DCDM replaces positional blocks with learnable semantic chunks via differentiable Chunking Attention, yielding consistent gains over block and unstructured diffusion baselines up to 1.5B parameters.
Piqa: Reasoning about physical commonsense in natural language
3 Pith papers cite this work. Polarity classification is still indexing.
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verdicts
UNVERDICTED 3representative citing papers
Looped language models with latent iterative computation and entropy-regularized depth allocation achieve performance matching up to 12B standard LLMs through superior knowledge manipulation.
FASQ delivers calibration-free LLM compression with continuous size trade-offs via product quantization and custom CUDA kernels that accelerate decode beyond FP16 speeds on consumer hardware.
citing papers explorer
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Dynamic Chunking for Diffusion Language Models
DCDM replaces positional blocks with learnable semantic chunks via differentiable Chunking Attention, yielding consistent gains over block and unstructured diffusion baselines up to 1.5B parameters.
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Scaling Latent Reasoning via Looped Language Models
Looped language models with latent iterative computation and entropy-regularized depth allocation achieve performance matching up to 12B standard LLMs through superior knowledge manipulation.
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FASQ: Flexible Accelerated Subspace Quantization for Calibration-Free LLM Compression
FASQ delivers calibration-free LLM compression with continuous size trade-offs via product quantization and custom CUDA kernels that accelerate decode beyond FP16 speeds on consumer hardware.