1% ============================================================================2% Part V — The Artificial Intelligence Transformation3% Written by Agent E (AI Research Specialist), 2026-11-03, from4% literature_synthesis/agent_E/{review.md, claims.csv, contributions.csv,5% open_problems.csv}. Evidence rules: landmark papers are known from metadata6% and abstracts only (never full text); preprints are named as preprints; corpus7% numbers enter only through the macros of tables/numbers.tex or the generated8% tables tab_ai_*.tex (tables/make_tables.py, tables_part05). Claim identifiers9% [E-nn] in comments refer to claims.csv. No causal claim about ChatGPT.10% ============================================================================1112\section{Scope, evidence base and reading rules}13\label{sec:ai-scope}1415This part synthesises the nine categories of the taxonomy that the ontology16groups under \emph{AI and machine learning}: large language models, foundation17models, multimodal AI, generative AI, AI agents and autonomous systems,18reinforcement learning, machine-learning theory, deep-learning architectures,19and AI safety and alignment. Three kinds of evidence are combined, and the20reader should keep them apart. The first is the corpus itself: the yearly and21monthly counts, growth rates, structural-change estimates and citation flows of22Part~IV, read here only through the generated tables of this part23(Tables~\ref{tab:ai_growth}--\ref{tab:ai_landmarks}) and the macros of24\texttt{tables/numbers.tex}. The second is the automatically selected landmark25set of the area, \SelectionByAreaAiMl{} records chosen by field-normalised26citation impact within each primary category (Part~IV,27Section~\ref{sec:m-score}), of which the agent read the title, venue, type,28identifiers, citation windows and, where OpenAlex stores one, the abstract.29The third is the verified reference list assembled in Phase~530(\texttt{references.bib} and \texttt{references\_part05.bib}), which carries31the technical lineage of the field whether or not the corpus ranking surfaced32it. Nothing in this part describes the content of a paper beyond what its33abstract or bibliographic record states; a result known only from an abstract34is introduced as what ``the abstract reports''. Works that exist only as35arXiv, SSRN or medRxiv deposits are called preprints throughout, and a36preprint's claim is never upgraded to a peer-reviewed finding by the number of37times it has been cited. The boundary of 30~November 2022 is a reference date38for the time-series models; the estimates below describe when the series39changed, not why.4041\subsection{What the landmark set is, and what it is not}42\label{sec:ai-landmarks}4344The landmark candidates of the area were screened record by record before any45of them was used, and the outcome conditions everything that follows. Of the46\SelectionByAreaAiMl{} records, the screening flagged 44 as unreliable for the47purpose of a critical synthesis (Table~\ref{tab:ai_landmark_flags};48\texttt{literature\_synthesis/agent\_E/contributions.csv}).\footnote{The49flags are a human judgement by the specialist agent from identifiers, citation50counts, venue, title and abstract; they were not produced by a script and51must be audited (Part~III, Phase~8). The counts in this paragraph are those of52the flag file, reproduced in Table~\ref{tab:ai_landmark_flags}.} Thirteen are53\emph{identifier mismatches}: the title and abstract belong to an obscure work54while the arXiv identifier and the 36-month citation count belong to a famous55one. A 2026 article on translating natural language into MongoDB queries56carries the arXiv identifier of the retrieval-augmented-generation paper57\citep{lewis2020rag} and its citations; a Portuguese-language essay on58copyright carries that of GPT-3 \citep{brown2020gpt3}; an essay on59``theatrical compliance'' carries that of chain-of-thought prompting60\citep{wei2022cot}; a preprint on an intensive-care simulation carries that of61Llama~2 \citep{touvron2023llama2}; a ``reproducible experiment runner'' for the62Adam optimiser carries more than forty thousand citations. Three records are63\emph{abstract mismatches}, in which a correct title (the GPT-4 technical64report, \citealp{openai2023gpt4}; ``GPTs are GPTs'',65\citealp{eloundou2024gpts}) is paired with the abstract of another work.66Eleven are \emph{implausible years}: mixup, the variational recurrent network67and several 2013--2019 bandit papers appear as 2024--2026 works, four of them68with April--June 2026 arXiv identifiers, so that their field-normalised scores69are computed against the wrong cohort. Seven are \emph{normalisation70artefacts}, dissertations and unindexed articles with two to twenty citations71and NCS36 above five hundred, because the reference mean for a rare document72type in a young cohort is close to zero. Ten are \emph{off scope}:73control-theoretic consensus papers assigned to AI agents, an essay on74educational-technology ``solutionism'' assigned to reinforcement learning.75% [E-02, E-03]7677The defect is not uniformly distributed. Identifier and abstract problems78concentrate in the large-language-model category, where eight of79thirty-three records are affected, and misdated classics concentrate in80machine-learning theory and deep-learning architectures. The mechanism lies81upstream of this review, in the merging of work records by the source82database and in the second-pass arXiv and title linkage of Phase~183(Section~\ref{sec:m-linkage}); it is reported here because the AI area is where84it bites hardest, the arXiv identifiers of the most-cited papers of the decade85being exactly the ones most likely to be attached to the wrong record. Two86consequences are drawn. First, the clean count of citation-ranked AI landmarks87is at most 190, and every ``most influential papers'' statement of Part~IX88that concerns AI must be computed on records whose DOI, arXiv identifier and89title are mutually consistent. Second, the field-normalised score should be90recomputed with document-type-specific reference sets and with the 202691cohort excluded; this is a recommendation to the director, not something done92here. % [E-04]9394What the set does contain is instructive in its own right95(Table~\ref{tab:ai_contribution_types}). Judged from the abstract for 19096records and from title, venue and type for the 44 without one, 84 of the 23497candidates are surveys, 67 methods, 33 empirical studies, 29 theory or position98papers, 17 engineering systems and only 4 benchmarks. % [E-05]99Within the LLM and generative-AI categories the citation-ranked top is100occupied by education and commentary papers written in the months after101November 2022 \citep{kasneci2023chatgptgood, lo2023rapidreview,102rudolph2023assessment, chan2023students, baidooanu2023chatgptedu,103dwivedi2023sowhat, vandis2023priorities, lim2023ragnarok}, not by the papers104that introduced the models. % [E-06]105This is a property of ranking by field-normalised impact inside a multi-label106category that spans computer science, education and information systems: a107commentary in an education journal is normalised against education108journals, and the first wave of ChatGPT commentary was cited at rates that109no technical paper of its cohort matched within thirty-six months. The110technical lineage---GPT-4, LLaMA, InstructGPT, chain-of-thought, DPO, QLoRA,111BLIP-2, Mamba---is present in the set only where an arXiv record was linked,112and sometimes, as just shown, linked to the wrong record. The synthesis113therefore grounds its technical claims on the verified reference list and uses114the landmark set to characterise what the corpus \emph{rewards} rather than115what the field \emph{invented}. Table~\ref{tab:ai_landmarks} lists all116234 candidates with their flag and contribution type.117118\input{tables/tab_ai_landmark_flags}119\input{tables/tab_ai_contribution_types}120121\subsection{The corpus picture of the nine categories}122\label{sec:ai-corpus}123124Table~\ref{tab:ai_growth} gives the primary-category volume, the compound125annual growth rates of the baseline (2018--2022) and post (2023--2025) periods126and the share of eligible works for each category; Table~\ref{tab:ai_counts}127gives the yearly multi-label counts, which are several times larger because128the AI categories overlap heavily and a work counts in every category it is129assigned to. The ordering is the same under either definition. Large language130models grow fastest in the post period, generative AI, multimodal AI,131foundation models and AI safety follow, reinforcement learning and132machine-learning theory accelerate modestly, deep-learning architectures grow133slightly more slowly than in the baseline, and AI agents reverse a negative134baseline growth rate. % [E-07]135The share of eligible works carrying the LLM label as primary category rises by136more than an order of magnitude between 2018 and 2025. % [E-08]137Two cautions apply. The 2026 column of Table~\ref{tab:ai_counts} covers138publications dated up to the snapshot of 14~October 2026 and is incomplete;139and the categories are defined by rules over topics, arXiv classes, venues and140keyword patterns (Section~\ref{sec:m-classification}), so that a work on141reward modelling for language models is an LLM work and not a reinforcement-learning142work, a point that matters for Section~\ref{sec:ai-rl}.143144\input{tables/tab_ai_growth}145\input{tables/tab_ai_counts}146147The structural-change estimates are in Table~\ref{tab:ai_breaks} and, for the148alternative event dates, Table~\ref{tab:ai_altdates}. For the LLM series the149interrupted time series of eq.~\eqref{eq:its} fitted at150\ItsEventsChatgpt{} gives a level change of151\ItsSeriesCatLlmItsChatgptLevelChange{} log points152(HAC $p = \ItsSeriesCatLlmItsChatgptLevelP$) and a slope change of153\ItsSeriesCatLlmItsChatgptSlopeChange{} log points per month154($p = \ItsSeriesCatLlmItsChatgptSlopeP$) over a pre-period slope of155\ItsSeriesCatLlmItsChatgptPreSlope{}; the placebo distribution of156Section~\ref{sec:m-placebo} places the level statistic at a placebo157$p$-value of \ItsSeriesCatLlmPlaceboChatgptLevelTPlaceboP{} and the slope158statistic at \ItsSeriesCatLlmPlaceboChatgptSlopeTPlaceboP{}; the159Bai--Perron procedure selects \ItsSeriesCatLlmBaiPerronMBic{} break, dated160in Table~\ref{tab:ai_breaks} to the middle of 2022, and the Bayesian161on-line change-point posterior mass between December 2022 and March 2023 is162\ItsSeriesCatLlmBocpdCpProbTwoZeroTwoTwoOneTwoToTwoZeroTwoThreeZeroThree{}.163Excluding three months on either side of the event (the donut specification)164changes the level estimate to165\ItsSeriesCatLlmItsChatgptDonutLevelChange{}166($p = \ItsSeriesCatLlmItsChatgptDonutLevelP$). % [E-09]167The same configuration---no significant level jump at the reference month, a168large and significant slope change, a placebo $p$-value for the level that169does not single out December 2022, a data-driven break before or well after170it---holds for multimodal AI (slope change171\ItsSeriesCatMultimodalAiItsChatgptSlopeChange) and generative AI172(\ItsSeriesCatGenerativeAiItsChatgptSlopeChange). AI agents (level173\ItsSeriesCatAiAgentsItsChatgptLevelChange, $p =174\ItsSeriesCatAiAgentsItsChatgptLevelP$; slope175\ItsSeriesCatAiAgentsItsChatgptSlopeChange), reinforcement learning (level176\ItsSeriesCatReinforcementLearningItsChatgptLevelChange, $p =177\ItsSeriesCatReinforcementLearningItsChatgptLevelP$) and AI safety (level178\ItsSeriesCatAiSafetyItsChatgptLevelChange, $p =179\ItsSeriesCatAiSafetyItsChatgptLevelP$) show \emph{negative} level changes180followed by positive slope changes, a dip and a subsequent acceleration whose181reading is taken up category by category below. Deep-learning architectures is182the only AI category whose slope change183(\ItsSeriesCatDeepLearningArchitecturesItsChatgptSlopeChange, $p =184\ItsSeriesCatDeepLearningArchitecturesItsChatgptSlopeP$) is not185distinguishable from zero. % [E-10]186187\input{tables/tab_ai_breaks}188\input{tables/tab_ai_altdates}189190The alternative dates of Section~\ref{sec:m-altdates} order themselves191monotonically. Level effects estimated at the GPT-3 (\ItsEventsGptThree) and192DALL-E~2 (\ItsEventsDalleTwo) dates are negative for every AI category,193whereas those at the GPT-4 (\ItsEventsGptFour) and o1 (\ItsEventsOOne) dates194are positive and significant for LLM, multimodal and generative AI: for the195LLM series, \ItsSeriesCatLlmItsGptThreeLevelChange{} at the GPT-3 date196($p = \ItsSeriesCatLlmItsGptThreeLevelP$), \ItsSeriesCatLlmItsGptFourLevelChange{}197at the GPT-4 date ($p = \ItsSeriesCatLlmItsGptFourLevelP$) and198\ItsSeriesCatLlmItsOOneLevelChange{} at the o1 date199($p = \ItsSeriesCatLlmItsOOneLevelP$). % [E-11]200Each of these is a separate fit with the event moved, and a positive level201effect at a later date in a convex series is what a steepening trend produces202whether or not anything happened at that date. The reading that the evidence203supports is therefore restricted: the acceleration of the AI literature is a204change of trend that the data-driven procedures place in the middle of 2022205and that steepened through 2023--2024; the monthly series cannot attribute it206to one product release, and this part does not. % [E-12]207208\subsection{Knowledge flows and detected emergence}209\label{sec:ai-flows}210211Table~\ref{tab:ai_flows} reports, for each AI category and for 2019, 2022 and2122025, the destination of its in-corpus references, i.e.\ the row shares213$s_{c \to c'}(t)$ of eq.~\eqref{eq:flow}. The large-language-model category is214the one whose profile changed most. In 2019 and 2022 roughly seven in ten of215its in-corpus references went to natural language processing; in 2025 about a216third do, while more than a quarter go to the LLM category itself and217measurable shares go to human--computer interaction and software engineering.218% [E-13]219Generative AI moved from a vision-centred profile to one split between vision,220LLM and HCI; AI safety, which cited LLM work at a negligible rate in 2019,221directs a tenth of its references there in 2025 and slightly more to HCI.222% [E-14]223Reinforcement learning moves the other way, citing robotics more and LLM work224almost not at all, and machine-learning theory and deep-learning architectures225are essentially unchanged. % [E-15]226At the level of categories, then, the LLM literature has become a citation hub227for generative AI, safety and agents while remaining peripheral to228reinforcement learning, theory and architectures. This is the corpus-level229answer to RQ5 that this part can give; it is consistent with the convergence230thesis of \citet{bommasani2021foundation}, but a change in which category a231field cites is not evidence that its methods converged, and232Section~\ref{sec:ai-synthesis} returns to the distinction. % [E-16]233234\input{tables/tab_ai_flows}235236The preregistered known-emergence set of Section~\ref{sec:m-emerging} contains237eleven AI concepts, and their lead times are the first empirical test of238RQ10 in this area. The detector flags \emph{large language models} in239\KnownLargeLanguageModelsDetectionMonth{},240\KnownLargeLanguageModelsLeadMonths{} months before the peak-growth month241\KnownLargeLanguageModelsPeakGrowthMonth{}, on242\KnownLargeLanguageModelsTotalWorks{} matching works; \emph{reasoning models}243\KnownReasoningModelsLeadMonths{} months ahead; retrieval-augmented generation244\KnownRetrievalAugmentedGenerationLeadMonths{} months; vision-language models245\KnownVisionLanguageModelsLeadMonths{}; diffusion models246\KnownDiffusionModelsLeadMonths{}; LLM agents \KnownLlmAgentsLeadMonths{};247state-space sequence models \KnownStateSpaceSequenceModelsLeadMonths{};248mixture of experts \KnownMixtureOfExpertsLeadMonths{}; speculative decoding249\KnownSpeculativeDecodingLeadMonths{}; AI code generation250\KnownAiCodeGenerationLeadMonths{}; and mechanistic interpretability251\KnownMechanisticInterpretabilityLeadMonths{}, a negative lead in which the252peak-growth month precedes detection on only253\KnownMechanisticInterpretabilityTotalWorks{} matching works. The median lead254over the whole set is \KnownSetMedianLeadMonths{} months. % [E-17]255The long lead for language models is not foresight: the regular expression256matches the pre-2020 literature on ``language models'' and the detector fires257on its slow growth years before the 2023 surge. The short leads for258state-space models, mixture of experts and speculative decoding are the259honest result, and they say that topics driven by a single widely adopted260preprint are detected only months before their growth peaks.261262\section{Large language models and foundation models}263\label{sec:ai-fm}264265\subsection{The established lineage}266267The architecture that dominates the window is the decoder-only Transformer268\citep{vaswani2017attention} trained autoregressively at scale269\citep{radford2019gpt2, brown2020gpt3, chowdhery2022palm}, with the encoder270and encoder--decoder variants of the baseline period \citep{devlin2019bert,271raffel2020t5} surviving mainly as embedding models. Two empirical regularities272from before the window organise the technical literature inside it. The first273is the scaling law, in its original form a power law of loss in parameters $N$274and tokens $D$ \citep{kaplan2020scaling} and in the compute-optimal form of275\citet{hoffmann2022chinchilla}, which, for a fixed training budget $C \approx2766ND$, allocates roughly equal factors of growth to $N$ and $D$ and therefore277implies far more tokens per parameter than the earlier prescription. The278second is the sparse mixture-of-experts layer \citep{shazeer2017moe,279fedus2022switch}, which decouples parameter count from per-token compute. The280open-weight lineage LLaMA, Llama~2 and Llama~3 \citep{touvron2023llama,281touvron2023llama2, dubey2024llama3}---all three preprints, the first of which282is in the landmark set---follows the Chinchilla allocation; the LLaMA abstract283reports that a 13-billion-parameter model trained only on publicly available284data outperforms GPT-3 on most benchmarks, a claim that the abstract makes and285that this review has not verified. % [E-18]286The closed frontier releases are documented by technical reports without287training details \citep{openai2023gpt4, gemini2023, openai2024o1}; they are288cited here as the dated artefacts they are, and the GPT-4 record in the289landmark set, as noted above, carries someone else's abstract. The LLM surveys290of the window \citep{zhao2023llmsurvey, naveed2023llmoverview,291minaee2024llmsurvey, min2023plmsurvey} map a sub-literature that by 2025 had292its own citation core (Table~\ref{tab:ai_flows}); the journal version of the293first of them is in the landmark set and its abstract organises the field294into pre-training, adaptation, utilisation and evaluation.295296The most visible post-2022 development in the landmark set is not a new297general model but the diffusion of the foundation-model recipe into domains298with their own data. Self-supervised pathology encoders trained on hundreds of299thousands of whole-slide images \citep{chen2024uni, lu2024conch,300wang2024chief, huang2023plip, ding2025titan}, a radiology model trained on301two- and three-dimensional scans \citep{wu2025radfm}, a dermatology model on302two million clinical images \citep{yan2025panderm}, a brain-MRI model303\citep{tak2026brainiac}, an Earth-system model trained on more than a million304hours of geophysical data \citep{bodnar2025aurora}, a tabular model that the305abstract describes as a prior-data fitted network outperforming306gradient-boosted trees on small datasets \citep{hollmann2025tabpfn},307time-series representation models \citep{jin2023timellm, liu2024timesurl} and308a language model fine-tuned on ten million human choices to predict behaviour309across psychological experiments \citep{binz2025centaur} are, with the310exception of the time-series preprint, peer-reviewed journal or conference311papers, and each abstract reports outperforming task-specific baselines on a312battery of benchmarks. % [E-19]313What the abstracts do not report, and what the reviews in the same set list as314unresolved \citep{paschali2025fmradiology, zhang2026datacentric}, is external,315multi-site validation under distribution shift; the claim that domain316foundation models will displace task-specific pipelines in regulated settings317is speculative and is carried to Part~X as such. % [E-24]318319Below the frontier, the practical growth of the open-weight ecosystem320concentrated on adaptation. Low-rank adaptation \citep{hu2021lora}, its321four-bit variant QLoRA, whose abstract reports fine-tuning a 65-billion-parameter322model on a single 48\,GB GPU \citep{dettmers2023qlora}, the zero-initialised323adaption prompts of LLaMA-Adapter \citep{zhang2023llamaadapter} and the324parameter-efficient fine-tuning surveys \citep{han2024peftsurvey} define a325regime in which a research group without frontier compute can specialise a326model in hours. The QLoRA abstract's statement that its best model reaches327``99.3\,\% of the performance level of ChatGPT'' on the Vicuna benchmark is a328judge-based measurement, a point to which Section~\ref{sec:ai-eval} returns.329% [E-20]330331\subsection{Contested claims: emergence, sparks and pure-RL reasoning}332333Three claims about what scaled language models are have been argued largely in334preprints, and the corpus can record the argument but not settle it. The first335is emergence. \citet{wei2022emergent} report abilities that appear336discontinuously as scale increases; \citet{schaeffer2023mirage} show that for337the tasks examined the discontinuities are produced by discontinuous338metrics---exact-match accuracy on multi-token answers---and largely vanish339under continuous or token-level ones. Both are preprints, the disagreement is340about measurement rather than data, and the manuscript presents it as such.341% [E-21]342The second is ``Sparks of Artificial General Intelligence''343\citep{bubeck2023sparks}, which reports qualitative experiments on an early,344unreleased version of GPT-4 and, in the abstract's words, contends that the345model exhibits more general intelligence than previous systems. The record is346among the most cited of the landmark set, it is a preprint, its experiments347are not reproducible because the model version is unavailable, and the348critique that scale does not confer understanding \citep{bender2021parrots}349is in the same set with a comparable citation count. % [E-22]350The third is the reasoning turn: the o1 system card \citep{openai2024o1}351moved computation to inference time, and the DeepSeek-R1 paper, whose352peer-reviewed version is in the landmark set \citep{deepseek2025r1nature}353alongside the preprint \citep{deepseek2025r1}, reports that reasoning ``can be354incentivized through pure reinforcement learning, obviating the need for355human-labelled reasoning trajectories''. The claim is peer reviewed; its356generality beyond domains with verifiable rewards---mathematics, code---is not357tested by anything in the corpus, and it is the first entry of the open358problems in Section~\ref{sec:ai-synthesis}. % [E-23]359360\section{Alignment and post-training}361\label{sec:ai-align}362363The recipe that turned a pretrained language model into an assistant was in364place before the window: a reward model fitted to human preference365comparisons \citep{christiano2017rlhf} and a policy optimised against it with366a proximal objective \citep{schulman2017ppo}, applied to instruction following367in InstructGPT \citep{ouyang2022instructgpt} and extended with helpfulness and368harmlessness data \citep{bai2022hh} and with model-written critiques against a369written constitution \citep{bai2022constitutional}. Inside the window the370method's two moving parts were simplified and questioned. Direct preference371optimisation \citep{rafailov2023dpo} rewrites the RLHF objective so that the372policy is fitted directly to preference pairs through a closed-form373log-ratio loss, removing the separate reward model and the on-policy sampling;374the NeurIPS record is in the landmark set, without an abstract in the file.375The open problems of the reward-model route---reward hacking, preference376mis-specification, evaluator disagreement, the gap between stated and revealed377preferences---are catalogued by \citet{casper2023rlhfproblems}, a preprint378whose taxonomy is nevertheless the standard reference. % [E-25]379The corpus adds a sociological observation to this methodological one.380Reinforcement learning as a category directs less than one per cent of its381in-corpus references to LLM work in 2025 (Table~\ref{tab:ai_flows}), while the382LLM category's own self-citation share grew to more than a quarter. The383``return of reinforcement learning'' through post-training is therefore a384phenomenon of the LLM literature, which absorbed PPO, DPO and the group-relative385policy optimisation of DeepSeek-R1 as its own methods; the classification386separates the two communities even where the mathematics is shared, and387Section~\ref{sec:ai-rl} draws the consequence for any claim about the fate of388reinforcement learning.389390That aligned behaviour is brittle under optimisation pressure is established391by three lines of work. Universal adversarial suffixes found by gradient-guided392search transfer across models \citep{zou2023universal}; the ``jailbroken''393analysis attributes failures to competing objectives and mismatched394generalisation between pretraining and safety training395\citep{wei2023jailbroken}; and PAIR, a peer-reviewed method in the landmark396set, is described in its abstract as an attacker language model that generates397semantic jailbreaks against a target with black-box access, in about twenty398queries \citep{chao2025pair}. % [E-26]399The first two are preprints and the third was published in 2025; together they400show that every public alignment method of the window can be defeated by a401modest adaptive budget. Whether this brittleness is intrinsic to402preference-based alignment or an artefact of current scale and data is the403question that none of the landmark abstracts answers, and it is carried to404Part~X.405406\section{Reasoning, retrieval, tool use and code}407\label{sec:ai-reason}408409Chain-of-thought prompting \citep{wei2022cot, kojima2022zeroshotcot},410tree-structured search over thoughts \citep{yao2023tot} and verbal411self-reflection across episodes \citep{shinn2023reflexion} are the prompting412lineage of the reasoning literature; their evidence is benchmark gains, mostly413on arithmetic and symbolic tasks, and several of the records are preprints.414The reasoning-model turn of 2024--2025 \citep{openai2024o1,415deepseek2025r1nature} internalised the procedure by training the model to416produce long intermediate traces and by spending compute at inference rather417than at training. The detector of Section~\ref{sec:m-emerging} flagged418\emph{reasoning models} in \KnownReasoningModelsDetectionMonth{},419\KnownReasoningModelsLeadMonths{} months before the peak-growth month of420\KnownReasoningModelsPeakGrowthMonth{}; the long lead is an artefact of a421regular expression that includes chain-of-thought and therefore matched the4222023 prompting literature, and the honest reading is that the concept's423vocabulary existed well before its models. % [E-17]424425Retrieval augmentation is the most widely adopted of the window's patterns.426The dense passage retriever \citep{karpukhin2020dpr} and the427retrieval-augmented generation architecture \citep{lewis2020rag} precede the428window; their adoption inside it is surveyed by \citet{gao2023ragsurvey}, and429the known-set detector counts \KnownRetrievalAugmentedGenerationTotalWorks{}430matching works, detected in \KnownRetrievalAugmentedGenerationDetectionMonth{}431with peak growth in \KnownRetrievalAugmentedGenerationPeakGrowthMonth{}.432Tool use---ReAct's interleaving of reasoning traces and actions433\citep{yao2023react}, Toolformer's self-supervised API calls434\citep{schick2023toolformer}, ToolLLM's instruction tuning over thousands of435real APIs \citep{qin2023toolllm}, surveyed by \citet{mialon2023augmented}---is436the bridge from language models to the agents of Section~\ref{sec:ai-agents};437all four are preprints. Hallucination, the generation of fluent content438unsupported by the input or by fact, is the failure mode that these439mechanisms are meant to contain, and it remains a measurement problem before440it is a modelling problem: the surveys \citep{ji2023hallucination,441huang2025hallucination} agree on taxonomies and disagree on metrics. The442clearest statement of why is in the landmark set. The Med-PaLM paper443\citep{singhal2023clinical} built MultiMedQA from six medical444question-answering datasets and a new set of consumer questions, and its445abstract reports that a human evaluation framework along axes of factuality,446comprehension, reasoning and harm was needed because automated evaluation447against limited benchmarks did not meet the bar for clinical use. % [E-27]448449Code generation is the application in which the window's models were first450measured against professional work. The Codex evaluation451\citep{chen2021codex}, AlphaCode's competition-level results452\citep{li2022alphacode}, the open code models \citep{nijkamp2023codegen,453roziere2023codellama, li2023starcoder} and the SWE-bench task of resolving454real repository issues \citep{jimenez2024swebench} define the benchmark455sequence; the controlled productivity experiment of \citet{peng2023copilot}456and the field data of \citet{ziegler2022copilot} are the usage evidence, with457the usability and security caveats of \citet{vaithilingam2022expectation},458\citet{barke2023groundedcopilot} and \citet{pearce2022asleep}. The detector's459lead for \emph{AI code generation} is \KnownAiCodeGenerationLeadMonths{}460months. The software-engineering reading of this literature belongs to461Part~VII; here it is noted only that the LLM category's references to software462engineering in 2025 (Table~\ref{tab:ai_flows}) are the trace of this463application in the citation graph.464465\section{Multimodal and generative AI}466\label{sec:ai-multimodal}467468\subsection{Vision--language models}469470Two baseline-period results make the multimodal literature of the window471possible: contrastive image--text pre-training, which aligns an image encoder472and a text encoder on web-scale pairs \citep{radford2021clip}, and the vision473Transformer, which made the image encoder the same object as the text model474\citep{dosovitskiy2021vit}. The single-stream alternative, in which words and475image regions are tokens of one Transformer, is represented in the landmark476set by VL-BERT \citep{su2019vlbert}; the taxonomy of representation,477translation, alignment, fusion and co-learning that the field still uses is478that of \citet{baltrusaitis2019multimodal}, also in the set. Inside the window479the dominant design is a frozen or lightly adapted language model fed by a480visual encoder through a small bridge: a perceiver resampler in Flamingo481\citep{alayrac2022flamingo}; a querying Transformer trained in two stages482between frozen encoders and frozen LLMs in BLIP-2, whose abstract motivates483the design by the prohibitive cost of end-to-end pre-training484\citep{li2023blip2}; instruction tuning of the same bridge in InstructBLIP485\citep{dai2023instructblip}; a linear projection in LLaVA486\citep{liu2023llava}; shared functional modules with the language decoder as487``universal interface'' in mPLUG-Owl2 \citep{ye2024mplugowl2}. The promptable488segmentation model \citep{kirillov2023sam} is the dense-prediction analogue,489and the open image--text corpus LAION-5B \citep{schuhmann2022laion5b} is what490made CLIP-scale training reproducible outside industry. % [E-28]491Of these records, BLIP-2, InstructBLIP, LLaVA and LAION-5B are preprint or492proceedings deposits; mPLUG-Owl2 is a CVPR paper. The surveys493\citep{yin2024mllmsurvey, zhang2024mmllms} organise the design space; the494corpus-level trace is the vision-language detector, which fires495\KnownVisionLanguageModelsLeadMonths{} months before the concept's peak growth.496497Whether these models \emph{fuse} modalities or merely \emph{condition} on the498text channel is the open question that the landmark abstracts raise most499sharply. An evaluation of eight proprietary and open multimodal foundation500models on 1,090 medical cases reports, in its abstract, that image predictions501are ``largely driven by text'', that accuracy rises monotonically with the502amount of informative text, and that even mild textual suggestions of an503incorrect diagnosis degrade image-based classification504\citep{buckley2026textexploit}. % [E-30]505The scoping review of 432 medical multimodal papers \citep{schouten2025multimodalmedicine}506and the survey of fusion under noisy, missing and imbalanced modalities507\citep{zhang2026lowqualityfusion} describe the same robustness gap from the508methods side. The medical records in the set---generalist biomedical and509radiology models \citep{zhang2024biomedgpt, wu2025radfm}, reinforcement-learning510fine-tuning for medical reasoning in vision-language models and a zero-shot511evaluation of GPT-5 on medical visual question answering, the latter a512preprint \citep{lai2026medr1, wang2025gpt5medical}---report benchmark gains513but not modality ablations; the question is carried to Part~X.514515\subsection{Diffusion models and their applications}516517The generative lineage of the window is diffusion. Denoising diffusion518probabilistic models \citep{ho2020ddpm} and their latent-space formulation519\citep{rombach2022ldm} precede the boundary; Imagen \citep{saharia2022imagen}520and DALL-E~2 \citep{ramesh2022dalle2} are the 2022 text-to-image systems,521and the Imagen record is in the landmark set without an abstract. Three522technical contributions in the set define how the models are controlled.523Classifier-free guidance \citep{ho2022cfg}, a preprint, trains the conditional524and unconditional score jointly and interpolates between them at sampling525time, removing the separate classifier that earlier guidance required;526textual inversion \citep{gal2022textualinversion}, also a preprint, learns a527new token embedding from three to five images of a concept so that a frozen528model can render it; T2I-Adapter \citep{mou2024t2iadapter}, an AAAI paper,529trains small adapters that align external structural or colour signals with530the internal knowledge of a frozen text-to-image model. The Transformer531backbone for diffusion \citep{peebles2023dit} and its rectified-flow532successor \citep{esser2024sd3}, a preprint, close the architectural loop by533replacing the U-Net. The detector flags \emph{diffusion models}534\KnownDiffusionModelsLeadMonths{} months before peak growth. Outside images,535the one peer-reviewed scientific application in the landmark set is536MatterGen \citep{zeni2025mattergen}, whose abstract reports a diffusion model537that generates stable, diverse inorganic materials across the periodic table538and can be fine-tuned towards property constraints. % [E-29]539Speech followed the same self-supervised path a few years earlier540\citep{baevski2020wav2vec2, hsu2021hubert} and reached general-purpose541recognition with weakly supervised training at scale \citep{radford2023whisper};542the review of \citet{mehrish2023speechreview} covers the category, which the543taxonomy places outside the nine categories of this part.544545\subsection{What the generative-AI category rewards}546547Generative AI is the category whose citation profile changed most inside the548window, from a vision-centred one to a three-way split between vision, LLM549and HCI (Table~\ref{tab:ai_flows}). Its citation-ranked landmarks are,550however, not the diffusion papers just described but education, policy and551adoption studies: a policy framework from surveys of students and staff552\citep{chan2023policyframework}, scoping and systematic reviews of assessment553and academic integrity \citep{xia2024assessmentreview, bittle2025integrity},554analyses of the guidelines of 116 and of 50 United States universities555\citep{mcdonald2025hepolicies}, a paradox-theory essay \citep{lim2023ragnarok},556an OECD outlook, and two economics papers: a preregistered field experiment,557circulated as an SSRN preprint, whose title states that generative AI can harm558learning \citep{bastani2024harmlearning}, and nationally representative559surveys whose abstract reports that 45 per cent of United States adults aged56018--64 used generative AI by late 2024 and that work adoption was faster than561that of the personal computer \citep{bick2026rapidadoption}. % [E-31]562The pattern repeats the one observed for LLMs in Section~\ref{sec:ai-landmarks}:563technical generative-AI papers are counted in the category but, normalised564against their own cohort, rarely reach its citation top within thirty-six565months, whereas the first social-science and education responses to the566technology do. A reader who takes the landmark list as the field's567intellectual map would conclude that generative AI is a topic in education568research; the correct conclusion is that field normalisation inside a569multi-label category measures discourse, and that the measurement is570informative about diffusion rather than about invention.571