Reference
Research Library
LIB-001Draftv0.1.0Reviewed 2026-10-15
An index of 120 foundational works (books, seminal papers and major reports) that support the encyclopedia, grouped by subject. Each row records who wrote the work, when and where it appeared, a persistent identifier, its subject, and the encyclopedia article IDs it supports. The index lists sources only; it makes no claims about their findings. Article IDs refer either to existing files in articles/ or to entries in docs/PLANNED_REGISTER.md.
#How entries were confirmed
- arXiv entries: title, author list and first-version year were read from the arXiv abstract page (
arxiv.org/abs/<id>). The year shown is the year of the arXiv identifier, that is, of first submission, which can differ from the year of a later peer-reviewed publication. - DOI entries: title, authors, year and venue were read from the Crossref record (
api.crossref.org/works/<doi>). Where the Crossref year is that of a volume or reprint, that year is shown (for example, Turing's 1936 paper appears as 1937 because the Crossref record dates the volume in which it was printed; the Krizhevsky et al. entry is the 2017 CACM reprint of the 2012 conference paper). - ISBN entries: bibliographic data were read from Open Library catalogue records. Where the catalogue record omitted co-authors, the author list was checked against the publisher's or authors' own book page (Sutton and Barto, and Goodfellow, Bengio and Courville). Edition and year are those of the ISBN shown.
- The Dartmouth proposal was confirmed on the AI Magazine page (AAAI), which lists McCarthy, Minsky, Rochester and Shannon.
- Author lists are copied in source order. Where a work has more than six listed authors, the first author is followed by "et al." and the number of authors the source lists. Titles keep the source wording; leading section numerals and all-capital styling were removed where the source record had them (for example, Turing 1950).
- Works that could not be confirmed against a source page were omitted rather than listed. This index is a bibliography, not an endorsement: inclusion does not rate a work's claims, which belong in article evidence matrices.
#Using the index
The "Supports" column is an editorial mapping from a work to the articles where it is a natural primary or background source. It does not mean a given article already cites the work. Fast-moving evidence (benchmark scores, model specifications, prices) is intentionally not recorded here; see MODEL-001, FACT-001, TIME-001 and FCST-REG-001.
#Computation, information and probability
| Author(s) | Year | Title | Venue / publisher | Identifier | Subject | Supports |
|---|---|---|---|---|---|---|
| A. M. Turing | 1937 | On Computable Numbers, with an Application to the Entscheidungsproblem | Proceedings of the London Mathematical Society | DOI 10.1112/plms/s2-42.1.230 | Computability; Turing machines | FOUND-004, HIST-001, BIO-TURING-001 |
| Warren S. McCulloch, Walter Pitts | 1943 | A logical calculus of the ideas immanent in nervous activity | The Bulletin of Mathematical Biophysics | DOI 10.1007/BF02478259 | Formal neuron models | HIST-001, HIST-003, TECH-NN-001, FOUND-013 |
| C. E. Shannon | 1948 | A Mathematical Theory of Communication | Bell System Technical Journal | DOI 10.1002/j.1538-7305.1948.tb01338.x | Information theory | FOUND-009, BIO-SHANNON-001, HIST-001 |
| A. L. Samuel | 1959 | Some Studies in Machine Learning Using the Game of Checkers | IBM Journal of Research and Development | DOI 10.1147/rd.33.0210 | Early machine learning; game playing | TECH-ML-001, HIST-009 |
| L. G. Valiant | 1984 | A theory of the learnable | Communications of the ACM | DOI 10.1145/1968.1972 | Computational learning theory | TECH-ML-001, FOUND-004, FOUND-007 |
| Judea Pearl | 1988 | Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference | Morgan Kaufmann | ISBN 9781558604797 | Probabilistic reasoning; Bayesian networks | FOUND-008, FOUND-003 |
| G. Cybenko | 1989 | Approximation by superpositions of a sigmoidal function | Mathematics of Control, Signals, and Systems | DOI 10.1007/BF02551274 | Universal approximation | TECH-NN-001, TECH-DL-001 |
| Kurt Hornik, Maxwell Stinchcombe, Halbert White | 1989 | Multilayer feedforward networks are universal approximators | Neural Networks | DOI 10.1016/0893-6080(89)90020-8 | Universal approximation | TECH-NN-001, TECH-DL-001 |
| Thomas M. Cover, Joy A. Thomas | 2006 | Elements of Information Theory | Wiley-Interscience (2nd ed.) | ISBN 9780471241959 | Information theory textbook | FOUND-009 |
| Sanjeev Arora, Boaz Barak | 2009 | Computational Complexity: A Modern Approach | Cambridge University Press | ISBN 9780521424264 | Computational complexity textbook | FOUND-004 |
#Philosophy of mind and the definition of intelligence
| Author(s) | Year | Title | Venue / publisher | Identifier | Subject | Supports |
|---|---|---|---|---|---|---|
| A. M. Turing | 1950 | Computing Machinery and Intelligence | Mind | DOI 10.1093/mind/LIX.236.433 | Turing test; machine intelligence | FOUND-005, HIST-001, BIO-TURING-001 |
| Thomas Nagel | 1974 | What Is It Like to Be a Bat? | The Philosophical Review | DOI 10.2307/2183914 | Subjective experience; consciousness | FOUND-005, PHIL-CONSC-001, FOUND-014 |
| John R. Searle | 1980 | Minds, brains, and programs | Behavioral and Brain Sciences | DOI 10.1017/S0140525X00005756 | Chinese Room; understanding | FOUND-005, FOUND-014, PHIL-CONSC-001 |
| Shane Legg, Marcus Hutter | 2007 | Universal Intelligence: A Definition of Machine Intelligence | Minds and Machines | DOI 10.1007/s11023-007-9079-x | Formal definition of intelligence | FOUND-002, ASI-DEF-001, AGI-DEF-001 |
| François Chollet | 2019 | On the Measure of Intelligence | arXiv preprint | arXiv:1911.01547 | Intelligence measurement; skill acquisition | FOUND-002, AGI-DEF-001, AGI-TEST-001, CAP-BENCH-001 |
| Emily M. Bender, Alexander Koller | 2020 | Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data | Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics | DOI 10.18653/v1/2020.acl-main.463 | Meaning and understanding in language models | FOUND-014, TECH-LLM-001, CAP-LIMITS-001 |
#History and early AI
| Author(s) | Year | Title | Venue / publisher | Identifier | Subject | Supports |
|---|---|---|---|---|---|---|
| Claude E. Shannon | 1950 | Programming a Computer for Playing Chess | The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science | DOI 10.1080/14786445008521796 | Computer chess | HIST-009, BIO-SHANNON-001 |
| John McCarthy, Marvin L. Minsky, Nathaniel Rochester, Claude E. Shannon | 1955 (publ. 2006) | A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955 | AI Magazine 27(4) | DOI 10.1609/aimag.v27i4.1904 | Founding of the field | HIST-002, BIO-MCCARTHY-001 |
| F. Rosenblatt | 1958 | The perceptron: A probabilistic model for information storage and organization in the brain. | Psychological Review | DOI 10.1037/h0042519 | Perceptron | HIST-003, BIO-ROSENBLATT-001, TECH-NN-001 |
| Marvin Minsky, Seymour Papert | 1969 (expanded ed. 1988) | Perceptrons: An Introduction to Computational Geometry | MIT Press | ISBN 9780262631112 | Perceptron limits; computational geometry | HIST-003, HIST-004, BIO-MINSKY-001 |
| Allen Newell, Herbert A. Simon | 1976 | Computer science as empirical inquiry | Communications of the ACM | DOI 10.1145/360018.360022 | Symbolic AI; physical symbol system | HIST-002, HIST-003, FOUND-003 |
| J J Hopfield | 1982 | Neural networks and physical systems with emergent collective computational abilities. | Proceedings of the National Academy of Sciences | DOI 10.1073/pnas.79.8.2554 | Associative-memory networks | HIST-008, TECH-NN-001 |
| Murray Campbell, A.Joseph Hoane, Feng-hsiung Hsu | 2002 | Deep Blue | Artificial Intelligence | DOI 10.1016/S0004-3702(01)00129-1 | Chess engines | HIST-009 |
| Nils J. Nilsson | 2010 | The Quest for Artificial Intelligence: A History of Ideas and Achievements | Cambridge University Press | ISBN 9780521122931 | History of AI | HIST-001, HIST-004, HIST-003 |
#Machine learning and neural-network foundations
| Author(s) | Year | Title | Venue / publisher | Identifier | Subject | Supports |
|---|---|---|---|---|---|---|
| David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams | 1986 | Learning representations by back-propagating errors | Nature | DOI 10.1038/323533a0 | Backpropagation | TECH-BACKPROP-001, HIST-008, BIO-HINTON-001 |
| Y. LeCun et al. (7 listed authors) | 1989 | Backpropagation Applied to Handwritten Zip Code Recognition | Neural Computation | DOI 10.1162/neco.1989.1.4.541 | Convolutional networks; applied backpropagation | TECH-CNN-001, TECH-BACKPROP-001, BIO-LECUN-001 |
| Corinna Cortes, Vladimir Vapnik | 1995 | Support-vector networks | Machine Learning | DOI 10.1007/BF00994018 | Support vector machines | HIST-007, TECH-ML-001 |
| Sepp Hochreiter, Jürgen Schmidhuber | 1997 | Long Short-Term Memory | Neural Computation | DOI 10.1162/neco.1997.9.8.1735 | Recurrent networks; LSTM | TECH-RNN-001 |
| Y. Lecun, L. Bottou, Y. Bengio, P. Haffner | 1998 | Gradient-based learning applied to document recognition | Proceedings of the IEEE | DOI 10.1109/5.726791 | Convolutional networks; document recognition | TECH-CNN-001, HIST-005, BIO-LECUN-001 |
| Geoffrey E. Hinton, Simon Osindero, Yee-Whye Teh | 2006 | A Fast Learning Algorithm for Deep Belief Nets | Neural Computation | DOI 10.1162/neco.2006.18.7.1527 | Deep belief networks; pretraining | HIST-005, TECH-DL-001, BIO-HINTON-001 |
| Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean | 2013 | Efficient Estimation of Word Representations in Vector Space | arXiv preprint | arXiv:1301.3781 | Word embeddings | TECH-TOK-001, HIST-012 |
| Yann LeCun, Yoshua Bengio, Geoffrey Hinton | 2015 | Deep learning | Nature | DOI 10.1038/nature14539 | Deep learning overview | TECH-DL-001, HIST-005, BIO-BENGIO-001 |
#Deep learning architectures
| Author(s) | Year | Title | Venue / publisher | Identifier | Subject | Supports |
|---|---|---|---|---|---|---|
| Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio | 2014 | Neural Machine Translation by Jointly Learning to Align and Translate | arXiv preprint | arXiv:1409.0473 | Attention in sequence models | TECH-ATTN-001, TECH-ARCH-001 |
| Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun | 2015 | Deep Residual Learning for Image Recognition | arXiv preprint | arXiv:1512.03385 | Residual networks | TECH-CNN-001, TECH-DL-001 |
| Olga Russakovsky et al. (12 listed authors) | 2015 | ImageNet Large Scale Visual Recognition Challenge | International Journal of Computer Vision | DOI 10.1007/s11263-015-0816-y | Image-recognition benchmark | HIST-005, HIST-013, CAP-BENCH-001 |
| Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton | 2017 | ImageNet classification with deep convolutional neural networks | Communications of the ACM (reprint of the 2012 NIPS paper) | DOI 10.1145/3065386 | ImageNet classification; CNNs | HIST-005, TECH-CNN-001, TECH-HW-001 |
| Ashish Vaswani et al. (8 listed authors) | 2017 | Attention Is All You Need | arXiv preprint | arXiv:1706.03762 | Transformer | TECH-ARCH-001, TECH-ATTN-001, HIST-010 |
| Noam Shazeer et al. (7 listed authors) | 2017 | Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer | arXiv preprint | arXiv:1701.06538 | Mixture of experts | TECH-MOE-001 |
| Alexey Dosovitskiy et al. (12 listed authors) | 2020 | An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale | arXiv preprint | arXiv:2010.11929 | Vision transformers | TECH-MM-001, TECH-ARCH-001, HIST-013 |
| Alec Radford et al. (12 listed authors) | 2021 | Learning Transferable Visual Models From Natural Language Supervision | arXiv preprint | arXiv:2103.00020 | Vision-language models | TECH-MM-001 |
#Language models and scaling
| Author(s) | Year | Title | Venue / publisher | Identifier | Subject | Supports |
|---|---|---|---|---|---|---|
| Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova | 2018 | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | arXiv preprint | arXiv:1810.04805 | Bidirectional pretraining | TECH-LLM-001, HIST-010, HIST-012 |
| Jared Kaplan et al. (10 listed authors) | 2020 | Scaling Laws for Neural Language Models | arXiv preprint | arXiv:2001.08361 | Scaling laws | TECH-SCALE-001, TECH-LLM-001, ECON-COMP-001 |
| Patrick Lewis et al. (12 listed authors) | 2020 | Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks | arXiv preprint | arXiv:2005.11401 | Retrieval augmentation | TECH-RAG-001 |
| Tom B. Brown et al. (31 listed authors) | 2020 | Language Models are Few-Shot Learners | arXiv preprint | arXiv:2005.14165 | In-context learning; GPT-3 | TECH-LLM-001, HIST-010, CAP-GENAI-001 |
| Mark Chen et al. (58 listed authors) | 2021 | Evaluating Large Language Models Trained on Code | arXiv preprint | arXiv:2107.03374 | Code generation | TECH-CODE-001, CAP-CODE-001 |
| Rishi Bommasani et al. (114 listed authors) | 2021 | On the Opportunities and Risks of Foundation Models | arXiv preprint | arXiv:2108.07258 | Foundation models | TECH-LLM-001, CAP-GENAI-001, HIST-010 |
| Jason Wei et al. (16 listed authors) | 2022 | Emergent Abilities of Large Language Models | arXiv preprint | arXiv:2206.07682 | Emergent abilities | TECH-SCALE-001, CAP-LIMITS-001 |
| Jordan Hoffmann et al. (22 listed authors) | 2022 | Training Compute-Optimal Large Language Models | arXiv preprint | arXiv:2203.15556 | Compute-optimal scaling | TECH-SCALE-001, ECON-COMP-001 |
| Long Ouyang et al. (20 listed authors) | 2022 | Training language models to follow instructions with human feedback | arXiv preprint | arXiv:2203.02155 | Instruction tuning; RLHF | TECH-ALIGN-001, SAFE-ALIGN-001 |
| Hugo Touvron et al. (14 listed authors) | 2023 | LLaMA: Open and Efficient Foundation Language Models | arXiv preprint | arXiv:2302.13971 | Open-weight language models | CAP-OPEN-001, TECH-LLM-001 |
| OpenAI; 281 listed authors (first individual: Josh Achiam) | 2023 | GPT-4 Technical Report | arXiv preprint | arXiv:2303.08774 | Frontier model report | TECH-LLM-001, CAP-BENCH-001 |
| Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, Chelsea Finn | 2023 | Direct Preference Optimization: Your Language Model is Secretly a Reward Model | arXiv preprint | arXiv:2305.18290 | Preference optimization | TECH-ALIGN-001 |
| Rylan Schaeffer, Brando Miranda, Sanmi Koyejo | 2023 | Are Emergent Abilities of Large Language Models a Mirage? | arXiv preprint | arXiv:2304.15004 | Emergence and metric choice | TECH-SCALE-001, CAP-BENCH-001 |
| DeepSeek-AI et al. (200 listed authors) | 2025 | DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning | arXiv preprint | arXiv:2501.12948 | Reasoning via reinforcement learning | TECH-REASON-001, CAP-OPEN-001 |
#Reinforcement learning and game playing
| Author(s) | Year | Title | Venue / publisher | Identifier | Subject | Supports |
|---|---|---|---|---|---|---|
| Volodymyr Mnih et al. (19 listed authors) | 2015 | Human-level control through deep reinforcement learning | Nature | DOI 10.1038/nature14236 | Deep Q-networks | TECH-RL-001, HIST-009 |
| David Silver et al. (20 listed authors) | 2016 | Mastering the game of Go with deep neural networks and tree search | Nature | DOI 10.1038/nature16961 | AlphaGo | TECH-RL-001, HIST-009 |
| David Silver et al. (17 listed authors) | 2017 | Mastering the game of Go without human knowledge | Nature | DOI 10.1038/nature24270 | AlphaGo Zero; self-play | TECH-RL-001, HIST-009 |
| John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov | 2017 | Proximal Policy Optimization Algorithms | arXiv preprint | arXiv:1707.06347 | Policy-gradient method | TECH-RL-001, TECH-ALIGN-001 |
| David Silver et al. (13 listed authors) | 2018 | A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play | Science | DOI 10.1126/science.aar6404 | AlphaZero | TECH-RL-001, HIST-009 |
| Richard S. Sutton, Andrew G. Barto | 2018 | Reinforcement Learning: An Introduction | MIT Press (2nd ed.) | ISBN 9780262039246 | Reinforcement learning textbook | TECH-RL-001, BIO-SUTTON-001 |
#Generative models
| Author(s) | Year | Title | Venue / publisher | Identifier | Subject | Supports |
|---|---|---|---|---|---|---|
| Diederik P Kingma, Max Welling | 2013 | Auto-Encoding Variational Bayes | arXiv preprint | arXiv:1312.6114 | Variational autoencoders | TECH-VAE-001 |
| Ian J. Goodfellow et al. (8 listed authors) | 2014 | Generative Adversarial Networks | arXiv preprint | arXiv:1406.2661 | Generative adversarial networks | TECH-GAN-001, CAP-GENAI-001 |
| Jonathan Ho, Ajay Jain, Pieter Abbeel | 2020 | Denoising Diffusion Probabilistic Models | arXiv preprint | arXiv:2006.11239 | Diffusion models | TECH-DIFF-001, CAP-GENAI-001 |
| Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer | 2021 | High-Resolution Image Synthesis with Latent Diffusion Models | arXiv preprint | arXiv:2112.10752 | Latent diffusion | TECH-DIFF-001, CAP-GENAI-001 |
#Reasoning, tools and agents
| Author(s) | Year | Title | Venue / publisher | Identifier | Subject | Supports |
|---|---|---|---|---|---|---|
| Karl Cobbe et al. (12 listed authors) | 2021 | Training Verifiers to Solve Math Word Problems | arXiv preprint | arXiv:2110.14168 | Verifiers; math word problems | TECH-REASON-001, CAP-BENCH-001 |
| Jason Wei et al. (9 listed authors) | 2022 | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models | arXiv preprint | arXiv:2201.11903 | Chain-of-thought prompting | TECH-REASON-001, CAP-REASON-001, TECH-LLM-001 |
| Shunyu Yao et al. (7 listed authors) | 2022 | ReAct: Synergizing Reasoning and Acting in Language Models | arXiv preprint | arXiv:2210.03629 | Language-model agents | TECH-AGENT-001 |
| Carlos E. Jimenez et al. (7 listed authors) | 2023 | SWE-bench: Can Language Models Resolve Real-World GitHub Issues? | arXiv preprint | arXiv:2310.06770 | Software-engineering benchmark | CAP-CODE-001, CAP-AGENTEVAL-001 |
| Hunter Lightman et al. (10 listed authors) | 2023 | Let's Verify Step by Step | arXiv preprint | arXiv:2305.20050 | Process supervision | TECH-REASON-001, SAFE-OVERSIGHT-001 |
| Charlie Snell, Jaehoon Lee, Kelvin Xu, Aviral Kumar | 2024 | Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters | arXiv preprint | arXiv:2408.03314 | Test-time compute | TECH-REASON-001 |
| Thomas Kwa et al. (26 listed authors) | 2025 | Measuring AI Ability to Complete Long Software Tasks | arXiv preprint | arXiv:2503.14499 | Task-horizon measurement | CAP-AGENTEVAL-001, CAP-REASON-001 |
#Evaluation and benchmarks
| Author(s) | Year | Title | Venue / publisher | Identifier | Subject | Supports |
|---|---|---|---|---|---|---|
| Dan Hendrycks et al. (7 listed authors) | 2020 | Measuring Massive Multitask Language Understanding | arXiv preprint | arXiv:2009.03300 | Multitask benchmark | CAP-BENCH-001 |
| Stephanie Lin, Jacob Hilton, Owain Evans | 2021 | TruthfulQA: Measuring How Models Mimic Human Falsehoods | arXiv preprint | arXiv:2109.07958 | Truthfulness benchmark | CAP-LIMITS-001, CAP-BENCH-001 |
| Aarohi Srivastava et al. (451 listed authors) | 2022 | Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models | arXiv preprint | arXiv:2206.04615 | Capability benchmark suite | CAP-BENCH-001 |
| Percy Liang et al. (50 listed authors) | 2022 | Holistic Evaluation of Language Models | arXiv preprint | arXiv:2211.09110 | Holistic evaluation | CAP-BENCH-001, CAP-COMPARE-001 |
#AGI, superintelligence and forecasting
| Author(s) | Year | Title | Venue / publisher | Identifier | Subject | Supports |
|---|---|---|---|---|---|---|
| Irving John Good | 1966 | Speculations Concerning the First Ultraintelligent Machine | Advances in Computers (book chapter) | DOI 10.1016/S0065-2458(08)60418-0 | Intelligence explosion | ASI-EXP-001, ASI-001 |
| Nick Bostrom | 2012 | The Superintelligent Will: Motivation and Instrumental Rationality in Advanced Artificial Agents | Minds and Machines | DOI 10.1007/s11023-012-9281-3 | Orthogonality and instrumental goals | SAFE-ALIGN-001, SAFE-XRISK-001 |
| Nick Bostrom | 2014 | Superintelligence: Paths, Dangers, Strategies | Oxford University Press | ISBN 9780199678112 | Superintelligence; control problem | ASI-001, ASI-PATH-001, SAFE-XRISK-001, BIO-BOSTROM-001 |
| Katja Grace, John Salvatier, Allan Dafoe, Baobao Zhang, Owain Evans | 2017 | When Will AI Exceed Human Performance? Evidence from AI Experts | arXiv preprint | arXiv:1705.08807 | Expert forecasts | AGI-TIME-001, FCST-METHOD-001 |
| Toby Ord | 2020 | The Precipice: Existential Risk and the Future of Humanity | Hachette Books | ISBN 9780316484916 | Existential risk | SAFE-XRISK-001, PHIL-CIV-001 |
| Meredith Ringel Morris et al. (8 listed authors) | 2023 | Levels of AGI for Operationalizing Progress on the Path to AGI | arXiv preprint | arXiv:2311.02462 | AGI levels taxonomy | AGI-DEF-001, AGI-001 |
| Sébastien Bubeck et al. (14 listed authors) | 2023 | Sparks of Artificial General Intelligence: Early experiments with GPT-4 | arXiv preprint | arXiv:2303.12712 | Early capability assessment of GPT-4 | AGI-001, AGI-TEST-001 |
| Chris Lu, Cong Lu, Robert Tjarko Lange, Jakob Foerster, Jeff Clune, David Ha | 2024 | The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery | arXiv preprint | arXiv:2408.06292 | Automated research | ASI-AIRD-001, APP-SCI-001 |
| Katja Grace et al. (7 listed authors) | 2024 | Thousands of AI Authors on the Future of AI | arXiv preprint | arXiv:2401.02843 | Expert forecasts | AGI-TIME-001, FCST-METHOD-001 |
#Alignment, safety and interpretability
| Author(s) | Year | Title | Venue / publisher | Identifier | Subject | Supports |
|---|---|---|---|---|---|---|
| Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, Dan Mané | 2016 | Concrete Problems in AI Safety | arXiv preprint | arXiv:1606.06565 | Safety problem taxonomy | SAFE-ALIGN-001, SAFE-ROBUST-001 |
| Paul Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, Dario Amodei | 2017 | Deep reinforcement learning from human preferences | arXiv preprint | arXiv:1706.03741 | Learning from human preferences | SAFE-ALIGN-001, TECH-ALIGN-001 |
| Evan Hubinger, Chris van Merwijk, Vladimir Mikulik, Joar Skalse, Scott Garrabrant | 2019 | Risks from Learned Optimization in Advanced Machine Learning Systems | arXiv preprint | arXiv:1906.01820 | Mesa-optimization | SAFE-ALIGN-001 |
| Stuart Russell | 2019 | Human Compatible: Artificial Intelligence and the Problem of Control | Viking | ISBN 9780525558613 | Provably beneficial AI | SAFE-ALIGN-001, BIO-RUSSELL-001 |
| Brian Christian | 2020 | The Alignment Problem: Machine Learning and Human Values | W. W. Norton & Company | ISBN 9780393635829 | Alignment; popular history | SAFE-ALIGN-001, SAFE-ETHICS-001 |
| Dan Hendrycks, Nicholas Carlini, John Schulman, Jacob Steinhardt | 2021 | Unsolved Problems in ML Safety | arXiv preprint | arXiv:2109.13916 | ML safety research agenda | SAFE-ROBUST-001, SAFE-ALIGN-001 |
| Catherine Olsson et al. (26 listed authors) | 2022 | In-context Learning and Induction Heads | arXiv preprint | arXiv:2209.11895 | Mechanistic interpretability | SAFE-INTERP-001, TECH-ARCH-001 |
| Joseph Carlsmith | 2022 | Is Power-Seeking AI an Existential Risk? | arXiv preprint | arXiv:2206.13353 | Power-seeking risk argument | SAFE-XRISK-001 |
| Richard Ngo, Lawrence Chan, Sören Mindermann | 2022 | The Alignment Problem from a Deep Learning Perspective | arXiv preprint | arXiv:2209.00626 | Alignment risk argument | SAFE-ALIGN-001, SAFE-XRISK-001 |
| Yuntao Bai et al. (51 listed authors) | 2022 | Constitutional AI: Harmlessness from AI Feedback | arXiv preprint | arXiv:2212.08073 | Constitutional AI | SAFE-ALIGN-001, TECH-ALIGN-001 |
| Dan Hendrycks, Mantas Mazeika, Thomas Woodside | 2023 | An Overview of Catastrophic AI Risks | arXiv preprint | arXiv:2306.12001 | Catastrophic risk taxonomy | SAFE-XRISK-001 |
| Toby Shevlane et al. (21 listed authors) | 2023 | Model evaluation for extreme risks | arXiv preprint | arXiv:2305.15324 | Dangerous-capability evaluation | SAFE-EVAL-001, GOV-INTL-001 |
| Evan Hubinger et al. (39 listed authors) | 2024 | Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training | arXiv preprint | arXiv:2401.05566 | Persistent deceptive behaviour | SAFE-ALIGN-001, SAFE-EVAL-001 |
| Ryan Greenblatt et al. (20 listed authors) | 2024 | Alignment faking in large language models | arXiv preprint | arXiv:2412.14093 | Alignment faking | SAFE-ALIGN-001, SAFE-EVAL-001 |
#Security, ethics and society
| Author(s) | Year | Title | Venue / publisher | Identifier | Subject | Supports |
|---|---|---|---|---|---|---|
| Christian Szegedy et al. (7 listed authors) | 2013 | Intriguing properties of neural networks | arXiv preprint | arXiv:1312.6199 | Adversarial examples | SAFE-ROBUST-001, SAFE-SEC-001 |
| Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy | 2014 | Explaining and Harnessing Adversarial Examples | arXiv preprint | arXiv:1412.6572 | Adversarial examples | SAFE-ROBUST-001, SAFE-SEC-001 |
| Margaret Mitchell et al. (9 listed authors) | 2018 | Model Cards for Model Reporting | arXiv preprint | arXiv:1810.03993 | Model documentation | SAFE-ETHICS-001, CAP-COMPARE-001 |
| Timnit Gebru et al. (7 listed authors) | 2018 | Datasheets for Datasets | arXiv preprint | arXiv:1803.09010 | Dataset documentation | TECH-DATA-001, SAFE-ETHICS-001 |
| Nicholas Carlini et al. (12 listed authors) | 2020 | Extracting Training Data from Large Language Models | arXiv preprint | arXiv:2012.07805 | Training-data extraction; privacy | SAFE-SEC-001, SAFE-PRIV-001, TECH-DATA-001 |
| Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, Shmargaret Shmitchell | 2021 | On the Dangers of Stochastic Parrots | Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency | DOI 10.1145/3442188.3445922 | Risks of large language models | SAFE-ETHICS-001, TECH-LLM-001 |
| Kate Crawford | 2021 | Atlas of AI | Yale University Press | ISBN 9780300209570 | Material and social costs of AI | SAFE-ETHICS-001, ECON-IND-001 |
| Laura Weidinger et al. (23 listed authors) | 2021 | Ethical and social risks of harm from Language Models | arXiv preprint | arXiv:2112.04359 | Harm taxonomy for language models | SAFE-ETHICS-001, CAP-LIMITS-001 |
| Andy Zou, Zifan Wang, Nicholas Carlini, Milad Nasr, J. Zico Kolter, Matt Fredrikson | 2023 | Universal and Transferable Adversarial Attacks on Aligned Language Models | arXiv preprint | arXiv:2307.15043 | Jailbreaks | SAFE-SEC-001 |
| Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, Christoph Endres, Thorsten Holz, Mario Fritz | 2023 | Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection | arXiv preprint | arXiv:2302.12173 | Prompt injection | SAFE-SEC-001 |
#Economy, compute and labor
| Author(s) | Year | Title | Venue / publisher | Identifier | Subject | Supports |
|---|---|---|---|---|---|---|
| Emma Strubell, Ananya Ganesh, Andrew McCallum | 2019 | Energy and Policy Considerations for Deep Learning in NLP | arXiv preprint | arXiv:1906.02243 | Energy use of training | ECON-INFRA-001 |
| Jaime Sevilla, Lennart Heim, Anson Ho, Tamay Besiroglu, Marius Hobbhahn, Pablo Villalobos | 2022 | Compute Trends Across Three Eras of Machine Learning | arXiv preprint | arXiv:2202.05924 | Training-compute trends | ECON-COMP-001, TECH-SCALE-001 |
| Pablo Villalobos, Anson Ho, Jaime Sevilla, Tamay Besiroglu, Lennart Heim, Marius Hobbhahn | 2022 | Will we run out of data? Limits of LLM scaling based on human-generated data | arXiv preprint | arXiv:2211.04325 | Data availability limits | TECH-DATA-001, ECON-COMP-001 |
| Erik Brynjolfsson, Danielle Li, Lindsey Raymond | 2023 | Generative AI at Work | arXiv preprint | arXiv:2304.11771 | Productivity field evidence | ECON-LABOR-001 |
| Tyna Eloundou, Sam Manning, Pamela Mishkin, Daniel Rock | 2023 | GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models | arXiv preprint | arXiv:2303.10130 | Labor-market exposure | ECON-LABOR-001 |
#Governance and policy reports
| Author(s) | Year | Title | Venue / publisher | Identifier | Subject | Supports |
|---|---|---|---|---|---|---|
| Miles Brundage et al. (26 listed authors) | 2018 | The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation | arXiv preprint | arXiv:1802.07228 | Misuse risks | GOV-INTL-001, SAFE-SEC-001 |
| Elham Tabassi | 2023 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | NIST AI 100-1 (U.S. National Institute of Standards and Technology) | DOI 10.6028/NIST.AI.100-1 | Risk management framework | GOV-US-001, ORG-STANDARDS-001, SAFE-ROBUST-001 |
| Yoshua Bengio et al. (96 listed authors) | 2025 | International AI Safety Report | arXiv preprint | arXiv:2501.17805 | International scientific assessment | GOV-INTL-001, SAFE-XRISK-001, ORG-SAFETY-001 |
#Science applications
| Author(s) | Year | Title | Venue / publisher | Identifier | Subject | Supports |
|---|---|---|---|---|---|---|
| John Jumper et al. (34 listed authors) | 2021 | Highly accurate protein structure prediction with AlphaFold | Nature | DOI 10.1038/s41586-021-03819-2 | Protein structure prediction | APP-PROT-001, APP-SCI-001 |
#Textbooks and general surveys
| Author(s) | Year | Title | Venue / publisher | Identifier | Subject | Supports |
|---|---|---|---|---|---|---|
| Ian Goodfellow, Yoshua Bengio, Aaron Courville | 2016 | Deep Learning | MIT Press | ISBN 9780262035613 | Deep learning textbook | TECH-DL-001, TECH-NN-001 |
| Melanie Mitchell | 2019 | Artificial Intelligence: A Guide for Thinking Humans | Farrar, Straus and Giroux | ISBN 9780374257835 | Capabilities and limits; general-audience survey | FOUND-001, CAP-LIMITS-001 |
| Stuart J. Russell, Peter Norvig | 2020 | Artificial Intelligence: A Modern Approach | Pearson (4th ed.) | ISBN 9780134610993 | AI textbook | FOUND-001, TECH-ML-001, CAP-TYPES-001 |
#Revision history
- v0.1.0 (2026-10-15): Initial draft. Citations checked against arXiv/Crossref/publisher pages where marked; not independently fact-checked.