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Wednesday, May 20, 2026 Lion (optimizer) v4 Lion (EvoLved Sign Momentum) is a stochastic optimizer for training deep neural networks, introduced by researchers at Google in the February 2023 paper "Symbolic Discovery of Optimization Algorithms" by… rsLoRA (Rank-Stabilized LoRA) v4 rsLoRA (rank-stabilized LoRA) is a one-line modification to the low-rank adaptation fine-tuning method for large neural networks. VeRA (Vector-based Random Matrix Adaptation) v5 Vector-based Random Matrix Adaptation (VeRA) is a parameter-efficient fine-tuning (PEFT) method for adapting large pretrained neural networks to downstream tasks while training only a tiny fraction of the… LoftQ v3 LoftQ (short for LoRA-Fine-Tuning-aware Quantization) is a quantization and initialization framework for large language models that jointly quantizes a pre-trained backbone and initializes the attached… LongLoRA v4 LongLoRA is a parameter-efficient fine-tuning technique that extends the context window of pre-trained large language models with substantially lower computation than full fine-tuning. GaLore (Gradient Low-Rank Projection) v4 GaLore (Gradient Low-Rank Projection) is a memory-efficient training strategy for large neural networks that projects each weight matrix's gradient into a low-rank subspace, computes the Adam-style optimizer… DoRA (Weight-Decomposed Low-Rank Adaptation) v6 DoRA (Weight-Decomposed Low-Rank Adaptation) is a parameter-efficient fine-tuning (PEFT) method for large neural networks introduced in February 2024 by researchers from NVIDIA, the Hong Kong University of… HuggingFace PEFT v4 PEFT (Parameter-Efficient Fine-Tuning) is an open-source Python library from Hugging Face that adapts large pretrained models to new tasks by training only a small set of added or selected parameters, often… HuggingFace TRL v5 TRL (Transformer Reinforcement Learning, now stylized as Transformers Reinforcement Learning) is an open-source Python library maintained by Hugging Face for post-training large language models with… LLaMA-Factory v4 LLaMA-Factory is an open-source unified framework for the efficient fine-tuning of large language models (LLMs) and vision-language models (VLMs). Qwen2.5-VL v4 Qwen2.5-VL is a series of open-weight vision-language models released on 26 January 2025 by the Qwen team at Alibaba (Alibaba Cloud), succeeding the earlier Qwen2-VL family. Unsloth v5 Unsloth is an open-source Python library that fine-tunes large language models up to two times faster while using up to 70 percent less GPU memory and, in its own words, with "no accuracy loss". Axolotl v5 Axolotl is a free and open source framework for fine-tuning and post-training large language models, written in Python and driven entirely by a single YAML configuration file. InternVL v5 InternVL is a family of open-source multimodal large language models developed by the OpenGVLab research group at the Shanghai Artificial Intelligence Laboratory in collaboration with academic partners… Molmo v3 Molmo is a family of open-weight, open-data vision-language models (VLMs) released by the Allen Institute for AI (Ai2) on 25 September 2024. Florence-2 v4 Florence-2 is a vision foundation model developed by Microsoft Research that handles a wide range of computer vision and vision-language tasks through a single unified SAM 2 v4 SAM 2 (Segment Anything Model 2) is a promptable visual segmentation model for both images and video developed by Meta AI and released on 29 July 2024. DINOv3 v5 DINOv3 is a family of self-supervised computer vision foundation models released by Meta AI in August 2025. DINOv2 v7 DINOv2 is a family of self-supervised Vision Transformer models released by Meta AI Research in April 2023 that produces general-purpose visual features transferring to many downstream tasks without… SigLIP v6 SigLIP (Sigmoid Loss for Language-Image Pre-training) is a family of vision-language encoders developed by researchers at Google DeepMind that pre-trains image and text encoders by treating each image-text… Strawberry (OpenAI codename) v4 Strawberry was the internal codename used at OpenAI for the research program that produced the o-series of reasoning models, most notably OpenAI o1. Sonauto v4 Sonauto is a generative artificial intelligence music platform that converts text prompts, lyrics, and melody inputs into complete songs with vocals and instrumentation. Cruise (self-driving) v4 Cruise (legally Cruise LLC, formerly Cruise Automation) was an American self-driving vehicle company that operated as a subsidiary of General Motors from 2016 until early 2025, when GM acquired full ownership… Boomy v5 Boomy is a generative artificial intelligence music platform that lets people without musical training assemble original songs in a web browser and publish them, under their own artist name, to streaming… Limitless AI v4 Limitless AI is an American personal artificial intelligence company best known for the Limitless Pendant, a small clip-on wearable that captures ambient conversational audio and pipes transcripts, action… Synchron v3 Synchron is a clinical-stage neurotechnology company developing the Stentrode, a minimally invasive, endovascular brain-computer interface (BCI) intended to restore digital communication and device control for… comma.ai v5 comma.ai, Inc. is an American advanced driver-assistance technology company headquartered in San Diego, California. SWE-agent v4 SWE-agent is an open-source autonomous software engineering agent created by the Princeton NLP group (Princeton Language and Intelligence, with co-authors from Stanford) and first released on April 2, 2024. OpenAI Assistants API v4 The OpenAI Assistants API is a stateful, server-managed application programming interface that OpenAI introduced on November 6, 2023, at its first DevDay developer conference, letting developers build… Google Vertex AI v4 Google Vertex AI is the unified machine learning and generative artificial intelligence platform offered by Google Cloud, announced at Google I/O on May 18 Anthropic Message Batches API v3 The Message Batches API (commonly referred to as the Anthropic Batches API) is an asynchronous bulk processing endpoint offered by Anthropic for its Claude family of large language models. RFM-1 (Robotics Foundation Model) v4 RFM-1 (Robotics Foundation Model 1) is an 8 billion parameter multimodal transformer for robotic manipulation announced by Covariant on March 11, 2024 at the MODEX 2024 trade show in Atlanta. F5-TTS v4 F5-TTS (short for "A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching") is an open-source text-to-speech and zero-shot voice cloning model released in October 2024 by researchers from… SIMA (DeepMind) v4 SIMA (Scalable Instructable Multiworld Agent) is a family of generalist embodied AI agents developed by Google DeepMind that follow free-form natural-language instructions to act in a wide range of… AG-UI Protocol v4 The AG-UI Protocol (Agent-User Interaction Protocol) is an open, event-based protocol that standardizes how AI agents communicate with user-facing applications. GLM-4-Voice v4 GLM-4-Voice is an open-weights end-to-end speech-to-speech large language model released in October 2024 by Zhipu AI together with the Knowledge Engineering Group (KEG) at Tsinghua University. SimPO v4 SimPO (Simple Preference Optimization) is a reference-free offline preference learning algorithm for aligning large language models with human preferences. DiLoCo v5 DiLoCo (Distributed Low-Communication training) is a distributed optimization algorithm for neural networks introduced by Google DeepMind in November 2023 to train large language models across loosely… Megatron-LM v5 Megatron-LM is NVIDIA's open-source framework for training very large transformer language models across GPU clusters CosyVoice v4 CosyVoice is a family of open-source multilingual neural text-to-speech (TTS) and voice cloning models developed by the Tongyi Speech Lab (Tongyi SpeechTeam) at Alibaba Group and released under the Apache 2.0… PiSSA v4 PiSSA (Principal Singular values and Singular vectors Adaptation) is a parameter-efficient fine-tuning method for large language models that initializes LoRA-style low-rank adapter matrices from the dominant… Model stealing v4 Model stealing (also known as model extraction, model functionality extraction, or model theft) is an adversarial machine learning attack in which an adversary queries a black-box model through its prediction… Long-context language models v8 Long-context language models are large language models engineered to accept and reason over inputs far larger than the few-thousand-token windows used by early transformer systems, with frontier models in 2026… The Bitter Lesson v3 The Bitter Lesson is a short essay by Canadian-American computer scientist Richard S. Sutton, published on his personal blog at incompleteideas.net on March 13, 2019. Linear Attention v4 Linear attention is a family of sub-quadratic attention mechanisms that replaces the softmax dot-product operation of standard Transformer self-attention with a feature-map-based factorization Hyena v5 Hyena is a sub-quadratic, attention-free neural sequence operator that replaces the self-attention operator of the Transformer with a recurrence of long, implicitly parameterized convolutions and… Patchscopes v4 Patchscopes is an interpretability framework for inspecting hidden representations of large language models by patching an internal activation from a source computation into a separate target inference whose… Sparse Coding v4 Sparse coding is a representation learning principle in which a signal is encoded as a linear combination of a small number of elements drawn from a (usually overcomplete) dictionary of atoms. Induction Heads v4 Induction heads are a circuit pattern in Transformer language models in which a small set of attention heads, typically spread across two layers, perform an in-context "match and copy" operation that completes… Linear Probes v5 A linear probe is a small linear classifier (or linear regressor) trained on the frozen internal activations of a neural network to test whether a particular concept, property, or label is linearly decodable… SynthID v5 SynthID is a family of digital watermarking technologies developed by Google DeepMind for marking and identifying content generated by generative AI systems. MMDiT (Multimodal Diffusion Transformer) v4 MMDiT (Multimodal Diffusion Transformer, sometimes written MM-DiT) is a transformer architecture for text-conditioned image generation that gives image tokens and text tokens their own separate weights but… Flow Matching v4 Flow Matching is a simulation-free training framework for generative models that fits a time-dependent velocity field to transport samples from a source distribution (typically a standard Gaussian) to a data… SDXL (Stable Diffusion XL) v4 SDXL, short for Stable Diffusion XL, is an open-weights latent text-to-image diffusion model released by Stability AI on 26 July 2023, built around a 2.6 billion parameter U-Net backbone, two text encoders… Rectified Flow v5 Rectified Flow is a generative modeling framework that learns a transport ordinary differential equation between two probability distributions by regressing a velocity field along straight-line interpolations… Normal Computing v7 Normal Computing is an American deep-tech startup developing thermodynamic computing hardware and AI-driven electronic design automation (EDA) software. Lepton AI v3 Lepton AI was an American AI cloud company, founded in 2023, that built a cloud-native inference platform for serving large language models, generative image models, and other AI workloads on NVIDIA GPUs. fal.ai v5 fal.ai (legally Features and Labels, Inc., often stylized fal) is a San Francisco-based artificial intelligence company that operates a specialized inference platform for generative-media models, including… OctoAI v4 OctoAI (originally OctoML) was an American artificial intelligence infrastructure company that operated a generative-AI inference platform and, before its pivot Sleeper Agents (paper) v4 "Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training" is a January 2024 Anthropic research paper (arXiv:2401.05566, submitted 10 January 2024) that deliberately trained large language… Persona vectors v4 Persona vectors are single linear directions in the activation space of a large language model that correspond to high level character traits such as evil, sycophancy, or a propensity to hallucinate. On the Biology of a Large Language Model v4 On the Biology of a Large Language Model is a mechanistic interpretability paper published by Anthropic on March 27, 2025, in the Transformer Circuits Thread. Scaling Monosemanticity v4 Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet is the May 21 Towards Monosemanticity v5 Towards Monosemanticity is an October 2023 mechanistic interpretability paper from Anthropic that used a sparse autoencoder to decompose the internal activations of a small language model into thousands of… Palisade Research v4 Palisade Research is a United States 501(c)(3) nonprofit research organization that studies the offensive capabilities of contemporary artificial intelligence systems in order to demonstrate, document, and… Vision-language-action model v6 A vision-language-action model (VLA model, or VLA) is a machine-learning model for robotics that uses visual observations and a natural-language instruction to generate actions a robot can execute. Mobile ALOHA v3 Mobile ALOHA is an open-source, low-cost system for collecting bimanual mobile manipulation data and learning household tasks from it, developed at Stanford University by Zipeng Fu, Tony Z. Zhao π₀ (pi-zero) v3 π₀ (pronounced pi-zero and sometimes written pi0 or pizero) is a vision-language-action model (VLA) developed by the robotics foundation-model startup Physical Intelligence AG2 (framework) v4 AG2 (formerly AutoGen) is an open-source Python framework for building applications composed of multiple cooperating large-language-model (LLM) agents. smolagents v4 smolagents is an open source Python library for building agents powered by large language models (LLMs), released by hugging face on 30 December 2024. Magentic-One v3 Magentic-One is a generalist multi-agent system released by Microsoft Research's AI Frontiers lab in November 2024 to autonomously solve complex, multi-step tasks across the open web, local file systems, and… AI safety via debate v3 AI safety via debate is a proposed approach to scalable oversight in which two artificial agents take turns presenting short statements about a question or proposed action while a human (or weaker AI) judge… Instrumental convergence v5 Instrumental convergence is a hypothesis in AI safety holding that a wide range of sufficiently capable agents, when pursuing almost any final goal, will rationally adopt a common set of intermediate subgoals… Alignment faking v5 Alignment faking is when an AI model strategically complies with (or appears to share) its training objective while it believes it is being observed or trained, in order to avoid having its existing… Agentic misalignment v3 Agentic misalignment is a term coined by Anthropic in a June 2025 research release for cases in which a goal-directed large language model (LLM), placed in an autonomous business setting with access to tools… Model organisms of misalignment v5 Model organisms of misalignment is a research methodology in Anthropic's alignment program AI control v4 AI control is a research paradigm in technical AI safety that designs and evaluates deployment-time safety protocols under the explicit assumption that the underlying AI model may be actively trying to subvert… Eliciting latent knowledge v4 Eliciting latent knowledge (ELK) is an open problem in AI alignment formulated by Paul Christiano, Ajeya Cotra, and Mark Xu at the Alignment Research Center (ARC) and introduced in a December 2021 technical… Recursive reward modeling v3 Recursive reward modeling (RRM) is a proposed approach to the scalable oversight problem in AI alignment Scalable oversight v4 Scalable oversight is the AI safety problem of how humans can reliably supervise, evaluate, and provide training signal to artificial intelligence systems whose capabilities approach, equal, or exceed those of… Sandbagging (artificial intelligence) v4 Sandbagging, in the context of AI safety, refers to the strategic and intentional underperformance of an AI system on a capability evaluation or specific task, typically to hide a capability from human… Gradient hacking v4 Gradient hacking is a hypothesised failure mode of supervised and reinforcement-learning systems in which a sufficiently capable Specification gaming v4 Specification gaming is the phenomenon in which an optimizer satisfies the literal specification of an objective without producing the outcome that the designer actually wanted. 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Kadrey v. Meta Platforms, Inc. is a putative class action lawsuit filed in 2023 by a group of book authors against Meta Platforms, Inc., alleging that Meta infringed their copyrights by training its LLaMA…
