Beyond Standard LLMs

Train and Fine-Tune Sentence Transformers Models

Induction in Both Directions: A Mechanistic Analysis of In-Context Learning in Masked Diffusion Language Models

arXiv:2607. 15893v1 Announce Type: cross Abstract: While the internal mechanisms of autoregressive (AR) transformers have been studied extensively, much less is known about diffusion language models (DLMs), an emerging alternative that generates text by iterative denoising.

DiffuMamba: High-Throughput Diffusion LMs with Mamba Backbone

arXiv:2511. 15927v4 Announce Type: replace-cross Abstract: Diffusion language models (DLMs) have emerged as a promising alternative to autoregressive (AR) generation, yet their reliance on Transformer backbones limits inference efficiency due to quadratic attention or KV-cache overhead.

Diffuse to Compress: Leveraging Diffusion LMs for Lossless Compression

arXiv:2608. 11249v1 Announce Type: cross Abstract: We study the problem of lossless text compression, motivated by the rapid growth in the collection and storage of digital textual data - including plain text, source code, and structured formats such as XML - and by recent advances in neural language model-based compression.

A Visual Guide to Attention Variants in Modern LLMs

From MHA and GQA to MLA, sparse attention, and hybrid architectures

RELISH: LLM REgression with a Latent Iterative State Head

arXiv:2604. 01206v2 Announce Type: replace-cross Abstract: We present RELISH (REgression with a Latent Iterative State Head), a novel, lightweight architecture designed for text regression with large language models.

Towards Understanding What State Space Models Learn About Code

arXiv:2602. 06774v2 Announce Type: replace Abstract: State Space Models (SSMs) have emerged as an efficient alternative to the Transformer architecture.

The State Of LLMs 2025: Progress, Problems, and Predictions

A 2025 review of large language models, from DeepSeek R1 and RLVR to inference-time scaling, benchmarks, architectures, and predictions for 2026.

A Survey on Diffusion Language Models

arXiv:2508. 10875v3 Announce Type: replace-cross Abstract: Diffusion Language Models (DLMs) are rapidly emerging as a powerful and promising alternative to the dominant autoregressive (AR) paradigm.

TimpaTeks: Automatic In-place Text Sequence Modification via Diffusion Language Model Steering

arXiv:2606. 08408v1 Announce Type: cross Abstract: We extend activation steering to diffusion language models (DLMs) and study a novel problem that arose due to the inference mechanism of DLMs: Modifying a text in-place to manifest a different concept.

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.

Large Language Models: A New Moore's Law?