arXiv AI
1d ago

Depth-Recurrent Attention Mixture (Dreamer): Giving Latent Reasoning the Attention it Deserves

The paper introduces Dreamer, a depth‑recurrent attention mixture framework that combines sequence attention, depth attention, and sparse expert attention to overcome the hidden‑size bottleneck in latent reasoning models. Dreamer decouples scaling dimensions, allowing efficient depth‑recurrent scaling, and achieves state‑of‑the‑art performance on language reasoning benchmarks while using 2 to 8× fewer training tokens than FLOP‑, parameter‑, and memory‑matched baselines. It also demonstrates significantly greater expert selection diversity, up to 11× larger than current MoE models.

By Jonas Knupp, Jan Hendrik Metzen, Jeremias Bohn, Georg Groh, Kristian Kersting
arXiv Machine Learning
Sep 24

Attention Routing Stabilizes Early: Working-Set Inference for Recurrent Language Models

The paper investigates how attention dynamics evolve across recurrent depth in language models, finding that attention support stabilizes early while hidden states and outputs take longer. It proposes WISE, a training‑free method that uses full attention in early steps and then reuses the discovered sparse working set for later steps, preserving performance on multi‑hop QA tasks. Experiments show that WISE maintains quality up to 2K context, offers measurable speedups, and highlights the importance of recurrent discovery of attention support.

By Ke Wan, Chen Chen
arXiv Computation and Language
Oct 12

RAMP: Recurrent Adaptive Mixed Precision for Efficient Prefill in Recurrent-Depth Language Models

The paper introduces RAMP, a Recurrent Adaptive Mixed Precision method that applies low‑precision weight‑and‑activation computation early in the recurrent depth of language models and switches to high precision for final refinement. By analyzing how quantization errors propagate, the authors show that early‑stage errors are dampened by subsequent steps, allowing RAMP to maintain near‑full‑precision prefill quality. Experiments up to 4K context lengths demonstrate up to a 1.16× speedup and a 13.9% latency reduction on an NVIDIA H100.

By Ke Wan, Chen Chen