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Papers that changed AI, with a plain-language explainer for each.
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- 2026
GLM-5: from Vibe Coding to Agentic Engineering
arXiv
Zhipu's GLM-5 report describes a 744-billion-parameter mixture-of-experts model built on sparse attention and asynchronous reinforcement learning, aimed at moving AI-assisted programming from vibe coding toward agentic engineering.
- 2026
Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces
arXiv
Terminal-Bench 2.0 is a curated benchmark of 89 hard tasks carried out in computer terminal environments, built to measure how well AI agents handle realistic command-line work; frontier models and agents score below 65% on it.
- 2025
Kimi K2: Open Agentic Intelligence
arXiv
Kimi K2 is Moonshot AI's trillion-parameter mixture-of-experts model, pre-trained with the MuonClip optimizer and post-trained on large-scale agentic data plus reinforcement learning; the report reports state-of-the-art results among open-source non-thinking models and releases base and post-trained checkpoints.
- 2024
SAM 2: Segment Anything in Images and Videos
arXiv
SAM 2 is a Meta FAIR foundation model that segments objects in images and videos from simple prompts such as points, boxes or masks, released together with a large video segmentation dataset and training code.
- 2024
τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains
arXiv
τ-bench is a benchmark, submitted to arXiv in June 2024, that tests language agents in dynamic conversations with a simulated user, with domain-specific API tools and policy rules, and scores them by database state rather than by the text of their replies.
- 2024
OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments
arXiv
OSWorld is a benchmark and environment for testing multimodal agents on 369 real computer tasks in Ubuntu, Windows and macOS; humans complete over 72.36% of the tasks, while the best model reaches only 12.24%.
- 2023
GAIA: a benchmark for General AI Assistants
arXiv
GAIA is a benchmark of real-world questions that are easy for humans but hard for AI assistants, testing reasoning, multimodality, web browsing, and tool use. It compares AI progress against average human performance.
- 2023
SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
arXiv
A benchmark of 2,294 real software engineering problems taken from GitHub issues and the pull requests that resolved them, in which a model must edit a codebase to fix an issue; on release the best model solved under 2% of them.
- 2023
WebArena: A Realistic Web Environment for Building Autonomous Agents
arXiv
WebArena is a reproducible environment of four fully functional websites — e-commerce, a social forum, collaborative software development and content management — built for testing language-guided web agents. Its best GPT-4 agent completed 14.41% of tasks; humans completed 78.24%.[^1]
- 2023
Segment Anything
arXiv 2304.02643
A 2023 paper that introduces the Segment Anything project: a promptable segmentation task, a model, and a dataset of over 1 billion masks on 11 million images, all released to advance foundation models for computer vision.
- 2023
Toolformer: Language Models Can Teach Themselves to Use Tools
arXiv
A 2023 paper by Schick, Dwivedi-Yu, Dessì and co-authors showing that a language model can teach itself when to call a calculator, search engine, Q&A system, translation system or calendar, and how to use the results.
- 2022
ReAct: Synergizing Reasoning and Acting in Language Models
arXiv
ReAct is a prompting method that has a language model interleave written reasoning with calls to outside tools or environments, improving question answering, fact verification and interactive decision-making.
- 2021
Learning Transferable Visual Models From Natural Language Supervision (CLIP)
ICML
CLIP is an OpenAI model trained on 400 million image-text pairs to map pictures and sentences into one shared space, so it can classify images zero-shot from a written description.
- 2020
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (ViT)
arXiv
A 2020 paper showing that a plain Transformer applied directly to sequences of image patches, with no convolution at all, can match state-of-the-art convolutional networks on image classification.
- 2020
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
arXiv 2010.11929 (ICLR 2021)
A 2020 paper by Alexey Dosovitskiy and colleagues showing that a pure Transformer applied directly to sequences of image patches — the Vision Transformer, or ViT — matches state-of-the-art convolutional networks on image classification when pre-trained on large amounts of data.
- 2020
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (RAG)
arXiv
A 2020 paper that pairs a pre-trained seq2seq model with a dense Wikipedia index fetched by a neural retriever, setting state of the art on three open-domain QA tasks and producing more factual text than a parametric-only baseline.
- 2020
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
arXiv
A 2020 paper by Mildenhall, Srinivasan, Tancik, Barron, Ramamoorthi and Ng that represents a scene as a neural radiance field: a network mapping 5D coordinates to density and colour, optimized from a sparse set of images with known camera poses.
- 2018
A Style-Based Generator Architecture for Generative Adversarial Networks
arXiv
This 2018 paper introduces a style-based GAN generator that separates high-level attributes from stochastic variation and allows scale-specific control, improving distribution quality, interpolation, and disentanglement.
- 2018
Large Scale GAN Training for High Fidelity Natural Image Synthesis
arXiv
A 2018 paper by Andrew Brock, Jeff Donahue and Karen Simonyan that trained generative adversarial networks at the largest scale attempted at the time, introduced BigGANs and set a new state of the art in class-conditional image synthesis.
- 2017
Progressive Growing of GANs for Improved Quality, Stability, and Variation
arXiv
A 2017 paper by Karras, Aila, Laine and Lehtinen that trains generative adversarial networks by growing the generator and discriminator from low to high resolution, improving stability, quality and variation.