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2,489 articles changed in the last 30 days; 52 are new in the last 7 days. Dates show when an article's text last changed, so link and formatting passes do not move it up the list. Page 46 of 50.

All updates New articles

Tuesday, June 9, 2026

Global-MMLUv5Global-MMLU is a multilingual evaluation benchmark that extends the MMLU question-answering dataset across 42 languages, with designated subsets labeled culturally sensitive (CS) and culturally agnostic (CA).
Claude Govv4Claude Gov is a custom set of large language models that Anthropic built for United States national security customers, announced on June 5, 2025.
Interim Measures for the Management of Generative AI Servicesv6The Interim Measures for the Management of Generative Artificial Intelligence Services (Chinese: 生成式人工智能服务管理暂行办法
Capability overhangv7Capability overhang is a term used in ai safety and AI policy discourse to describe a situation in which the latent capabilities of a deployed AI system
BrainChip Akidav6Akida is a family of fully digital, event-based neuromorphic processor intellectual property (IP) cores and reference chips developed by BrainChip Holdings Ltd
Albert Guv10Albert Gu is an American computer scientist, Assistant Professor of Machine Learning at Carnegie Mellon University, and co-founder and Chief Scientist of Cartesia AI.

Monday, June 8, 2026

Latent reasoning via recurrent depth (Huginn)v3Latent reasoning via recurrent depth is an approach to scaling a language model's test-time computation by iterating a recurrent transformer block in latent (hidden) space
IBM Granite 4.0v4IBM Granite 4.0 is the fourth generation of IBM's Granite family of open-weight enterprise large language models, released on October 2, 2025.
Apertusv3Apertus is a fully open, multilingual large language model developed in Switzerland and released on September 2, 2025, by EPFL, ETH Zurich, and the Swiss National Supercomputing Centre (CSCS) under the Swiss…
Rivosv4Rivos, Inc. was a fabless semiconductor startup based in Santa Clara, California, that designed server-class system-on-chip (SoC) processors pairing high-performance RISC-V CPU cores with a data-parallel…
Remote Labor Indexv4

NewerPage 46 of 50Older

The Remote Labor Index (RLI) is an AI benchmark that measures how well AI agents can complete real, paid remote knowledge work end to end.

Tilly Norwoodv4Tilly Norwood is a fully AI-generated synthetic performer, marketed as an "AI actress," created in 2025 by the Dutch comedian, actress, and producer Eline Van der Velden through her production company…

Studio Ghibli ChatGPT momentv4The "Studio Ghibli ChatGPT moment" refers to a viral internet trend that erupted in late March 2025, after OpenAI released a new native image generation capability inside ChatGPT, powered by the GPT-4o model.

MIT "GenAI Divide" report (2025)v4The "GenAI Divide" report, formally titled The GenAI Divide: State of AI in Business 2025, is a research report published in July 2025 by MIT NANDA, an initiative based at the MIT Media Lab.

Grok "MechaHitler" incidentv4The Grok "MechaHitler" incident was a content-moderation and AI-safety failure that occurred on July 8 to 9, 2025, when Grok, the chatbot built by xAI and integrated into the social platform X (formerly…

Reflection 70B controversyv4The Reflection 70B controversy was an open-source AI credibility episode that began on September 5, 2024, when Matt Shumer, co-founder and chief executive of OthersideAI (the company behind the AI writing…

Perplexity AI copyright lawsuitsv4The Perplexity AI copyright lawsuits are a cluster of lawsuits, cease-and-desist demands, and public disputes brought against Perplexity AI by news publishers, reference publishers, and online platforms over…

Disney & Universal v. Midjourneyv4Disney and Universal v. Midjourney is a copyright infringement lawsuit filed on June 11, 2025, in which The Walt Disney Company and Comcast's NBCUniversal jointly sued the generative AI image service…

Florida v. OpenAIv4Florida v. OpenAI is a civil enforcement lawsuit filed on June 1, 2026, by the State of Florida, through Attorney General James Uthmeier, against OpenAI and its chief executive Sam Altman.

Musk v. Altman (Musk v. OpenAI)v3Musk v. Altman, also referred to as Musk v. OpenAI, is a lawsuit filed by the entrepreneur Elon Musk accusing OpenAI and its chief executive Sam Altman of abandoning the company's founding mission as an open…

AI at the 2025 ICPC World Finalsv5In September 2025, at the 49th International Collegiate Programming Contest (ICPC) World Finals in Baku, Azerbaijan, two leading artificial-intelligence laboratories reported that their general-purpose…

DeepSeek market crash (Jan 2025)v6The DeepSeek market crash of January 2025 was a sharp, single-day selloff in United States and global technology stocks on Monday, January 27, 2025, triggered by the rapid rise of the Chinese AI lab DeepSeek…

Scale SEAL Leaderboardsv3The SEAL Leaderboards are a set of expert-curated, contamination-resistant evaluation leaderboards for frontier large language models, produced by the Safety, Evaluations and Alignment Lab (SEAL) at Scale AI.

Future of Life Institute AI Safety Indexv3The Future of Life Institute AI Safety Index (often shortened to the AI Safety Index or FLI AI Safety Index) is a periodic "report card" published by the Future of Life Institute (FLI) that grades the leading…

State of AI Reportv4The State of AI Report is a free, annual review of progress in artificial intelligence, published every year since 2018.

Stanford HAI AI Index Reportv4The AI Index Report is an annual, independent, data-driven publication that tracks, distills, and visualizes trends in artificial intelligence across research and development, technical performance, the…

Data Provenance Initiativev3The Data Provenance Initiative (DPI) is a volunteer-led, multi-institution research collective that audits and documents the licenses, sources, creators, and consent status of the datasets most widely used to…

OpenThoughtsv6OpenThoughts is an open-source initiative and a series of datasets of verified reasoning traces created to train open reasoning models.

WildChatv5WildChat is a large public corpus of real conversations between human users and ChatGPT, released by researchers at the Allen Institute for AI (AI2) and Cornell University.

FineWeb-2v4FineWeb-2 (also written FineWeb2) is a massively multilingual web pretraining dataset released by Hugging Face in December 2024.

LIBEROv4LIBERO ("LIfelong learning BEnchmark on RObot manipulation tasks") is an AI benchmark for studying knowledge transfer in lifelong robot learning.

MedHELMv5MedHELM (Holistic Evaluation of Large Language Models for Medical Tasks) is a benchmark and evaluation framework that measures how well large language models perform on realistic clinical work.

FinanceBenchv4FinanceBench is an AI benchmark for open-book financial question answering, designed to test whether large language models can answer the kinds of questions a financial analyst asks about a publicly traded…

OpenAI MRCR (Multi-Round Co-reference Resolution)v4OpenAI MRCR (Multi-Round Co-reference Resolution) is a long-context evaluation dataset published by OpenAI that measures a language model's ability to distinguish between multiple near-identical "needles"…

LongFact / SAFEv4LongFact and SAFE are a paired benchmark and evaluation method for measuring the long-form factuality of large language models, introduced by researchers at Google DeepMind and Stanford University in the 2024…

FActScorev4FActScore (Factual precision in Atomicity Score) is an evaluation method and metric, introduced in 2023, for measuring the factual precision of long-form text generated by large language models.

T2I-CompBenchv5T2I-CompBench is an AI benchmark for evaluating compositional text-to-image generation, introduced in the paper "T2I-CompBench: A Comprehensive Benchmark for Open-world Compositional Text-to-image Generation"…

BELEBELEv4Belebele is a multiple-choice machine reading comprehension (MRC) AI benchmark that is fully parallel across 122 language variants, meaning the same questions, passages, and answer choices are translated into…

RepoBenchv4RepoBench is an AI benchmark for repository-level code auto-completion, introduced in the 2023 paper "RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems" by Tianyang Liu, Canwen Xu, and…

GSO (Global Software Optimization Bench)v4GSO (Global Software Optimization), also styled as a benchmark of "Challenging Software Optimization Tasks for Evaluating SWE-Agents," is an AI benchmark that measures whether AI agents and language models can…

SWE-rebenchv4SWE-rebench is a continuously refreshed, contamination-resistant AI benchmark and public leaderboard for evaluating AI agents on real-world software engineering tasks.

CRMArena / CRMArena-Prov4CRMArena is an AI benchmark for evaluating large language model agents on professional customer relationship management (CRM) tasks inside a realistic, schema-faithful Salesforce environment.

MultiChallengev4MultiChallenge is an AI benchmark for evaluating large language models on realistic multi-turn conversations.

TheAgentCompanyv5TheAgentCompany is an AI benchmark that evaluates AI agents on long-horizon, economically valuable knowledge work inside a self-hosted simulation of a small software company.

LAB-Benchv4LAB-Bench (the Language Agent Biology Benchmark) is an AI benchmark of more than 2,400 multiple-choice questions built to measure how well language models and AI agents can perform practical biology research…

ChemBenchv4ChemBench is an automated AI benchmark that measures the chemical knowledge, reasoning, and safety judgment of large language models and compares their performance against expert human chemists.

ScienceAgentBenchv4ScienceAgentBench is an AI benchmark for evaluating whether language model agents can perform real, data-driven scientific analysis by writing and executing code.

PutnamBenchv4PutnamBench is a benchmark for evaluating automated theorem proving systems, especially neural and large-language-model-based provers, on competition mathematics.

OlympiadBenchv3OlympiadBench is an AI benchmark of 8,476 Olympiad-level mathematics and physics problems, designed to test the advanced scientific reasoning of large language models and large multimodal models.

MMLU-Reduxv4MMLU-Redux is a manually re-annotated, error-corrected subset of the Massive Multitask Language Understanding (MMLU) benchmark

AGIEvalv4AGIEval is an AI benchmark for evaluating foundation models on tasks that were originally designed for, and taken by, humans.

MoE load-balancing lossv5The MoE load balancing loss is an auxiliary training objective used in sparse mixture of experts (MoE) neural networks to keep work spread evenly across the expert sub-networks.

NLWebv4NLWeb (short for Natural Language Web) is an open project from Microsoft that makes it easy to add a natural-language conversational interface to a website, drawing on the site's own existing structured data…

RAPTORv4RAPTOR (Recursive Abstractive Processing for Tree-Organized Retrieval) is a retrieval method for retrieval-augmented generation introduced in a 2024 paper by researchers at Stanford University.

Corrective RAG (CRAG)v4Corrective Retrieval Augmented Generation (CRAG) is a method for improving the robustness of retrieval-augmented generation (RAG) when the underlying retrieval step returns irrelevant, incomplete, or factually…

Self-RAGv4Self-RAG (Self-Reflective Retrieval-Augmented Generation) is a framework that trains a single large language model to adaptively decide when to retrieve external passages, to generate text grounded in those…

Sleep-time computev4Sleep-time compute is a technique for large language model inference in which a model uses idle periods, before any user query has arrived, to "think" about a known context offline and pre-compute a richer…

SDEditv4SDEdit (Stochastic Differential Editing) is a method for guided image synthesis and editing that turns a rough user guide, such as a stroke painting, a coarse collage, or a real photograph with edits pasted…

Prompt-to-Promptv4Prompt-to-Prompt is a training-free image editing technique for text-conditioned diffusion models that edits a generated image by manipulating the model's cross-attention maps when the text prompt is changed.

IP-Adapterv4IP-Adapter (short for Image Prompt Adapter) is a lightweight neural network module that adds image-prompt conditioning to a pretrained text-to-image diffusion model, allowing a reference image to guide…

Diffusion Forcingv4Diffusion Forcing is a training paradigm for sequence generative modeling introduced in 2024 that assigns each token in a sequence its own independent, randomly sampled noise level during training.

Show-ov4Show-o is a unified multimodal model, introduced in 2024, that handles both multimodal understanding and visual generation inside a single Transformer.

Transfusionv4Transfusion is a recipe, introduced by Meta in 2024, for training a single Transformer over a mixture of discrete text and continuous image data using two training objectives simultaneously: a…

MaskGITv6MaskGIT, short for Masked Generative Image Transformer, is an image-synthesis method introduced by Google Research in the 2022 paper "MaskGIT: Masked Generative Image Transformer" by Huiwen Chang, Han Zhang…

Masked Autoregressive (MAR) generationv4Masked Autoregressive (MAR) generation is an image-generation method introduced in the 2024 paper "Autoregressive Image Generation without Vector Quantization" by Tianhong Li, Yonglong Tian, He Li, Mingyang…

Visual Autoregressive modeling (VAR)v5Visual Autoregressive modeling (VAR) is an image generation paradigm, introduced in 2024, that reframes autoregressive image synthesis as coarse-to-fine "next-scale prediction" rather than the conventional…

Circuit Breakers (Representation Rerouting)v4Circuit Breakers are an AI safety method, introduced in 2024, that aims to make a large language model (LLM) or multimodal model robust to harmful generations by intervening directly on the model's internal…

Gemma Scopev5Gemma Scope is an open, comprehensive suite of sparse autoencoders (SAEs) released by Google DeepMind in 2024 to support mechanistic interpretability research on its open-weight Gemma 2 language models.

TopK SAEv5A TopK SAE (TopK sparse autoencoder) is a variant of sparse autoencoder that enforces sparsity by keeping only the K largest latent pre-activations for each input and zeroing all the rest

Position Interpolation (PI)v3Position Interpolation (PI) is a method for extending the context window of a pretrained large language model that uses rotary position embedding (RoPE).

Self-Extendv4Self-Extend (written SelfExtend in the original paper) is a training-free technique that lets a pretrained large language model process inputs much longer than the context window it was trained on, with no…

DeepNorm / DeepNetv5DeepNorm is a normalization and weight initialization scheme for Transformer networks that makes the training of very deep models stable.

Register tokens (Vision Transformers Need Registers)v4Register tokens are a small set of extra learnable tokens added to the input sequence of a Vision Transformer (ViT) so the network has a dedicated place to carry out internal, image-level computation.

PEER (Parameter Efficient Expert Retrieval / Mixture of a Million Experts)v5PEER, short for Parameter Efficient Expert Retrieval, is a neural network layer for Transformer models that replaces the dense feed-forward block with a sparse mixture of experts drawn from an extremely large…

Expert Choice routingv4Expert Choice routing (often abbreviated EC) is a routing method for mixture of experts (MoE) layers in neural networks, introduced by researchers at Google in 2022.

Soft MoEv3Soft MoE (Soft Mixture of Experts) is a fully differentiable variant of the sparse mixture of experts (MoE) layer.

Gated DeltaNetv5Gated DeltaNet is a linear attention sequence model that augments the delta rule, a key-value error-correction update used in the earlier DeltaNet architecture

H-Net (dynamic chunking)v3H-Net, short for Hierarchical Network, is a tokenizer-free neural sequence model that learns to segment raw bytes into content-adaptive "chunks" as part of ordinary end-to-end training, rather than relying on…

Mixture-of-Recursions (MoR)v4Mixture-of-Recursions (MoR) is a Transformer architecture, introduced in 2025, that unifies two previously separate strategies for building efficient language models: parameter sharing through recursion, and…

Hymbav4Hymba is a hybrid-head neural-network architecture for small language models introduced by NVIDIA researchers in November 2024.

Quantization-Aware Training (QAT)v5Quantization-aware training (QAT) is a model compression technique in which the effects of quantization are simulated during the training or fine-tuning of a neural network, so that the model learns parameter…

Contrastive decodingv5Contrastive decoding (CD) is a decoding strategy for text generation from a large language model that selects tokens by contrasting two models of different sizes.

Self-speculative decoding (LayerSkip)v4Self-speculative decoding is a family of speculative decoding methods that accelerate large language model (LLM) inference by using the target model itself, run in a cheaper reduced-depth mode

Prompt lookup decodingv4Prompt lookup decoding (PLD), also called n-gram speculative decoding, is an inference acceleration method for large language models that speeds up text generation without changing the model or its outputs.

Prefix caching (automatic prefix caching)v4Prefix caching is an inference optimization for large language model serving that stores and reuses the key-value (KV) cache computed for a shared prompt prefix, so that the prefill for that prefix is computed…

KV-cache quantizationv4KV cache quantization is a family of large language model inference optimizations that store the attention key and value (KV) cache in low-bit numeric formats, typically 2 to 4 bits per value

AQLM (Additive Quantization of Language Models)v3AQLM, short for Additive Quantization of Language Models, is a weight-only post-training quantization method that compresses the weights of a large language model to roughly 2 to 3 bits per parameter while…

SpinQuantv4SpinQuant is a post-training quantization method for large language models that inserts learned rotation matrices into a transformer network to make its weights, activations, and KV cache easier to represent…

Depth up-scaling (DUS)v5Depth up-scaling (DUS) is a model-scaling method that builds a deeper large language model by duplicating and stacking the layers of an existing pretrained Transformer checkpoint and then continuing to…

Sparse upcyclingv4Sparse upcycling is a technique for building a sparsely activated mixture of experts (MoE) model by initializing it from an already trained dense Transformer checkpoint

Selective Language Modeling (Rho-1)v4Selective Language Modeling (SLM) is a pretraining objective for language models that applies the training loss to only a chosen subset of tokens rather than to every token in the corpus.

DoReMiv4DoReMi (Domain Reweighting with Minimax Optimization) is a method for automatically choosing the proportions, or "domain weights," of each data source in a pretraining corpus for a large language model.

Instruction backtranslation (Humpback)v4Instruction backtranslation is a self-alignment method for generating instruction tuning data, introduced by researchers at Meta AI in the paper "Self-Alignment with Instruction Backtranslation," first posted…

Model soupsv5Model soups is a weight-averaging technique (a form of model merging) that combines several independently fine-tuned neural networks into a single model by averaging their parameters.

DARE (Drop And REscale)v3DARE (Drop And REscale) is a training-free preprocessing technique for model merging that sparsifies the parameter changes introduced by fine-tuning before those changes are combined across several models.

ReST / ReST-EM (Reinforced Self-Training)v5ReST (Reinforced Self-Training) is a family of self-training algorithms for large language models that improve a model by fine-tuning it on its own filtered outputs instead of on additional human-written data.

Quiet-STaRv4Quiet-STaR is a self-supervised training method that teaches a large language model to generate short, token-level internal "thoughts," or rationales, that help it predict the text that follows

VAPO (Value-based Augmented PPO)v4VAPO (Value-based Augmented Proximal Policy Optimization) is a reinforcement learning framework for training large language models on long chain-of-thought reasoning tasks.

DAPO (Decoupled Clip and Dynamic Sampling Policy Optimization)v3DAPO, short for Decoupled Clip and Dynamic sAmpling Policy Optimization, is an open-source reinforcement learning algorithm and training system for large language models, introduced in March 2025 by…

Group Sequence Policy Optimization (GSPO)v5Group Sequence Policy Optimization (GSPO) is a reinforcement learning algorithm for training large language models, introduced by the Qwen team at Alibaba in July 2025.