Interleaves chain-of-thought reasoning with tool-using actions in one LLM loop: the model plans, queries a source like Wikipedia, then revises from results. Cuts hallucination versus reasoning-only prompting and beats trained agents on interactive tasks.
AI Agent Papers2024
Treats the interface between an LM agent and a computer as a design variable. A custom agent-computer interface (ACI) with concise file-edit, repo-navigation, and test commands plus compact feedback reaches 12.5% pass@1 on SWE-bench, 87.7% on HumanEvalFix.
Reframes "can machines think?" as a concrete test: the imitation game, now the Turing test, where a machine passes if its typed replies are indistinguishable from a human's. Rebuts nine objections and backs machines that learn like children.
Models the brain probabilistically and proposes the perceptron: weighted threshold units that learn to classify patterns by adjusting connection strengths from examples, rather than storing fixed memories. The 1958 root of trainable neural networks.
David E. Rumelhart, Geoffrey E. Hinton +1University of California, San Diego, Carnegie Mellon University
Introduces the generalized delta rule — backpropagation — for training multi-layer networks with hidden units by gradient descent on output error, letting hidden layers learn internal representations that solve problems single-layer networks cannot.
Treats a network's weights as a noisy channel and penalizes the bits needed to describe them, formalizing the "bits-back" coding trick — an early variational argument later recognized as a conceptual ancestor of the VAE.
Reframes model selection as data compression: the best hypothesis is the one that lets you describe the data in the fewest bits. Walks through MDL twice — once conceptually, once with full math — turning Occam's razor into a usable inference principle.
The result that kicked off the deep learning era: in 2012 a deep CNN cut ImageNet top-5 error from 26% to 15%, showing that GPU-trained networks with ReLU and dropout could beat decades of hand-engineered computer vision features.
First model to learn control policies straight from raw Atari pixels, pairing a convolutional net with Q-learning and experience replay. One unchanged architecture played seven games, beating prior methods on six and a human expert on three.
Measures why complexity in closed systems rises then falls while entropy only climbs, using a coffee-and-cream cellular automaton. The key result: only interacting particles produce a transient complexity peak; non-interacting ones never do.
Frames generative modeling as a two-player game: a generator forges data while a discriminator learns to spot fakes, training both by backpropagation alone — no Markov chains, no inference networks. The adversarial pressure yields sharp samples.
First model to make a decoder dynamically focus on different source words instead of cramming a whole sentence into one fixed vector — the soft-alignment idea that became "attention" and, three years later, powered the Transformer.