LLMs

Context Language Models
https://github.com/facebookresearch/context-language-models

Contrastive Language Models
https://contrastive-lm.notion.site/

Contrastive Language Models
https://contrastive-lm.notion.site/

nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4
https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4

The Illusion of Stochasticity in LLMs
https://arxiv.org/abs/2604.06543

nvidia/Gemma-4-31B-IT-NVFP4
https://huggingface.co/nvidia/Gemma-4-31B-IT-NVFP4

1-bit Bonsai: The First Commercially Viable 1-bit LLMs
https://prismml.com/news/bonsai-8b

Training Language Models via Neural Cellular Automata
https://hanseungwook.github.io/blog/nca-pre-pre-training/

Olmo Hybrid and future LLM architectures
https://www.interconnects.ai/p/olmo-hybrid-and-future-llm-architectures

Reversing Large Language Models for Efficient Training and Fine-Tuning
https://arxiv.org/abs/2512.02056

CALM: Continuous Autoregressive Language Models
https://github.com/shaochenze/calm

Distilled models ready for local deployment
https://teichai.com/models

One-step Language Modeling via Continuous Denoising
https://arxiv.org/abs/2602.16813

LaCy: What Small Language Models Can and Should Learn is Not Just a Question of Loss
https://arxiv.org/abs/2602.12005

MemoryLLM: Plug-n-Play Interpretable Feed-Forward Memory for Transformers
https://arxiv.org/abs/2602.00398

Fast and Lossless BitNet b1.58 Inference on CPUs
https://github.com/microsoft/BitNet

Recursive Language Models
https://arxiv.org/abs/2512.24601v1

https://blog.google/technology/developers/functiongemma/
https://blog.google/technology/developers/t5gemma-2/

FunctionGemma: Bringing bespoke function calling to the edge
https://blog.google/technology/developers/functiongemma/

Tokenization in Transformers v5: Simpler, Clearer, and More Modular
https://huggingface.co/blog/tokenizers

NVIDIA Nemotron 3 Family of Models
https://research.nvidia.com/labs/nemotron/Nemotron-3/?ncid=ref-inor-399942

MiniMax-M2, a model built for Max coding & agentic workflows
https://github.com/MiniMax-AI/MiniMax-M2

Continuous Autoregressive Language Models
https://huggingface.co/papers/2510.27688

Belief in the Machine: Investigating Epistemological Blind Spots of Language Models
https://github.com/suzgunmirac/belief-in-the-machine

Less is More: Recursive Reasoning with Tiny Networks
https://github.com/SamsungSAILMontreal/TinyRecursiveModels

Language Models are Injective and Hence Invertible
https://huggingface.co/papers/2510.15511

Teaching LLMs to Plan: Logical Chain-of-Thought Instruction Tuning for Symbolic Planning
https://arxiv.org/abs/2509.13351

Extracting alignment data in open models
https://arxiv.org/abs/2510.18554

RND1: Scaling Diffusion Language Models
https://github.com/RadicalNumerics/RND1

CS 329X: Human-Centered LLMs
https://web.stanford.edu/class/cs329x

GEPA: System Optimization through Reflective Text Evolution
https://github.com/gepa-ai/gepa

LLM-JEPA: Large Language Models Meet Joint Embedding Predictive Architectures
https://arxiv.org/abs/2509.14252

Regression Language Models for Code
https://arxiv.org/abs/2509.26476

Learning to Reason for Hallucination Span Detection
https://arxiv.org/abs/2510.02173

Understanding In-context Learning of Addition via Activation Subspaces
https://arxiv.org/abs/2505.05145

Prompt optimization can enable AI control research
https://www.lesswrong.com/posts/bALBxf3yGGx4bvvem/prompt-optimization-can-enable-ai-control-research