% ============================================================================
% Part V — The Artificial Intelligence Transformation
% Written by Agent E (AI Research Specialist), 2026-11-03, from
% literature_synthesis/agent_E/{review.md, claims.csv, contributions.csv,
% open_problems.csv}. Evidence rules: landmark papers are known from metadata
% and abstracts only (never full text); preprints are named as preprints; corpus
% numbers enter only through the macros of tables/numbers.tex or the generated
% tables tab_ai_*.tex (tables/make_tables.py, tables_part05). Claim identifiers
% [E-nn] in comments refer to claims.csv. No causal claim about ChatGPT.
% ============================================================================

\section{Scope, evidence base and reading rules}
\label{sec:ai-scope}

This part synthesises the nine categories of the taxonomy that the ontology
groups under \emph{AI and machine learning}: large language models, foundation
models, multimodal AI, generative AI, AI agents and autonomous systems,
reinforcement learning, machine-learning theory, deep-learning architectures,
and AI safety and alignment. Three kinds of evidence are combined, and the
reader should keep them apart. The first is the corpus itself: the yearly and
monthly counts, growth rates, structural-change estimates and citation flows of
Part~IV, read here only through the generated tables of this part
(Tables~\ref{tab:ai_growth}--\ref{tab:ai_landmarks}) and the macros of
\texttt{tables/numbers.tex}. The second is the automatically selected landmark
set of the area, \SelectionByAreaAiMl{} records chosen by field-normalised
citation impact within each primary category (Part~IV,
Section~\ref{sec:m-score}), of which the agent read the title, venue, type,
identifiers, citation windows and, where OpenAlex stores one, the abstract.
The third is the verified reference list assembled in Phase~5
(\texttt{references.bib} and \texttt{references\_part05.bib}), which carries
the technical lineage of the field whether or not the corpus ranking surfaced
it. Nothing in this part describes the content of a paper beyond what its
abstract or bibliographic record states; a result known only from an abstract
is introduced as what ``the abstract reports''. Works that exist only as
arXiv, SSRN or medRxiv deposits are called preprints throughout, and a
preprint's claim is never upgraded to a peer-reviewed finding by the number of
times it has been cited. The boundary of 30~November 2022 is a reference date
for the time-series models; the estimates below describe when the series
changed, not why.

\subsection{What the landmark set is, and what it is not}
\label{sec:ai-landmarks}

The landmark candidates of the area were screened record by record before any
of them was used, and the outcome conditions everything that follows. Of the
\SelectionByAreaAiMl{} records, the screening flagged 44 as unreliable for the
purpose of a critical synthesis (Table~\ref{tab:ai_landmark_flags};
\texttt{literature\_synthesis/agent\_E/contributions.csv}).\footnote{The
flags are a human judgement by the specialist agent from identifiers, citation
counts, venue, title and abstract; they were not produced by a script and
must be audited (Part~III, Phase~8). The counts in this paragraph are those of
the flag file, reproduced in Table~\ref{tab:ai_landmark_flags}.} Thirteen are
\emph{identifier mismatches}: the title and abstract belong to an obscure work
while the arXiv identifier and the 36-month citation count belong to a famous
one. A 2026 article on translating natural language into MongoDB queries
carries the arXiv identifier of the retrieval-augmented-generation paper
\citep{lewis2020rag} and its citations; a Portuguese-language essay on
copyright carries that of GPT-3 \citep{brown2020gpt3}; an essay on
``theatrical compliance'' carries that of chain-of-thought prompting
\citep{wei2022cot}; a preprint on an intensive-care simulation carries that of
Llama~2 \citep{touvron2023llama2}; a ``reproducible experiment runner'' for the
Adam optimiser carries more than forty thousand citations. Three records are
\emph{abstract mismatches}, in which a correct title (the GPT-4 technical
report, \citealp{openai2023gpt4}; ``GPTs are GPTs'',
\citealp{eloundou2024gpts}) is paired with the abstract of another work.
Eleven are \emph{implausible years}: mixup, the variational recurrent network
and several 2013--2019 bandit papers appear as 2024--2026 works, four of them
with April--June 2026 arXiv identifiers, so that their field-normalised scores
are computed against the wrong cohort. Seven are \emph{normalisation
artefacts}, dissertations and unindexed articles with two to twenty citations
and NCS36 above five hundred, because the reference mean for a rare document
type in a young cohort is close to zero. Ten are \emph{off scope}:
control-theoretic consensus papers assigned to AI agents, an essay on
educational-technology ``solutionism'' assigned to reinforcement learning.
% [E-02, E-03]

The defect is not uniformly distributed. Identifier and abstract problems
concentrate in the large-language-model category, where eight of
thirty-three records are affected, and misdated classics concentrate in
machine-learning theory and deep-learning architectures. The mechanism lies
upstream of this review, in the merging of work records by the source
database and in the second-pass arXiv and title linkage of Phase~1
(Section~\ref{sec:m-linkage}); it is reported here because the AI area is where
it bites hardest, the arXiv identifiers of the most-cited papers of the decade
being exactly the ones most likely to be attached to the wrong record. Two
consequences are drawn. First, the clean count of citation-ranked AI landmarks
is at most 190, and every ``most influential papers'' statement of Part~IX
that concerns AI must be computed on records whose DOI, arXiv identifier and
title are mutually consistent. Second, the field-normalised score should be
recomputed with document-type-specific reference sets and with the 2026
cohort excluded; this is a recommendation to the director, not something done
here. % [E-04]

What the set does contain is instructive in its own right
(Table~\ref{tab:ai_contribution_types}). Judged from the abstract for 190
records and from title, venue and type for the 44 without one, 84 of the 234
candidates are surveys, 67 methods, 33 empirical studies, 29 theory or position
papers, 17 engineering systems and only 4 benchmarks. % [E-05]
Within the LLM and generative-AI categories the citation-ranked top is
occupied by education and commentary papers written in the months after
November 2022 \citep{kasneci2023chatgptgood, lo2023rapidreview,
rudolph2023assessment, chan2023students, baidooanu2023chatgptedu,
dwivedi2023sowhat, vandis2023priorities, lim2023ragnarok}, not by the papers
that introduced the models. % [E-06]
This is a property of ranking by field-normalised impact inside a multi-label
category that spans computer science, education and information systems: a
commentary in an education journal is normalised against education
journals, and the first wave of ChatGPT commentary was cited at rates that
no technical paper of its cohort matched within thirty-six months. The
technical lineage---GPT-4, LLaMA, InstructGPT, chain-of-thought, DPO, QLoRA,
BLIP-2, Mamba---is present in the set only where an arXiv record was linked,
and sometimes, as just shown, linked to the wrong record. The synthesis
therefore grounds its technical claims on the verified reference list and uses
the landmark set to characterise what the corpus \emph{rewards} rather than
what the field \emph{invented}. Table~\ref{tab:ai_landmarks} lists all
234 candidates with their flag and contribution type.

\input{tables/tab_ai_landmark_flags}
\input{tables/tab_ai_contribution_types}

\subsection{The corpus picture of the nine categories}
\label{sec:ai-corpus}

Table~\ref{tab:ai_growth} gives the primary-category volume, the compound
annual growth rates of the baseline (2018--2022) and post (2023--2025) periods
and the share of eligible works for each category; Table~\ref{tab:ai_counts}
gives the yearly multi-label counts, which are several times larger because
the AI categories overlap heavily and a work counts in every category it is
assigned to. The ordering is the same under either definition. Large language
models grow fastest in the post period, generative AI, multimodal AI,
foundation models and AI safety follow, reinforcement learning and
machine-learning theory accelerate modestly, deep-learning architectures grow
slightly more slowly than in the baseline, and AI agents reverse a negative
baseline growth rate. % [E-07]
The share of eligible works carrying the LLM label as primary category rises by
more than an order of magnitude between 2018 and 2025. % [E-08]
Two cautions apply. The 2026 column of Table~\ref{tab:ai_counts} covers
publications dated up to the snapshot of 14~October 2026 and is incomplete;
and the categories are defined by rules over topics, arXiv classes, venues and
keyword patterns (Section~\ref{sec:m-classification}), so that a work on
reward modelling for language models is an LLM work and not a reinforcement-learning
work, a point that matters for Section~\ref{sec:ai-rl}.

\input{tables/tab_ai_growth}
\input{tables/tab_ai_counts}

The structural-change estimates are in Table~\ref{tab:ai_breaks} and, for the
alternative event dates, Table~\ref{tab:ai_altdates}. For the LLM series the
interrupted time series of eq.~\eqref{eq:its} fitted at
\ItsEventsChatgpt{} gives a level change of
\ItsSeriesCatLlmItsChatgptLevelChange{} log points
(HAC $p = \ItsSeriesCatLlmItsChatgptLevelP$) and a slope change of
\ItsSeriesCatLlmItsChatgptSlopeChange{} log points per month
($p = \ItsSeriesCatLlmItsChatgptSlopeP$) over a pre-period slope of
\ItsSeriesCatLlmItsChatgptPreSlope{}; the placebo distribution of
Section~\ref{sec:m-placebo} places the level statistic at a placebo
$p$-value of \ItsSeriesCatLlmPlaceboChatgptLevelTPlaceboP{} and the slope
statistic at \ItsSeriesCatLlmPlaceboChatgptSlopeTPlaceboP{}; the
Bai--Perron procedure selects \ItsSeriesCatLlmBaiPerronMBic{} break, dated
in Table~\ref{tab:ai_breaks} to the middle of 2022, and the Bayesian
on-line change-point posterior mass between December 2022 and March 2023 is
\ItsSeriesCatLlmBocpdCpProbTwoZeroTwoTwoOneTwoToTwoZeroTwoThreeZeroThree{}.
Excluding three months on either side of the event (the donut specification)
changes the level estimate to
\ItsSeriesCatLlmItsChatgptDonutLevelChange{}
($p = \ItsSeriesCatLlmItsChatgptDonutLevelP$). % [E-09]
The same configuration---no significant level jump at the reference month, a
large and significant slope change, a placebo $p$-value for the level that
does not single out December 2022, a data-driven break before or well after
it---holds for multimodal AI (slope change
\ItsSeriesCatMultimodalAiItsChatgptSlopeChange) and generative AI
(\ItsSeriesCatGenerativeAiItsChatgptSlopeChange). AI agents (level
\ItsSeriesCatAiAgentsItsChatgptLevelChange, $p =
\ItsSeriesCatAiAgentsItsChatgptLevelP$; slope
\ItsSeriesCatAiAgentsItsChatgptSlopeChange), reinforcement learning (level
\ItsSeriesCatReinforcementLearningItsChatgptLevelChange, $p =
\ItsSeriesCatReinforcementLearningItsChatgptLevelP$) and AI safety (level
\ItsSeriesCatAiSafetyItsChatgptLevelChange, $p =
\ItsSeriesCatAiSafetyItsChatgptLevelP$) show \emph{negative} level changes
followed by positive slope changes, a dip and a subsequent acceleration whose
reading is taken up category by category below. Deep-learning architectures is
the only AI category whose slope change
(\ItsSeriesCatDeepLearningArchitecturesItsChatgptSlopeChange, $p =
\ItsSeriesCatDeepLearningArchitecturesItsChatgptSlopeP$) is not
distinguishable from zero. % [E-10]

\input{tables/tab_ai_breaks}
\input{tables/tab_ai_altdates}

The alternative dates of Section~\ref{sec:m-altdates} order themselves
monotonically. Level effects estimated at the GPT-3 (\ItsEventsGptThree) and
DALL-E~2 (\ItsEventsDalleTwo) dates are negative for every AI category,
whereas those at the GPT-4 (\ItsEventsGptFour) and o1 (\ItsEventsOOne) dates
are positive and significant for LLM, multimodal and generative AI: for the
LLM series, \ItsSeriesCatLlmItsGptThreeLevelChange{} at the GPT-3 date
($p = \ItsSeriesCatLlmItsGptThreeLevelP$), \ItsSeriesCatLlmItsGptFourLevelChange{}
at the GPT-4 date ($p = \ItsSeriesCatLlmItsGptFourLevelP$) and
\ItsSeriesCatLlmItsOOneLevelChange{} at the o1 date
($p = \ItsSeriesCatLlmItsOOneLevelP$). % [E-11]
Each of these is a separate fit with the event moved, and a positive level
effect at a later date in a convex series is what a steepening trend produces
whether or not anything happened at that date. The reading that the evidence
supports is therefore restricted: the acceleration of the AI literature is a
change of trend that the data-driven procedures place in the middle of 2022
and that steepened through 2023--2024; the monthly series cannot attribute it
to one product release, and this part does not. % [E-12]

\subsection{Knowledge flows and detected emergence}
\label{sec:ai-flows}

Table~\ref{tab:ai_flows} reports, for each AI category and for 2019, 2022 and
2025, the destination of its in-corpus references, i.e.\ the row shares
$s_{c \to c'}(t)$ of eq.~\eqref{eq:flow}. The large-language-model category is
the one whose profile changed most. In 2019 and 2022 roughly seven in ten of
its in-corpus references went to natural language processing; in 2025 about a
third do, while more than a quarter go to the LLM category itself and
measurable shares go to human--computer interaction and software engineering.
% [E-13]
Generative AI moved from a vision-centred profile to one split between vision,
LLM and HCI; AI safety, which cited LLM work at a negligible rate in 2019,
directs a tenth of its references there in 2025 and slightly more to HCI.
% [E-14]
Reinforcement learning moves the other way, citing robotics more and LLM work
almost not at all, and machine-learning theory and deep-learning architectures
are essentially unchanged. % [E-15]
At the level of categories, then, the LLM literature has become a citation hub
for generative AI, safety and agents while remaining peripheral to
reinforcement learning, theory and architectures. This is the corpus-level
answer to RQ5 that this part can give; it is consistent with the convergence
thesis of \citet{bommasani2021foundation}, but a change in which category a
field cites is not evidence that its methods converged, and
Section~\ref{sec:ai-synthesis} returns to the distinction. % [E-16]

\input{tables/tab_ai_flows}

The preregistered known-emergence set of Section~\ref{sec:m-emerging} contains
eleven AI concepts, and their lead times are the first empirical test of
RQ10 in this area. The detector flags \emph{large language models} in
\KnownLargeLanguageModelsDetectionMonth{},
\KnownLargeLanguageModelsLeadMonths{} months before the peak-growth month
\KnownLargeLanguageModelsPeakGrowthMonth{}, on
\KnownLargeLanguageModelsTotalWorks{} matching works; \emph{reasoning models}
\KnownReasoningModelsLeadMonths{} months ahead; retrieval-augmented generation
\KnownRetrievalAugmentedGenerationLeadMonths{} months; vision-language models
\KnownVisionLanguageModelsLeadMonths{}; diffusion models
\KnownDiffusionModelsLeadMonths{}; LLM agents \KnownLlmAgentsLeadMonths{};
state-space sequence models \KnownStateSpaceSequenceModelsLeadMonths{};
mixture of experts \KnownMixtureOfExpertsLeadMonths{}; speculative decoding
\KnownSpeculativeDecodingLeadMonths{}; AI code generation
\KnownAiCodeGenerationLeadMonths{}; and mechanistic interpretability
\KnownMechanisticInterpretabilityLeadMonths{}, a negative lead in which the
peak-growth month precedes detection on only
\KnownMechanisticInterpretabilityTotalWorks{} matching works. The median lead
over the whole set is \KnownSetMedianLeadMonths{} months. % [E-17]
The long lead for language models is not foresight: the regular expression
matches the pre-2020 literature on ``language models'' and the detector fires
on its slow growth years before the 2023 surge. The short leads for
state-space models, mixture of experts and speculative decoding are the
honest result, and they say that topics driven by a single widely adopted
preprint are detected only months before their growth peaks.

\section{Large language models and foundation models}
\label{sec:ai-fm}

\subsection{The established lineage}

The architecture that dominates the window is the decoder-only Transformer
\citep{vaswani2017attention} trained autoregressively at scale
\citep{radford2019gpt2, brown2020gpt3, chowdhery2022palm}, with the encoder
and encoder--decoder variants of the baseline period \citep{devlin2019bert,
raffel2020t5} surviving mainly as embedding models. Two empirical regularities
from before the window organise the technical literature inside it. The first
is the scaling law, in its original form a power law of loss in parameters $N$
and tokens $D$ \citep{kaplan2020scaling} and in the compute-optimal form of
\citet{hoffmann2022chinchilla}, which, for a fixed training budget $C \approx
6ND$, allocates roughly equal factors of growth to $N$ and $D$ and therefore
implies far more tokens per parameter than the earlier prescription. The
second is the sparse mixture-of-experts layer \citep{shazeer2017moe,
fedus2022switch}, which decouples parameter count from per-token compute. The
open-weight lineage LLaMA, Llama~2 and Llama~3 \citep{touvron2023llama,
touvron2023llama2, dubey2024llama3}---all three preprints, the first of which
is in the landmark set---follows the Chinchilla allocation; the LLaMA abstract
reports that a 13-billion-parameter model trained only on publicly available
data outperforms GPT-3 on most benchmarks, a claim that the abstract makes and
that this review has not verified. % [E-18]
The closed frontier releases are documented by technical reports without
training details \citep{openai2023gpt4, gemini2023, openai2024o1}; they are
cited here as the dated artefacts they are, and the GPT-4 record in the
landmark set, as noted above, carries someone else's abstract. The LLM surveys
of the window \citep{zhao2023llmsurvey, naveed2023llmoverview,
minaee2024llmsurvey, min2023plmsurvey} map a sub-literature that by 2025 had
its own citation core (Table~\ref{tab:ai_flows}); the journal version of the
first of them is in the landmark set and its abstract organises the field
into pre-training, adaptation, utilisation and evaluation.

The most visible post-2022 development in the landmark set is not a new
general model but the diffusion of the foundation-model recipe into domains
with their own data. Self-supervised pathology encoders trained on hundreds of
thousands of whole-slide images \citep{chen2024uni, lu2024conch,
wang2024chief, huang2023plip, ding2025titan}, a radiology model trained on
two- and three-dimensional scans \citep{wu2025radfm}, a dermatology model on
two million clinical images \citep{yan2025panderm}, a brain-MRI model
\citep{tak2026brainiac}, an Earth-system model trained on more than a million
hours of geophysical data \citep{bodnar2025aurora}, a tabular model that the
abstract describes as a prior-data fitted network outperforming
gradient-boosted trees on small datasets \citep{hollmann2025tabpfn},
time-series representation models \citep{jin2023timellm, liu2024timesurl} and
a language model fine-tuned on ten million human choices to predict behaviour
across psychological experiments \citep{binz2025centaur} are, with the
exception of the time-series preprint, peer-reviewed journal or conference
papers, and each abstract reports outperforming task-specific baselines on a
battery of benchmarks. % [E-19]
What the abstracts do not report, and what the reviews in the same set list as
unresolved \citep{paschali2025fmradiology, zhang2026datacentric}, is external,
multi-site validation under distribution shift; the claim that domain
foundation models will displace task-specific pipelines in regulated settings
is speculative and is carried to Part~X as such. % [E-24]

Below the frontier, the practical growth of the open-weight ecosystem
concentrated on adaptation. Low-rank adaptation \citep{hu2021lora}, its
four-bit variant QLoRA, whose abstract reports fine-tuning a 65-billion-parameter
model on a single 48\,GB GPU \citep{dettmers2023qlora}, the zero-initialised
adaption prompts of LLaMA-Adapter \citep{zhang2023llamaadapter} and the
parameter-efficient fine-tuning surveys \citep{han2024peftsurvey} define a
regime in which a research group without frontier compute can specialise a
model in hours. The QLoRA abstract's statement that its best model reaches
``99.3\,\% of the performance level of ChatGPT'' on the Vicuna benchmark is a
judge-based measurement, a point to which Section~\ref{sec:ai-eval} returns.
% [E-20]

\subsection{Contested claims: emergence, sparks and pure-RL reasoning}

Three claims about what scaled language models are have been argued largely in
preprints, and the corpus can record the argument but not settle it. The first
is emergence. \citet{wei2022emergent} report abilities that appear
discontinuously as scale increases; \citet{schaeffer2023mirage} show that for
the tasks examined the discontinuities are produced by discontinuous
metrics---exact-match accuracy on multi-token answers---and largely vanish
under continuous or token-level ones. Both are preprints, the disagreement is
about measurement rather than data, and the manuscript presents it as such.
% [E-21]
The second is ``Sparks of Artificial General Intelligence''
\citep{bubeck2023sparks}, which reports qualitative experiments on an early,
unreleased version of GPT-4 and, in the abstract's words, contends that the
model exhibits more general intelligence than previous systems. The record is
among the most cited of the landmark set, it is a preprint, its experiments
are not reproducible because the model version is unavailable, and the
critique that scale does not confer understanding \citep{bender2021parrots}
is in the same set with a comparable citation count. % [E-22]
The third is the reasoning turn: the o1 system card \citep{openai2024o1}
moved computation to inference time, and the DeepSeek-R1 paper, whose
peer-reviewed version is in the landmark set \citep{deepseek2025r1nature}
alongside the preprint \citep{deepseek2025r1}, reports that reasoning ``can be
incentivized through pure reinforcement learning, obviating the need for
human-labelled reasoning trajectories''. The claim is peer reviewed; its
generality beyond domains with verifiable rewards---mathematics, code---is not
tested by anything in the corpus, and it is the first entry of the open
problems in Section~\ref{sec:ai-synthesis}. % [E-23]

\section{Alignment and post-training}
\label{sec:ai-align}

The recipe that turned a pretrained language model into an assistant was in
place before the window: a reward model fitted to human preference
comparisons \citep{christiano2017rlhf} and a policy optimised against it with
a proximal objective \citep{schulman2017ppo}, applied to instruction following
in InstructGPT \citep{ouyang2022instructgpt} and extended with helpfulness and
harmlessness data \citep{bai2022hh} and with model-written critiques against a
written constitution \citep{bai2022constitutional}. Inside the window the
method's two moving parts were simplified and questioned. Direct preference
optimisation \citep{rafailov2023dpo} rewrites the RLHF objective so that the
policy is fitted directly to preference pairs through a closed-form
log-ratio loss, removing the separate reward model and the on-policy sampling;
the NeurIPS record is in the landmark set, without an abstract in the file.
The open problems of the reward-model route---reward hacking, preference
mis-specification, evaluator disagreement, the gap between stated and revealed
preferences---are catalogued by \citet{casper2023rlhfproblems}, a preprint
whose taxonomy is nevertheless the standard reference. % [E-25]
The corpus adds a sociological observation to this methodological one.
Reinforcement learning as a category directs less than one per cent of its
in-corpus references to LLM work in 2025 (Table~\ref{tab:ai_flows}), while the
LLM category's own self-citation share grew to more than a quarter. The
``return of reinforcement learning'' through post-training is therefore a
phenomenon of the LLM literature, which absorbed PPO, DPO and the group-relative
policy optimisation of DeepSeek-R1 as its own methods; the classification
separates the two communities even where the mathematics is shared, and
Section~\ref{sec:ai-rl} draws the consequence for any claim about the fate of
reinforcement learning.

That aligned behaviour is brittle under optimisation pressure is established
by three lines of work. Universal adversarial suffixes found by gradient-guided
search transfer across models \citep{zou2023universal}; the ``jailbroken''
analysis attributes failures to competing objectives and mismatched
generalisation between pretraining and safety training
\citep{wei2023jailbroken}; and PAIR, a peer-reviewed method in the landmark
set, is described in its abstract as an attacker language model that generates
semantic jailbreaks against a target with black-box access, in about twenty
queries \citep{chao2025pair}. % [E-26]
The first two are preprints and the third was published in 2025; together they
show that every public alignment method of the window can be defeated by a
modest adaptive budget. Whether this brittleness is intrinsic to
preference-based alignment or an artefact of current scale and data is the
question that none of the landmark abstracts answers, and it is carried to
Part~X.

\section{Reasoning, retrieval, tool use and code}
\label{sec:ai-reason}

Chain-of-thought prompting \citep{wei2022cot, kojima2022zeroshotcot},
tree-structured search over thoughts \citep{yao2023tot} and verbal
self-reflection across episodes \citep{shinn2023reflexion} are the prompting
lineage of the reasoning literature; their evidence is benchmark gains, mostly
on arithmetic and symbolic tasks, and several of the records are preprints.
The reasoning-model turn of 2024--2025 \citep{openai2024o1,
deepseek2025r1nature} internalised the procedure by training the model to
produce long intermediate traces and by spending compute at inference rather
than at training. The detector of Section~\ref{sec:m-emerging} flagged
\emph{reasoning models} in \KnownReasoningModelsDetectionMonth{},
\KnownReasoningModelsLeadMonths{} months before the peak-growth month of
\KnownReasoningModelsPeakGrowthMonth{}; the long lead is an artefact of a
regular expression that includes chain-of-thought and therefore matched the
2023 prompting literature, and the honest reading is that the concept's
vocabulary existed well before its models. % [E-17]

Retrieval augmentation is the most widely adopted of the window's patterns.
The dense passage retriever \citep{karpukhin2020dpr} and the
retrieval-augmented generation architecture \citep{lewis2020rag} precede the
window; their adoption inside it is surveyed by \citet{gao2023ragsurvey}, and
the known-set detector counts \KnownRetrievalAugmentedGenerationTotalWorks{}
matching works, detected in \KnownRetrievalAugmentedGenerationDetectionMonth{}
with peak growth in \KnownRetrievalAugmentedGenerationPeakGrowthMonth{}.
Tool use---ReAct's interleaving of reasoning traces and actions
\citep{yao2023react}, Toolformer's self-supervised API calls
\citep{schick2023toolformer}, ToolLLM's instruction tuning over thousands of
real APIs \citep{qin2023toolllm}, surveyed by \citet{mialon2023augmented}---is
the bridge from language models to the agents of Section~\ref{sec:ai-agents};
all four are preprints. Hallucination, the generation of fluent content
unsupported by the input or by fact, is the failure mode that these
mechanisms are meant to contain, and it remains a measurement problem before
it is a modelling problem: the surveys \citep{ji2023hallucination,
huang2025hallucination} agree on taxonomies and disagree on metrics. The
clearest statement of why is in the landmark set. The Med-PaLM paper
\citep{singhal2023clinical} built MultiMedQA from six medical
question-answering datasets and a new set of consumer questions, and its
abstract reports that a human evaluation framework along axes of factuality,
comprehension, reasoning and harm was needed because automated evaluation
against limited benchmarks did not meet the bar for clinical use. % [E-27]

Code generation is the application in which the window's models were first
measured against professional work. The Codex evaluation
\citep{chen2021codex}, AlphaCode's competition-level results
\citep{li2022alphacode}, the open code models \citep{nijkamp2023codegen,
roziere2023codellama, li2023starcoder} and the SWE-bench task of resolving
real repository issues \citep{jimenez2024swebench} define the benchmark
sequence; the controlled productivity experiment of \citet{peng2023copilot}
and the field data of \citet{ziegler2022copilot} are the usage evidence, with
the usability and security caveats of \citet{vaithilingam2022expectation},
\citet{barke2023groundedcopilot} and \citet{pearce2022asleep}. The detector's
lead for \emph{AI code generation} is \KnownAiCodeGenerationLeadMonths{}
months. The software-engineering reading of this literature belongs to
Part~VII; here it is noted only that the LLM category's references to software
engineering in 2025 (Table~\ref{tab:ai_flows}) are the trace of this
application in the citation graph.

\section{Multimodal and generative AI}
\label{sec:ai-multimodal}

\subsection{Vision--language models}

Two baseline-period results make the multimodal literature of the window
possible: contrastive image--text pre-training, which aligns an image encoder
and a text encoder on web-scale pairs \citep{radford2021clip}, and the vision
Transformer, which made the image encoder the same object as the text model
\citep{dosovitskiy2021vit}. The single-stream alternative, in which words and
image regions are tokens of one Transformer, is represented in the landmark
set by VL-BERT \citep{su2019vlbert}; the taxonomy of representation,
translation, alignment, fusion and co-learning that the field still uses is
that of \citet{baltrusaitis2019multimodal}, also in the set. Inside the window
the dominant design is a frozen or lightly adapted language model fed by a
visual encoder through a small bridge: a perceiver resampler in Flamingo
\citep{alayrac2022flamingo}; a querying Transformer trained in two stages
between frozen encoders and frozen LLMs in BLIP-2, whose abstract motivates
the design by the prohibitive cost of end-to-end pre-training
\citep{li2023blip2}; instruction tuning of the same bridge in InstructBLIP
\citep{dai2023instructblip}; a linear projection in LLaVA
\citep{liu2023llava}; shared functional modules with the language decoder as
``universal interface'' in mPLUG-Owl2 \citep{ye2024mplugowl2}. The promptable
segmentation model \citep{kirillov2023sam} is the dense-prediction analogue,
and the open image--text corpus LAION-5B \citep{schuhmann2022laion5b} is what
made CLIP-scale training reproducible outside industry. % [E-28]
Of these records, BLIP-2, InstructBLIP, LLaVA and LAION-5B are preprint or
proceedings deposits; mPLUG-Owl2 is a CVPR paper. The surveys
\citep{yin2024mllmsurvey, zhang2024mmllms} organise the design space; the
corpus-level trace is the vision-language detector, which fires
\KnownVisionLanguageModelsLeadMonths{} months before the concept's peak growth.

Whether these models \emph{fuse} modalities or merely \emph{condition} on the
text channel is the open question that the landmark abstracts raise most
sharply. An evaluation of eight proprietary and open multimodal foundation
models on 1,090 medical cases reports, in its abstract, that image predictions
are ``largely driven by text'', that accuracy rises monotonically with the
amount of informative text, and that even mild textual suggestions of an
incorrect diagnosis degrade image-based classification
\citep{buckley2026textexploit}. % [E-30]
The scoping review of 432 medical multimodal papers \citep{schouten2025multimodalmedicine}
and the survey of fusion under noisy, missing and imbalanced modalities
\citep{zhang2026lowqualityfusion} describe the same robustness gap from the
methods side. The medical records in the set---generalist biomedical and
radiology models \citep{zhang2024biomedgpt, wu2025radfm}, reinforcement-learning
fine-tuning for medical reasoning in vision-language models and a zero-shot
evaluation of GPT-5 on medical visual question answering, the latter a
preprint \citep{lai2026medr1, wang2025gpt5medical}---report benchmark gains
but not modality ablations; the question is carried to Part~X.

\subsection{Diffusion models and their applications}

The generative lineage of the window is diffusion. Denoising diffusion
probabilistic models \citep{ho2020ddpm} and their latent-space formulation
\citep{rombach2022ldm} precede the boundary; Imagen \citep{saharia2022imagen}
and DALL-E~2 \citep{ramesh2022dalle2} are the 2022 text-to-image systems,
and the Imagen record is in the landmark set without an abstract. Three
technical contributions in the set define how the models are controlled.
Classifier-free guidance \citep{ho2022cfg}, a preprint, trains the conditional
and unconditional score jointly and interpolates between them at sampling
time, removing the separate classifier that earlier guidance required;
textual inversion \citep{gal2022textualinversion}, also a preprint, learns a
new token embedding from three to five images of a concept so that a frozen
model can render it; T2I-Adapter \citep{mou2024t2iadapter}, an AAAI paper,
trains small adapters that align external structural or colour signals with
the internal knowledge of a frozen text-to-image model. The Transformer
backbone for diffusion \citep{peebles2023dit} and its rectified-flow
successor \citep{esser2024sd3}, a preprint, close the architectural loop by
replacing the U-Net. The detector flags \emph{diffusion models}
\KnownDiffusionModelsLeadMonths{} months before peak growth. Outside images,
the one peer-reviewed scientific application in the landmark set is
MatterGen \citep{zeni2025mattergen}, whose abstract reports a diffusion model
that generates stable, diverse inorganic materials across the periodic table
and can be fine-tuned towards property constraints. % [E-29]
Speech followed the same self-supervised path a few years earlier
\citep{baevski2020wav2vec2, hsu2021hubert} and reached general-purpose
recognition with weakly supervised training at scale \citep{radford2023whisper};
the review of \citet{mehrish2023speechreview} covers the category, which the
taxonomy places outside the nine categories of this part.

\subsection{What the generative-AI category rewards}

Generative AI is the category whose citation profile changed most inside the
window, from a vision-centred one to a three-way split between vision, LLM
and HCI (Table~\ref{tab:ai_flows}). Its citation-ranked landmarks are,
however, not the diffusion papers just described but education, policy and
adoption studies: a policy framework from surveys of students and staff
\citep{chan2023policyframework}, scoping and systematic reviews of assessment
and academic integrity \citep{xia2024assessmentreview, bittle2025integrity},
analyses of the guidelines of 116 and of 50 United States universities
\citep{mcdonald2025hepolicies}, a paradox-theory essay \citep{lim2023ragnarok},
an OECD outlook, and two economics papers: a preregistered field experiment,
circulated as an SSRN preprint, whose title states that generative AI can harm
learning \citep{bastani2024harmlearning}, and nationally representative
surveys whose abstract reports that 45 per cent of United States adults aged
18--64 used generative AI by late 2024 and that work adoption was faster than
that of the personal computer \citep{bick2026rapidadoption}. % [E-31]
The pattern repeats the one observed for LLMs in Section~\ref{sec:ai-landmarks}:
technical generative-AI papers are counted in the category but, normalised
against their own cohort, rarely reach its citation top within thirty-six
months, whereas the first social-science and education responses to the
technology do. A reader who takes the landmark list as the field's
intellectual map would conclude that generative AI is a topic in education
research; the correct conclusion is that field normalisation inside a
multi-label category measures discourse, and that the measurement is
informative about diffusion rather than about invention.