and reduce inference steps in masked diffusion models.
InProceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 23336– 23351, Vienna, Austria
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UNVERDICTED 5representative citing papers
SpiralThinker stabilizes iterative latent reasoning in LLMs via text-latent interleaving and progressive alignment, achieving SOTA results among latent baselines on math, logic, and commonsense tasks.
CODI compresses explicit CoT into continuous space via self-distillation and is the first implicit method to match explicit CoT performance on GSM8k at GPT-2 scale with 3.1x compression and 28.2% higher accuracy than prior implicit approaches.
In a cellular automata rule-inference task designed to block memorization, neural models achieve high next-step accuracy but accuracy falls sharply with longer reasoning chains; depth, recurrence, memory, and test-time compute extend the reachable depth but do not remove the bound.
DAG-MoE uses a lightweight module to learn DAG-based structural aggregation of selected experts, expanding combination space and enabling intra-layer multi-step reasoning compared to standard weighted-sum MoE.
citing papers explorer
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Recursive Scaling in Masked Diffusion Models
Recursive Masked Diffusion Models add recursive depth via repeated application of the same transformer to improve parameter efficiency and reduce inference steps in masked diffusion models.
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SpiralThinker: Latent Reasoning through an Iterative Process with Text-Latent Interleaving
SpiralThinker stabilizes iterative latent reasoning in LLMs via text-latent interleaving and progressive alignment, achieving SOTA results among latent baselines on math, logic, and commonsense tasks.
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CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation
CODI compresses explicit CoT into continuous space via self-distillation and is the first implicit method to match explicit CoT performance on GSM8k at GPT-2 scale with 3.1x compression and 28.2% higher accuracy than prior implicit approaches.
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Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling
In a cellular automata rule-inference task designed to block memorization, neural models achieve high next-step accuracy but accuracy falls sharply with longer reasoning chains; depth, recurrence, memory, and test-time compute extend the reachable depth but do not remove the bound.
-
DAG-MoE: From Simple Mixture to Structural Aggregation in Mixture-of-Experts
DAG-MoE uses a lightweight module to learn DAG-based structural aggregation of selected experts, expanding combination space and enabling intra-layer multi-step reasoning compared to standard weighted-sum MoE.