arXiv AI
Aug 11
Full-bandwidth transformer
arXiv:2608. 08888v1 Announce Type: new Abstract: Autoregressive transformers compute along two axes: horizontally across generated tokens, and vertically through model depth.
Pretraining Latent Information Feedback Transformers with Teacher Supervision
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arXiv Machine Learning
Jun 16
Next-Latent Prediction Transformers Learn Compact World Models
arXiv:2511. 05963v4 Announce Type: replace Abstract: Transformers replace recurrence with a memory that grows with sequence length and self-attention that enables ad-hoc lookups over past tokens.
arXiv Computation and Language
Sep 16
Persistent Recurrent Memory Between Transformer Layers - Improves Language Model Generalization
The paper proposes a lightweight recurrent memory module inserted between the lower and upper halves of a 6‑layer decoder‑only transformer. This module, which uses cross‑attention to observe hidden states, a GRU to update a persistent state, and gated addition to modulate subsequent layers, adds only 3.7% more parameters. It reduces evaluation loss by 28.5% and narrows the generalization gap, with ablations showing the benefit comes solely from the memory topology rather than auxiliary losses.
arXiv Machine Learning
Jun 9
Rethinking Local Learning: A Cheaper and Faster Recipe for LLM Post-Training
arXiv:2605. 04913v4 Announce Type: replace-cross Abstract: LLM post-training typically propagates task gradients through the full depth of the model.
arXiv AI
Jun 29
The Context-Ready Transformer
arXiv:2606. 27538v1 Announce Type: cross Abstract: We introduce the context-ready transformer, a new recurrent neural network architecture built from a D-layer transformer block that pre-contextualizes each token before it enters the block.
arXiv Machine Learning
Sep 2
Latent Recurrent Transformer: Architecture Exploration, Training Strategies, and Scaling Behavior
arXiv:2605.26797v2 Announce Type: replace Abstract: We study Latent Recurrent Transformer (LRT), a lightweight augmentation of autoregressive transformers that reuses a high-level source-layer hidden...
arXiv AI
Jun 8
Data-Efficient Autoregressive-to-Diffusion Language Models via On-Policy Distillation
arXiv:2606. 06712v1 Announce Type: cross Abstract: We study the transformation of autoregressive models (ARLMs) into diffusion language models (DLMs).
arXiv Machine Learning
Sep 17
Long-Context Demonstration Selection Using State Space Models
The paper addresses the challenge of selecting demonstrations for long-context language model queries, where transformer inference costs grow quadratically with sequence length. It proposes two algorithms that distill transformer behavior into state space models (SSMs) with linear inference time, partitioning transformer layers into groups and estimating separate SSMs for each. The distilled SSMs achieve less than 0.7% approximation error, and in downstream tasks they reduce FLOPs by 14.2× while improving accuracy by 6.48% compared to baseline methods.
arXiv Machine Learning
Jun 4
Breaking the Scale Barrier: One-Shot Knowledge Transfer via Frequency Transform
arXiv:2603. 07523v3 Announce Type: replace Abstract: Transferring knowledge by fine-tuning large-scale pre-trained networks has become a standard paradigm for downstream tasks, yet the knowledge of a pre-trained model is tightly coupled with monolithic architecture, which restricts flexible reuse across models of varying scales.
arXiv Computer Vision
2d ago
Compressing History into Memory: Distilling Transformers into Recurrent Transformers
arXiv:2606.21562v2 Announce Type: replace Abstract: Transformers are AI's workhorse but their computational cost becomes prohibitive when processing long sequences. We target long-horizon streaming v...
arXiv AI
Aug 18
Decode-Branch Transformers: Decoupling the Primary Prefill Path from Additional Decode Computation
arXiv:2608. 12385v2 Announce Type: replace Abstract: As large language models serve ever more requests, cumulative inference cost is growing relative to the one-time cost of training.
arXiv AI
Jun 15
Learning What to Predict: Downstream-Guided Task Design for Continued Pretraining
arXiv:2601. 22108v2 Announce Type: replace-cross Abstract: Continued pretraining is optimized with fixed self-supervised tasks but selected by downstream performance, creating a coarse feedback loop in which practitioners evaluate checkpoints, change data mixtures or objectives, and restart runs, while individual updates remain blind to target capabilities.