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.
CoFrGeNet: Continued Fraction Architectures for Language Generation
CoFrGeNet introduces Continued Fraction Generative Networks, a new function class that replaces Multi-head Attention and Feed-Forward Networks in Transformer blocks with fewer parameters. The architecture includes custom gradient formulations for efficient optimization and can be plugged into existing Transformer workflows with minimal changes. Experiments on GPT2‑xl and Llama3 show competitive or superior performance on downstream tasks while using 1/2 to 2/3 of the original parameters and shorter pre‑training time.
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arXiv Machine Learning
Jun 3
Dynamic Short Convolutions Improve Transformers
arXiv:2606. 03825v1 Announce Type: new Abstract: Transformers have become the dominant architecture for large language models, largely due to the scalability and flexibility of attention, feed-forward layers, residual connections, and normalization.
arXiv AI
Aug 5
On the Limits of Layer Pruning for Generative Reasoning in Large Language Models
arXiv:2602. 01997v3 Announce Type: replace-cross Abstract: Recent work has shown that layer pruning can effectively compress large language models (LLMs) while retaining strong performance on classification benchmarks, often with little or no finetuning.
arXiv AI
Jun 16
Long-Context Modeling via GSS-Transformer Hybrid Architecture with Learnable Mixing
arXiv:2606. 16093v1 Announce Type: cross Abstract: Modeling long-range dependencies remains a central challenge in natural language processing.
arXiv Machine Learning
Aug 19
Recirculation
The paper introduces recirculation, an inference‑time architectural enhancement for foundation models that reduces perplexity and improves accuracy on generation and reasoning tasks without adding significant latency. Recirculation adds a specific form of recurrence, enabling the model to function as a dynamical system that tracks belief states, and is distinct from chain‑of‑thought or depth‑recurrence methods. An adaptive variant requires minimal hyperparameter tuning and achieves notable gains on the Gemma3 family, including a 23% perplexity drop and a 21% accuracy increase on GSM8k.
arXiv AI
Jul 17
T^2MLR: Transformer with Temporal Middle-Layer Recurrence
arXiv:2607. 15178v1 Announce Type: cross Abstract: Transformer reasoning is limited by autoregressive decoding, which repeat edly compresses rich hidden computation through token space and makes it difficult for intermediate reasoning states to persist across time.
arXiv Machine Learning
Jun 17
Olmo Hybrid: From Theory to Practice and Back
arXiv:2604. 03444v4 Announce Type: replace Abstract: Recent work has demonstrated the potential of non-transformer language models, especially linear recurrent neural networks (RNNs) and hybrid models that mix recurrence and attention.
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 24
Distilling Sequential Computation in Transformer Language Models
arXiv:2609.27233v1 Announce Type: new Abstract: Transformer language models process sequences token by token in an autoregressive manner, making growing contexts increasingly expensive. Yet many adja...
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.
Hugging Face Trending Papers
Sep 23
Distilling Sequential Computation in Transformer Language Models
Transformer language models process sequences token by token in an autoregressive manner, making growing contexts increasingly expensive. Yet many adjacent token spans are highly predictable or freque...
arXiv AI
Aug 18
SAPE: Sandwich Adapters for Parameter Efficiency in Large Language Model Fine-Tuning
arXiv:2608. 15360v1 Announce Type: cross Abstract: While Parameter-Efficient Fine-Tuning (PEFT) has substantially reduced the hardware cost of adapting Large Language Models (LLMs) by decreasing the number of trainable parameters, recent studies have sought to further improve PEFT through parameter sharing.