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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.
- 2024
LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code
arXiv
A benchmark for code LLMs that keeps collecting fresh contest problems from LeetCode, AtCoder and CodeForces, so that scores reflect ability rather than memorised test data, and that also tests code repair and execution reasoning.
- 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.
- 2021
Program Synthesis with Large Language Models (MBPP)
arXiv
A 2021 arXiv paper by Austin, Odena, Nye and eight other authors that introduced MBPP, a benchmark of 974 short Python programming tasks, and measured how language models from 244M to 137B parameters turn natural-language descriptions into working code.
- 2021
Evaluating Large Language Models Trained on Code (Codex / HumanEval)
arXiv
A 2021 paper from OpenAI introduces Codex, a GPT model fine-tuned on public GitHub code, and HumanEval, an evaluation set that judges generated Python programs by whether they work rather than by how closely they match a reference.
- 2017
Proximal Policy Optimization Algorithms
arXiv
A 2017 paper by Schulman, Wolski, Dhariwal, Radford and Klimov that introduced proximal policy optimization (PPO), a reinforcement learning method that is much simpler to implement and tune and became OpenAI's default RL algorithm.
- 1990
Elephants Don't Play Chess
Rodney Brooks's 1990 paper argued for building artificial intelligence from the ground up, with ongoing physical interaction with the environment as the main constraint on design, rather than through the abstract manipulation of symbols that had dominated the field for roughly thirty years.
- 1988
ALVINN: An Autonomous Land Vehicle in a Neural Network
ANIPS11
A 3-layer back-propagation network that takes camera and laser range finder images and outputs the direction to steer, trained on simulated road images and tested on Carnegie Mellon's autonomous navigation test vehicle.
- 1969
Perceptrons: An Introduction to Computational Geometry
A 1969 book by Marvin Minsky and Seymour Papert that proved mathematical limits on what single-layer perceptrons can compute — notably that they cannot compute parity or connectedness — and has long been credited with steering research away from neural networks.
- 1966
ELIZA - A Computer Program for the Study of Natural Language Communication Between Man and Machine
Communications of the ACM 9(1)
A January 1966 paper by Joseph Weizenbaum presenting ELIZA, a program on MIT's MAC time-sharing system that used keyword-triggered decomposition and reassembly rules to enable certain natural-language conversations between people and computers.
- 1961
Steps Toward Artificial Intelligence
Proceedings of the IRE 49(1)
Marvin Minsky's 1961 survey in Proceedings of the IRE organized heuristic programming into five problem areas — search, pattern recognition, learning, planning and induction — and argued that machines would need to build models of their environments.
- 1959
Some Studies in Machine Learning Using the Game of Checkers
IJRD
A. L. Samuel's 1959 paper reporting experiments in which a checkers program, given only the rules of the game and an imperfect set of parameters, learned in 8 to 10 hours of playing time to beat the person who wrote it.
- 1959
Programs with Common Sense
PSMTPNPL
John McCarthy's 1959 paper proposing the advice taker, a program that would solve problems by manipulating sentences in formal languages and act when it deduced an imperative — an early statement of logical AI.
- 1950
Computing Machinery and Intelligence
Mind
In 1950 Alan Turing replaced the question "Can machines think?" with the imitation game, now called the Turing test, and answered the main objections to machine intelligence one by one.
- 1943
A Logical Calculus of the Ideas Immanent in Nervous Activity
Bull. Math. Biophys.
In 1943 Warren McCulloch and Walter Pitts described a simplified model of a neuron and showed that networks of such units can carry out logical reasoning, an early link between brain models and computation.
- 1936
On Computable Numbers, with an Application to the Entscheidungsproblem
Proc. London Math. Soc.
Alan Turing's 1936 paper defined computable numbers through an abstract machine, now called the Turing machine, and showed that no general method can decide whether any given mathematical statement is provable.