A navigable atlas of selected AI research developments, their recorded relationships, and research directions from 1879 through 2026.
Historically influential concepts, systems or results
Developments needed to make a major lineage legible
Landmark papers, books, theses and reports that make a major lineage directly readable; selected 2010-2026 ArXiv work that fills a documented map gap
Open directions framed with a falsifiable or decision-relevant closure test
Curated, not exhaustive or peer reviewed
English-language and English-Wikipedia bias
Selected rather than systematic literature coverage; landmark links prioritize stable, authoritative access
Industrial work may be missing because results are private or poorly documented
Priority and influence claims can be disputed
Some ArXiv-sourced additions and extension relationships have no completed Wikipedia review; exact coverage is reported in the evidence summary
A map terminal means only that this edition records no downstream link
Network positions are generated from recorded associations; spatial proximity is not evidence of chronology, importance, similarity, consensus or causality
Repaired the first-minute map orientation with a curated, display-only 24-node anchor set and 72-relationship spine.
Exposed primary navigation across desktop and tablet layouts and added a compact mobile start chooser.
Renamed reader-facing relationship controls to Connections and Related without changing URL compatibility.
Simplified previews and reader-facing guidance while preserving the complete methodology under Help.
Separated relationship meaning from evidence strength
Separated significance, activity and trajectory facets
Added reviewed ranges and disclosed nonlinear spacing
Added structured cards for every open direction
Added list view, visible-fit, empty-state recovery and modal focus containment
Removed data-derived HTML injection sinks and added single-file security metadata
Added synchronized JSON-LD, JSON and NDJSON knowledge-graph exports with stable identifiers.
Reserved a branch-label rail, added scale-aware grouped overview markers, and removed the redundant footer statistics strip.
Anchored composite developments by their start year, repaired the Markov chronology, and added a rights-labeled landmark-reading graph for canonical papers and books.
Added a centered, versioned first-visit guide with an optional left dock, action-labeled theme switching, and verified two-theme contrast.
Made the docked guide a reserved left column so branch, era, timeline, list, empty-state and map text remain unobscured.
Added a deterministic, evidence-aware 2-D Network view with synchronized selection, filters, theme, trace and share state.
Packaged the atlas as a collaborative public-beta repository with citation, licensing, correction and contribution workflows.
Published stable Opportunity endpoints and root application record URLs with compatibility paths.
Added an exact staged-build manifest and persistent repository and contribution access.
Legacy editorial classification for developments treated as foundational in this map.
Legacy editorial classification indicating ongoing research or practice.
Legacy editorial classification indicating unusually high recent attention.
Legacy editorial classification; present activity requires node-specific review.
Legacy combined bucket; the independent trajectory facet remains unresolved unless re-reviewed.
Legacy editorial classification for a revival claim; activity is shown separately.
Evidence-marked question or incomplete generalization - from sparse combinations to capabilities that are not yet broadly reliable.
Markov introduced chains of dependent random variables in work communicated in 1906 and published in 1907, then used a chain model to analyze letter patterns in Eugene Onegin in 1913. The lineage underpins HMMs, MDPs and probabilistic sequence modeling.
Dependent-variable chains communicated 1906 and published 1907
Low-confidence editorial migration from the legacy category; unrepresented facets remain unassessed.
Frege's Begriffsschrift → Hilbert's formalist program
Frege's 1879 Begriffsschrift established a modern formal language for predicate logic; Hilbert's 1920s program pursued formalization and mechanical decision procedures. This 1879–1928 composite is plotted at its beginning so the timeline's starting date is visible and explained.
Gödel showed that any consistent, effectively axiomatized formal system expressive enough for arithmetic contains true statements it cannot prove. This bounds mechanized reasoning without precluding useful automation.
Turing formalized computation with an abstract universal machine. Most digital AI software fits this computational model, while analog and quantum systems extend the hardware story.
Switching circuits implement logic. The bridge from abstract symbols to physical hardware.
McCulloch and Pitts modeled idealized neurons as logical threshold units, creating an early bridge from neurobiology to artificial neural computation.
Strategic rationality formalized. Underpins multi-agent AI, GANs, poker solvers and mechanism design.
Stored-program architecture became computing's dominant general-purpose substrate. Specialized accelerators retain it at system level, while neuromorphic, analog and quantum approaches explore alternatives to the von Neumann bottleneck.
Feedback, control and communication in animal and machine. Seeded AI, control theory, robotics and the first misalignment worries.
Information quantified; prediction and compression unified. The deep reason language modeling works at all.
Hebbian learning linked correlated neural activity to synaptic change and remains foundational in associative and biologically plausible learning; modern gradient updates are not generally Hebbian.
'Computing Machinery and Intelligence' reframed 'can machines think?' as an imitation game. The field's north star and favorite argument.
The biophysics of the spike, measured and modeled. Ground truth for spiking networks and neuromorphic silicon.
McCarthy, Minsky, Shannon, Rochester bet a summer could crack intelligence. Wrong on timing, right on ambition — the field's founding myth.
Miller, Chomsky, Newell & Simon recast mind as information processing. Psychology and AI were born entangled.
Orientation-selective cells stacked in layers — the biological blueprint later copied by the neocognitron and CNNs.
Universal prediction by weighting all programs by simplicity. The idealized learner behind AIXI and compression-based views of intelligence.
Learning as compression, formalized. Quietly resurfaces in every era — most recently in 'compression is intelligence' scaling arguments.
Which problems are intractable. Explains why naive search-based AI hit walls — and why heuristics and learning are necessary.
Searle: syntax isn't semantics. The grounding critique haunted symbolic AI; multimodal, embodied learning is the modern rebuttal-in-progress.
Fodor & Pylyshyn: can networks compose meaning? The systematicity debate — reopened, not settled, by LLM reasoning.
Friston: brains minimize prediction error. Grand, contested, hard to operationalize — the neuroscience shadow of world-model AI.
Research direction: test whether active-inference objectives can support competitive, large-scale agents. Existing work has not yet established parity with frontier-scale model-based agents.
Can active-inference objectives produce compute-matched agents that equal strong model-based reinforcement-learning baselines on planning, adaptation, robustness, and efficiency?
Under identical observations, model capacity, environment steps, and tuning budgets, an active-inference agent matches or exceeds strong model-based baselines across several environment families, with objective ablations identifying where expected-free-energy terms add value.
Deep active inference with Monte Carlo tree search plans in dSprites and Animal-AI environments.
Discrete active-inference agents and the maintained pymdp library establish reproducible small-scale feasibility.
Individual: Implement a pymdp or neural active-inference agent on MiniGrid and compare it with PPO and a model-based baseline under fixed environment-step and tuning budgets.
Small lab: Run preregistered objective ablations across MiniGrid, Procgen, and selected DeepMind Control Suite tasks, measuring adaptation after dynamics and reward shifts.
Well-funded lab: Build a distributed active-inference agent and conduct a compute-matched simulation-to-robot evaluation with robustness, calibration, and wall-clock efficiency measurements.
Fountas et al. demonstrate a deep active-inference MCTS agent in continuous-state simulations.
Sajid et al. compare active inference with reinforcement learning in discrete Gym-style settings.
pymdp provides a maintained implementation for discrete Markov decision processes.
Low to medium; active communities exist, but large compute-matched benchmark studies are uncommon.
High for controlled comparisons and medium for broad generalization or robotics.
Newell & Simon's first AI program — proved theorems from Principia by heuristic search over symbols. AI's opening move.
McCarthy's symbolic language: code as data, recursion, garbage collection. AI's native tongue for 30 years.
Means–ends analysis as a theory of thought. 'General' proved elusive beyond puzzles — but it seeded planning and cognitive architectures.
McCarthy's program: represent knowledge as logical sentences, act by deduction. The purist wing of symbolic AI.
Robinson's single complete inference rule made automated deduction real — and revealed combinatorial explosion as the enemy.
Mass-spectrometry chemistry via encoded expertise. Proved domain knowledge beats general search — the knowledge turn begins.
Quillian's concepts-as-graphs. Ancestor of frames, ontologies, knowledge graphs — and, arguably, of embeddings-as-relations.
Winograd's dazzling language-plus-planning demo in a toy world. Famously refused to scale — the archetype of the demo-to-world gap.
Computation as deduction. Once a national strategy (Japan bet on it); now a respected niche in verification, Datalog and answer-set programming.
UK review damning AI's unmet promises; with DARPA cuts it froze funding for a decade. The field's first reckoning with its own hype.
Rule-based infection diagnosis that rivaled experts — and was never deployed (liability, integration, trust). The expert-system template and its fate in one system.
Structured slots and defaults for commonsense knowledge. Ancestor of objects, schemas, ontologies — and of every 'context' hack since.
Lenat's discovery programs invented concepts, won wargame tournaments — then 'ran out of steam.' Their post-mortem led him to Cyc.
HEARSAY-II's shared-workspace coordination of specialist modules. Faded — yet echoes in every multi-agent orchestration framework today.
Configured VAX orders with 10,000+ rules, saved ~$40M/yr — then drowned in rule-maintenance. The boom's flagship and its cautionary tale.
Formalizing defeasible commonsense reasoning. Non-monotonic logics provide rigorous tools, while the broader frame problem remains open and many practical systems use learned or domain-specific workarounds.
Shells, consultancies, and knowledge engineers turned expert systems into an industry. Lisp-machine workstations helped enable the boom; brittle rules and costly upkeep helped collapse it by 1990.
A whole hardware industry (Symbolics, LMI) for symbolic AI — wiped out by commodity workstations by 1987. Hardware bet on a software fad.
Japan's $850M national bet on massively parallel logic programming. Delivered little; its shadow scared funders worldwide for a decade.
Unified theories of cognition with dedicated labs for decades — marginal to modern AI, but a likely quarry for LLM-agent memory design.
Solve new problems by adapting stored cases. A thriving 90s subfield that faded to niche — conceptually reborn as retrieval-augmented everything.
Lenat's 40-year, million-axiom quest to hand-encode common sense. The grandest knowledge-engineering grind; still no general reasoner emerged.
Expert-system market collapse plus Lisp-machine crash. 'AI' became a dirty word; survivors rebranded as 'machine learning.'
Coq→Isabelle→Lean: humans and machines formalizing mathematics. A quiet community for 30 years — now the verification substrate for AI mathematicians.
Learning logic rules from examples. Elegant, interpretable, data-inefficient — sidelined by statistical ML; small revival alongside program synthesis.
Decidable fragments of logic for taxonomies; became OWL. Industrial ontology work persists; the research frontier moved on.
Zadeh's graded truth. Ran 80s–90s consumer devices (cameras, rice cookers, trains), then drifted out of core AI research entirely.
RDF and OWL standardized a machine-readable web of linked facts. The universal Semantic Web vision receded, but the standards persist in knowledge graphs and linked-data systems.
Google KG, Wikidata: semantic networks reborn industrially. Now the structured complement — and grounding source — for LLMs.
Marrying networks and symbols: program synthesis, tool use, logic-guided decoding. Perennial hope, real progress, no unified paradigm yet.
Research direction: combine broad language-model capabilities with end-to-end formal verification. Current systems demonstrate verified reasoning in bounded mathematics and code settings; broad coverage remains unresolved.
Can a system retain broad language-model coverage while making its consequential inferences machine-checkable?
On a predeclared suite spanning mathematics, repository-scale software verification, and structured domain rules, report proof coverage, proof-check success, residual unverified claims, abstentions, and capability loss relative to an otherwise matched unconstrained model.
DeepSeek-Prover-V2 demonstrates strong formal mathematics performance in Lean 4.
LeanDojo and ReProver make retrieval-augmented theorem proving reproducible at laboratory scale.
VeriSoftBench shows that mathematics-specialized provers transfer poorly to dependency-rich software repositories.
Individual: Build a proof-coverage auditor for a 7B prover on LeanDojo and VeriSoftBench that separates verified conclusions, unsupported premises, failed proofs, and timeouts.
Small lab: Train and evaluate one prover across mathematics and repository-scale software, explicitly measuring transfer loss and dependency-retrieval failures.
Well-funded lab: Develop a proof-carrying general assistant whose high-consequence outputs compile into multiple formal backends, with calibrated abstention when formalization is incomplete.
DeepSeek-Prover-V2-671B reports 88.9 percent on MiniF2F and solves 49 of 658 PutnamBench problems.
LeanDojo and ReProver provide open data, tooling, retrieval, and a 98,734-theorem benchmark.
FormalMATH, Leanabell-Prover-V2, and Lean Copilot provide additional narrow formal-reasoning systems.
High in formal mathematics and medium in cross-domain verification.
High for benchmark and tooling work; low for broad end-to-end verification.
'Programming a Computer for Playing Chess': minimax plus evaluation functions. The founding document of game AI.
Pruning cut game trees exponentially; heuristic evaluation made search practical. The workhorse of classical AI.
Optimal pathfinding with admissible heuristics, built for Shakey. In every router, game engine and planner since.
Actions as precondition/effect schemas. The planning formalism — and the stage on which the frame problem performed.
Waltz line-labeling to Mackworth's arc consistency: propagate constraints instead of searching blindly. Scheduling and layout still run on it.
First program to win a world title from humans; checkers fully solved by 2007. Proof of how far brute search + endgame tables go.
200M positions/sec plus hand-tuned evaluation topples chess. Taught two lessons: search scales, and chess wasn't intelligence after all.
Mature solvers, annual competitions, logistics benchmarks — then a plateau outside them. Now probed again as scaffolding for LLM agents.
CDCL made logical search industrial: chip verification, software analysis. Symbolic AI's quietest, most deployed victory.
Random rollouts + bandit selection cracked Go's branching factor. The bridge from classical search to AlphaGo — and to test-time reasoning.
Libratus and Pluribus beat top pros via counterfactual regret minimization. Game theory made computational — then the labs moved on.
Tree-of-Thoughts, planner hybrids, verifier-guided decoding: classical search reborn with learned heuristics. The search lane merges into the LLM lane.
When should language models generate heuristics, invoke symbolic planners or defer to explicit verification?
Robbins formalized explore-vs-exploit. From clinical trials to UCB — the mathematical kernel inside MCTS and every recommender.
A mathematical framework for sequential decisions under uncertainty, with dynamic programming and Bellman equations underpinning many planning and value-based reinforcement-learning methods.
Self-play plus a learned evaluation on 1950s hardware — machine learning's public debut and TD learning's ancestor.
Sutton: learn predictions from later predictions. RL's core algorithmic idea — later found running in dopamine neurons.
Watkins' off-policy value learning with a convergence proof. Tabular RL matured — then waited 24 years for deep nets.
Neural net + TD self-play reached top-human backgammon. The AlphaGo recipe, 24 years early — then largely ignored for a decade.
Policy gradients (REINFORCE → actor–critic)
REINFORCE optimized policies directly from sampled returns; actor–critic methods paired that policy with a learned value function. This is the missing bridge to PPO and RLHF.
Policy-gradient actor-critic consolidation, late 1990s
Schultz's recordings matched TD prediction error. Neuroscience validated RL's core signal — a rare two-way street between AI and brain.
REINFORCE, actor-critic, the textbook. Reinforcement learning becomes a coherent field with a shared language.
Temporal abstraction: learn skills, compose them. Perennially promising, perpetually stuck — LLM planners now route around it from above.
Deep nets + Q-learning + experience replay: one algorithm, 49 games, superhuman on many. Deep RL ignites (and DeepMind gets acquired).
Policy/value networks + MCTS + self-play. Move 37 became the era's cultural shockwave — a decade ahead of expert predictions.
Tabula-rasa self-play mastering Go, chess, shogi; MuZero learns the rules too. Search and learning unified — the lane's high-water mark.
A3C introduced asynchronous actor-critic training, and PPO became a common early optimization method for RLHF as well as a durable control baseline; later preference-training systems also use several alternatives.
Rewards from novelty and prediction error. Hot 2017–19, then faded — exploration at scale remains genuinely unsolved.
Emergent cooperation, competition and tool use (hide-and-seek). Re-energized by LLM agent societies and mechanism-design questions.
VAE+RNN agent trained inside its own learned dream. The seed of today's world-model wave — imagination as a training ground.
Learn the world, plan in imagination. The sample-efficiency path that matured into world-model agents for robots and games.
Superhuman Dota 2 and StarCraft II via massive self-play. Peak-then-pivot: point proven, labs promptly abandoned esports for language.
Systems that endlessly generate their own challenges. Niche but philosophically central — the question behind 'never-ending' agents.
Learn policies from logged trajectories without online exploration. Distribution shift remains hard, while Decision Transformer reframed policy learning as sequence modeling and the field feeds robotics and preference pipelines.
How can offline policies avoid extrapolating beyond dataset support while still improving over the behavior policy?
One transformer, 600 tasks, none mastered. Scaling alone didn't cure breadth-without-depth; quietly shelved as LLM agents took the crown.
ReAct→computer use→autonomous coding: language models that act. 2025–26's dominant paradigm — and its reliability problem.
How should agents plan, recover, maintain state and expose uncertainty across long-running tool use?
Train reasoning against checkable answers (math, code, proofs). The engine of o1/R1-class models — RL's biggest win since AlphaGo.
Robust, general open-ended self-improvement
Self-generated tasks and self-editing agents now show bounded improvement under human-designed verifiers. Still missing: safe, broadly general, sustained improvement without benchmark gaming, collapse, or a fixed external objective.
Can an AI system sustain repeated capability improvement across genuinely held-out domains without optimizing a fixed benchmark, depending on a hidden stronger teacher, or accumulating unacceptable safety regressions?
What would count as sustained general improvement rather than benchmark-specific optimization or evaluator exploitation?
Pre-register several improvement cycles and sealed domain families; show statistically reliable gains on newly unsealed tasks after every cycle; compare against static-agent, ordinary fine-tuning, search-only, and human-engineered baselines; publish compute, interventions, failures, contamination checks, and a safety-regression budget; and obtain an independent rerun.
The Darwin Gödel Machine evolves coding-agent implementations and improved bounded software-engineering benchmarks, while SEAL learns its own update directives and training data for bounded adaptation tasks. AlphaEvolve and related verifier-guided systems offer further evidence that self-generated search can improve artifacts. None yet demonstrates safe, durable, domain-general improvement under repeatedly refreshed evaluation.
Individual: Reproduce one DGM- or SEAL-style update loop on two disjoint small domains, freeze both evaluators before training, and publish every candidate, rollback, cost, and regression.
Small lab: Run a 10-cycle, multi-domain archive experiment with sealed future tasks and compare a static agent, self-editing agent, and diversity-preserving open-ended archive under the same inference and training budget.
Well-funded lab: Conduct a multi-model, long-horizon improvement trial with immutable external evaluations, independent red teaming, automatic rollback, full lineage, and a third-party audit of contamination and safety regressions.
Darwin Gödel Machine is a concrete self-editing, archive-based coding agent; its gains are on bounded coding benchmarks and one reported run cost about USD 22,000 in API use over roughly two weeks.
SEAL generates its own update directives and fine-tuning data, but evaluates bounded knowledge-incorporation and few-shot tasks rather than indefinite cross-domain improvement.
Search over self-edit strategies and verifier-guided evolutionary systems show useful improvement, while also exposing homogenization, evaluator gaming, and dependence on externally supplied objectives.
High for self-improving agents and automated coding; lower for preregistered, repeatedly refreshed, cross-domain and safety-bounded trials.
Medium for bounded coding experiments; low for a convincing general and safe long-horizon closure result.
Hardware neuron that learned from examples; the Navy promised it would 'walk, talk, see, write.' The hype and the machine that started it all.
Widrow–Hoff gradient learning in analog hardware. LMS still runs in every modem — neural nets' first quiet deployment.
Minsky & Papert proved single-layer limits (XOR); funding evaporated for 15 years. The canonical premature burial — multilayer nets weren't disproven, just undone.
Fukushima stacked Hubel–Wiesel receptive fields into a deep hierarchy. CNNs before backprop — an idea waiting 30 years for compute.
Energy-based associative memory that re-legitimized neural research. Faded for decades — then 'modern Hopfield' math turned out to equal attention. 2024 Nobel.
Stochastic energy models with hidden units. Training pain sidelined them, but their DNA lives in RBMs, energy-based models and diffusion.
Smolensky's bipartite Harmonium made Boltzmann machines easier to train; contrastive divergence revived it in the mid-2000s. Stacking RBMs supplied the pretraining recipe for deep belief networks.
Rumelhart–Hinton–Williams popularized credit assignment through layers (roots: Linnainmaa '70, Werbos '74). THE algorithm of the modern era.
The two-volume manifesto that brought neural nets back into cognitive science and AI. Winter number one, officially over.
MLPs can represent any function — existence, not learnability. Comfort, and false comfort, for a generation of theorists.
LeCun fused neocognitron structure with backprop; read ~10% of US checks by the late 90s. Then waited 15 years for its GPU moment.
Elman nets and backprop-through-time: sequence processing with memory. NLP's neural substrate until 2017.
Hochreiter's analysis of why depth wouldn't train. The diagnosis behind LSTM, pretraining detours, and eventually residual connections.
Gated memory defeats vanishing gradients. Carried speech and translation until 2017 — then dethroned by the transformer.
Layer-wise stacks of restricted Boltzmann machines re-ignited deep learning, then were superseded once ReLU networks and GPUs trained effectively end to end. A historically important transitional method.
Compression as self-supervision. Ancestor of VAEs, masked pretraining, and the representation-learning worldview.
Bahdanau's soft alignment for translation: look where it matters. The seed the transformer grew from.
Differentiable external memory. Beautiful, barely trainable, abandoned — yet it foreshadowed tool use, RAG and memory-augmented agents.
Skip connections let 100+ layers train. In literally everything since, transformers included. Depth's final unlock.
Networks generating other networks' weights. Never mainstream, but conceptually adjacent to LoRA, in-context learning and test-time adaptation.
'Attention Is All You Need': ditch recurrence, keep attention, parallelize everything. The architecture of the modern era — still unseated in 2026.
Message passing on graphs. The 2018–21 hype cooled; quietly indispensable in chemistry, chip design and weather.
Part–whole representations with learned routing offered an alternative to conventional CNNs. They did not become a mainstream replacement, but remain a specialist research line rather than a proven impossibility.
Neural networks parameterize continuous-time dynamics solved by differential-equation methods. Adoption is specialized but active in time series, control, scientific machine learning, and continuous normalizing flows.
Conditional compute: route each token through a sliver of a huge model. A 1991 idea that became the standard frontier architecture (Mixtral, DeepSeek-V3).
Performer, RWKV, and related methods sought subquadratic alternatives to exact attention. Better kernels narrowed the case, but recurrent and linear-attention variants remain an active efficiency niche and influenced state-space models.
Linear-time sequence modeling rooted in control theory. S4 and Mamba revived state-space models as competitive sequence architectures; hybrid attention-SSM designs are active and relative performance remains task- and scale-dependent.
Architectures whose weights update while running — nested learning against frozen-model amnesia. The architectural frontier of 2025–26.
Research direction: compare energy-based, diffusion-language-model and JEPA-style non-autoregressive systems with strong autoregressive baselines at equal data, parameter, inference-compute and latency budgets. Capable 8B-14B non-autoregressive systems already exist; robust parity across broad language tasks, efficiency and controllability remains unresolved.
Can a genuinely energy-based or non-autoregressive language objective match the strongest autoregressive models under equal data, parameters, training compute, and serving budgets across capability, efficiency, and controllability?
Run a preregistered scaling study with shared data, tokenizer, parameter and FLOP budgets, followed by matched-quality serving comparisons across capability, latency, throughput, controllability, and calibration.
Energy-Based Transformers learn compatibility energies and perform inference through energy minimization.
iLLaDA is an 8B masked-diffusion language model trained on 12 trillion tokens and is competitive with Qwen2.5-7B on several benchmarks.
LLaDA, SEDD, and Nemotron-Labs-Diffusion demonstrate scalable non-autoregressive or hybrid alternatives.
Individual: Benchmark released autoregressive, diffusion, and energy-based checkpoints under matched answer quality, reporting latency, function evaluations, memory, and controllable infilling.
Small lab: Train matched 300M-1B autoregressive, masked-diffusion, and energy-based models on one frozen corpus and publish complete learning and serving curves.
Well-funded lab: Run a preregistered 7B-14B matched-data scaling trial including instruction tuning, code, reasoning, long-form generation, and production-serving cost.
Energy-Based Transformers directly instantiate an energy-based language architecture.
LLaDA and iLLaDA show that masked diffusion can scale to 8B parameters and trillions of tokens.
Nemotron-Labs-Diffusion releases 3B, 8B, and 14B hybrid autoregressive-diffusion models.
SEDD reports competitive discrete-diffusion language modeling at smaller scale.
High and increasing across diffusion, energy-based, and hybrid language modeling.
Medium for controlled small-scale studies and low for genuine frontier parity.
Robbins–Monro stochastic approximation. The humble optimizer that, with tweaks, trained everything from ADALINE to GPT.
McCloskey & Cohen: networks overwrite old skills when learning new ones. The 35-year-old bug that still defines the continual-learning agenda.
Two cheap tricks — sparse activations, random deletion — made deep nets trainable without pretraining scaffolds. Simplicity beat cleverness.
Adaptive moments became the default optimizer; AdamW's decoupled weight decay became the durable modern variant. A thousand challengers later, the family remains stubbornly dominant.
Which optimizer behavior is actually responsible for robust scaling across architectures and data regimes?
Mini-batch statistics stabilized and accelerated deep-network training, becoming a standard ingredient in convolutional networks and the ResNet lineage.
Normalization across a layer's features removed dependence on batch statistics. It became the normalization backbone of recurrent networks and transformers.
Big teacher → small student. Now the pipeline by which frontier capability reaches phones and open models.
Pretraining followed by task adaptation became a dominant workflow across many domains, reducing but not eliminating from-scratch training and helping create the model-hub economy.
Learning supervisory signals from the data itself: contrastive learning, masked prediction, and predictive pretraining. SimCLR, MAE, wav2vec and BERT made it a shared foundation across vision, speech and language.
Which predictive objectives learn representations that transfer reliably across modalities and distribution shifts?
Sutton's essay: general methods riding compute beat human-crafted cleverness, every time so far. The scaling era's ideology — and its critics' target.
Loss falls as a power law in compute, data, parameters (Kaplan 2020); Chinchilla corrected the data ratio (2022). Research became engineering.
Low-rank adapters: customize a giant model on one GPU. Democratized fine-tuning and powered the open-weight explosion.
How should low-rank adaptation interact with quantization, continual updates and model merging?
INT8→FP4, pruning and structured sparsity shrink models for deployment while trading off memory, throughput and accuracy.
How far can post-training compression go before rare, small or safety-critical behaviors degrade in ways aggregate benchmarks miss?
IO-aware exact attention. Hardware-sympathetic algorithms beat clever approximations — and killed the linear-attention wave.
Mixtures, dedup, quality filters — then models teaching models (phi, distilled reasoning traces). Data quality as the real moat.
How can dataset provenance, quality and representativeness remain auditable at web scale?
Think longer instead of training bigger — the o1 insight. A second scaling axis, with search and verification in the loop.
DeepSeek's cheap RL-for-reasoning stack: frontier capability at a fraction of assumed cost. Efficiency itself as geopolitical disruption.
Nested learning, Titans-style memory, weight consolidation: the 2025–26 assault on catastrophic forgetting. Frontier labs now call it solvable.
Provable privacy budgets for training. Deployed in census and keyboards; a steady niche rather than a wave.
Training across devices or organizations without centralizing their raw data. It is deployed in privacy-sensitive settings and remains an active research area in optimization, security, and heterogeneous clients.
Deep learning theory (NTK, double descent)
Why do overparameterized nets generalize? NTK, double descent, lottery tickets — insight-rich, prediction-poor. Theory still trails practice by years.
Research direction: develop principled offline consolidation that transfers episodic experience into durable behavior without destructive forgetting. Long context and external memory address related but different problems.
Can episodic experience be consolidated into durable parametric behavior while bounding forgetting and outperforming long-context, retrieval, and rehearsal controls?
Across a long ordered experience stream, demonstrate retained prior-task performance, positive transfer, and stable general capability while beating matched replay-buffer, rehearsal, retrieval, long-context, and parameter-expansion controls at equal storage and compute.
TRACE exposes severe capability and instruction-following degradation during sequential LLM adaptation.
Delayed sleep-inspired replay can partially restore multiple sequentially learned tasks.
Replay, regularization, adapters, and dynamic expansion mitigate forgetting but do not establish general durable consolidation.
Individual: Compare delayed generative replay, immediate rehearsal, and LoRA isolation on a small TRACE stream with explicit storage and compute accounting.
Small lab: Consolidate episodic traces into a 7B model and compare parametric retention against retrieval, long context, and equal-size raw replay buffers.
Well-funded lab: Run a months-long multimodal experience stream with periodic consolidation, measuring forgetting, transfer, behavior drift, and retained safety properties.
Reasoning-augmented continual learning reduces some forgetting on TRACE.
Sleep-inspired replay partially restores prior tasks after multiple sequential updates.
Titans provides test-time neural memory but is not equivalent to offline parametric consolidation.
Long context and retrieval can preserve access to episodes without consolidating them into durable behavior.
High in continual learning; lower in biologically inspired offline consolidation at LLM scale.
Medium for 1B-7B studies and low for frontier-scale long-horizon consolidation.
Russian→English demo promised solved-in-5-years; the 1966 ALPAC report axed US funding. NLP's first boom-bust cycle, complete in twelve years.
Chomsky's formal syntax hierarchies fueled symbolic NLP for decades — and his 'poverty of stimulus' argued pure learning could never acquire language. LLMs reopened the case.
Weizenbaum's pattern-matching 'therapist'. The first chatbot — and the first Eliza effect, as users projected understanding onto scripts.
Schank: meaning as canonical structures, stories as scripts. Rich theory with a hand-built ceiling; faded with the statistical turn.
Jelinek's statistical turn ('every time I fire a linguist, performance goes up'). Ruled speech for 35 years — then deep nets retired it.
Count word sequences, predict the next. Powered search and speech for decades — the ancestor LLMs obliterated and descend from.
Documents and queries represented as weighted term vectors made ranked search practical. This classical retrieval lineage runs through Watson and into retrieval-augmented generation.
Annotated corpora turned linguistics into a measurable, competitive sport. The empirical culture LLMs inherited.
IBM's noisy-channel models revived MT from the ALPAC ashes; phrase-based SMT ran Google Translate for a decade — then neural MT ended it overnight.
Hand-built graphs of word meaning. Foundational for an era of NLP; essentially frozen since embeddings learned the same structure from text.
Bengio's neural LM: word embeddings + shared statistical strength. The right idea, one GPU generation too early.
Jeopardy! triumph via hybrid NLP+IR — then a healthcare moonshot that burned billions and was sold for parts. The era's overclaim case study.
DNNs halved error rates and retired the HMM pipeline — deep learning's first industrial conquest, before vision's.
Meaning as geometry ('king − man + woman ≈ queen'). Static embeddings were superseded by contextual ones — but the vector worldview conquered everything.
Sentence in, sentence out, via LSTMs. Machine translation went neural — and 'everything is sequence transduction' became a research program.
Google Translate's overnight quality jump — the first mass-scale deep NLP product, and SMT's abrupt retirement.
Masked bidirectional pretraining transformed language understanding and transfer. Encoder models remain widely used for embeddings, retrieval and classification alongside decoder-only language models.
Decoder-only LM 'too dangerous to release' — showed unsupervised generation plus zero-shot glimmers. The scaling bet forms.
At 175B parameters, GPT-3 showed sharply improved few-shot in-context learning under next-token training and accelerated the shift toward language-model APIs.
From autocomplete to agentic software engineering. The capability that let AI build AI tooling — and reshaped programming itself.
'Think step by step' unlocked latent multi-step reasoning. The prompt era's deepest finding — later trained-in via RL.
Supervised demonstrations, reward modeling and PPO turned pretrained LMs into preference-tuned assistants. This RLHF deployment recipe is distinct from the earlier supervised instruction-tuning lineage.
How should supervised instruction following and preference optimization be composed without reward-model overfitting?
100M users in two months. A research artifact became planetary infrastructure overnight — and reset funding, talent and politics for the whole field.
The short-lived job-title and incantation culture around prompt-only optimization faded. Prompt design itself remains active and was absorbed into broader context engineering, evaluation, retrieval, and product design.
Designing the full information environment around a model: instructions, retrieved evidence, tools, memory, and state. The operational successor to prompt-only optimization for production agents.
Bar exams passed, images understood. The capability jump that reset expectations, safety debates and national policy at once.
Llama leak → Llama 2/3 → Mistral, Qwen, DeepSeek. Weights in the wild: fine-tune culture, sovereignty politics, and a live frontier-gap debate.
Bolt a search engine to the model. The hallucination patch that became commodity plumbing — now competing with million-token contexts.
RoPE scaling, efficient attention, context engineering. Whole books in the prompt — rivaling retrieval for many workloads.
RL-trained deliberate thinking with visible chains of thought. Olympiad-level math and code jumps — the current capability frontier.
Mercury, Gemini Diffusion: parallel denoising instead of left-to-right generation. A speed-play challenger to autoregression — early days.
Frontier assistants (GPT-5, Gemini 3, Claude)
Converged reasoning + multimodal + agents, competing on reliability, cost and depth-of-thought. The 2026 state of the art.
Deep bidirectional language models produced word representations that changed with context. ELMo made the transition from static embeddings to BERT-style contextual pretraining explicit.
Roberts' edges and 3D from line drawings — computer vision's first thesis. Minsky assigned vision as a summer project in 1966; it took 50 years.
Levels of analysis, primal sketch → 2.5D → 3D. Shaped a generation of vision science; aged into the anti-example for end-to-end learning.
Engineered descriptors ruled recognition for a decade of careful pipelines — rendered obsolete in one conference cycle after AlexNet.
Viola-Jones cascades enabled real-time face detection and were widely deployed in early cameras and vision products; later learned detectors largely displaced the original pipeline.
14M crowd-labeled images and an annual contest. The dataset that made deep learning measurable — and made benchmarks the field's engine.
GPU-trained CNN dropped ImageNet error from 26% to 16%. The result that flipped the entire field to deep learning within 18 months.
R-CNN→Mask R-CNN→DETR: from labels to boxes to pixels. The perception stack under AVs, robots and medical imaging.
How should open-world detectors represent unknown objects and failures rather than forcing closed-set predictions?
DeepFace/FaceNet crossed human accuracy — and dragged AI into surveillance politics, bias audits and bans. Capability and controversy, together.
An encoder–decoder with skip connections built for biomedical segmentation. Its multiscale denoising shape later became the workhorse backbone of diffusion models.
Treating images as patch tokens made pure transformers competitive for image recognition at scale and helped unify vision-language architectures; convolutional networks remain widely used.
400M web image-text pairs; open-vocabulary recognition for free. The bridge into multimodal LLMs and the guidance system of image generation.
Segment anything, zero-shot, from a click or a phrase. Foundation-model economics arrive in core vision tooling.
Scenes as learned radiance fields; photoreal 3D from phone photos. Feeding robotics sims, film, and world models.
How can neural scene representations become editable, physically grounded and useful for embodied agents?
LeCun's anti-autoregressive program: predict in representation space, not pixels. Embodied common sense as the goal; the bet against LLM-only intelligence.
GPT-4V→Gemini: see, hear, speak in one model. Vision stopped being a separate field and became an input mode.
Probabilistic latent spaces you can sample. Generative deep learning's respectable start — and diffusion's mathematical grandparent.
Forger vs detective training. Ruled generation 2014–20 (StyleGAN faces, deepfakes) — dethroned by diffusion; lingers in fast-sampling and super-res niches.
Invertible networks provide exact likelihoods and efficient sampling. Diffusion dominates general image generation, but flows remain active in density estimation, simulation-based inference, and scientific applications.
How can invertible models scale while retaining tractable density estimation and useful inductive bias?
Gram-matrix artistry and dog-faced clouds — the first viral neural art. Charming, superseded by text-to-image control.
Autoregressive raw-waveform modeling set a new quality bar for text-to-speech and influenced neural audio research; many current speech and music systems use different codec, diffusion or transformer architectures.
Denoise noise into data, step by step. Stable training beat GAN fragility — the generative workhorse across image, audio, video, molecules.
Which continuous-time formulations best trade training stability, controllability and sampling cost?
Text-to-image (latent + guided diffusion)
Latent diffusion made high-resolution synthesis economical; classifier-free guidance made text control practical. DALL·E 2 and Stable Diffusion brought language-steered imagery to everyone — and opened a copyright battlefield.
How can controllability, provenance and creator rights improve without concentrating access to generation tools?
Transformer backbones gave diffusion models a scalable architecture used across image and increasingly video and audio generation; other backbones remain active.
Suno, ElevenLabs, voice cloning at consumer scale. Creative industries' negotiation with AI, conducted in real time.
Minutes of coherent video with sound. The live question: are these world simulators in disguise, or very good pixel dreamers?
Playable, promptable generated worlds. Games and training grounds for agents — a possible road to spatial intelligence (World Labs' Marble, too).
Research direction: test whether policies trained predominantly in learned generative worlds can outperform matched classical-simulation baselines across multiple physical settings and embodiments. Learned-world-to-real policy loops already exist; robust general transfer remains unresolved.
Can policies learned mainly inside learned generative world models transfer reliably to multiple physical settings and embodiments, and outperform classical simulation under the same real-world data and compute budget?
Pre-register at least three tasks, two physical settings, and two embodiments; hold real-world interaction budgets equal; compare learned-world, classical simulator, mixed, imitation-only, and direct-real-training baselines; report intervention and failure rates; and reproduce the result at an independent site.
PhysWorld reconstructs task-specific physical worlds from generated videos and trains residual policies that transfer to real manipulation; 2026 world-action-model work transfers synthetic priors to a Franka arm without real demonstrations on four tasks. DayDreamer, Cosmos Policy, DreamGen, and Gen2Act establish adjacent learned-world or generative-policy loops. These are important counterexamples to the old claim that the loop does not exist, but they do not yet establish robust multi-setting, multi-embodiment superiority over strong classical simulation.
Individual: In LIBERO or RoboCasa, train a policy from a learned video/world model and a classical physics simulator using identical synthetic trajectories, then evaluate both on the same held-out visual and dynamics shifts.
Small lab: Use one physical arm for three manipulation tasks and two scene shifts; cap real interactions and compare learned-world, domain-randomized simulator, mixed, and imitation baselines with blinded success scoring.
Well-funded lab: Run a preregistered multi-embodiment, multi-site transfer study with equal real-data budgets, safety cages, independent replication, and a full accounting of generation, reconstruction, simulation, and policy-training compute.
PhysWorld reports an average 82 percent success versus a 67 percent zero-shot baseline across its open-world manipulation evaluation, using generated task video and physical reconstruction; scope and simulator fidelity remain limiting factors.
Efficient Sim-to-Real Transfer of World-Action Models from Synthetic Priors reports about 800 synthetic demonstrations per task and 35 percent average Franka success over four tasks with no real demonstrations, but does not prove that the world-action model is necessary or better than a matched classical simulator.
DayDreamer learns online on physical robots, and Cosmos Policy performs strongly on standard manipulation suites, but neither is a clean demonstration of multi-embodiment policies trained mainly in dreamed worlds.
High for robot world models and generative policies; medium for controlled learned-world versus classical-simulator comparisons across physical settings.
Medium in simulation, medium-low for one physical arm, and low for independent multi-embodiment closure.
Maximum likelihood, hypothesis testing, the Bayes–Laplace tradition. The bedrock mathematics machine learning repeatedly rediscovers.
Kalman filtering formalized optimal linear-Gaussian state estimation and became widely used in navigation, tracking and control, often alongside particle filters, factor graphs and learned estimators.
Duda & Hart: classification as statistics. Machine learning before the name existed.
Fit models with hidden variables by alternating expectation and maximization. The engine under HMMs, mixtures and topic models.
Valiant: what is learnable, from how many examples? Learning theory born — rigor that both guided and lagged practice.
Interpretable recursive splits. Still royalty on tabular data through their ensemble descendants.
Modular probabilistic reasoning; rescued uncertainty from logic's grip, won Pearl a Turing Award, and seeded the causal turn.
Sample or bound intractable posteriors. The machinery under Bayesian ML — and, via the ELBO, under VAEs and diffusion.
Max-margin plus the kernel trick: ML's monarch 1995–2010, with actual theory. Dethroned by deep nets; retired to small-data niches.
Priors over functions with honest uncertainty. Elegant, O(n³)-cursed — survives inside Bayesian optimization.
Committees of weak learners (AdaBoost→XGBoost). Thirty years on, still the champion of messy tabular business data.
Conditional random fields were central to structured prediction in 2000s NLP and vision. Neural models displaced them from the frontier, but CRFs remain useful where structured labels and explicit dependencies matter.
Latent Dirichlet allocation modeled corpora as mixtures of themes. Embeddings and LLMs displaced it from the frontier, while interpretable corpus exploration and digital-humanities uses persist.
Netflix Prize matrix factorization → trillion-parameter ad models. The profitable dark matter of ML — huge, deployed, rarely glamorous.
do-calculus and counterfactuals: reasoning about interventions, not correlations. Deep learning's acknowledged blind spot; integration keeps being attempted.
Bayesian optimization tunes expensive black boxes sample-efficiently; neural architecture search and population-based training broadened AutoML into architecture and schedule search. Useful everywhere, fully automatic science nowhere.
When is automatic search more sample-efficient and reproducible than expert-designed architectures and schedules?
Models expressed as programs with automated inference, including Stan and language-based systems descended from Church. A specialist but active ecosystem used in science, statistics, and uncertainty-aware modeling.
Deep ensembles and calibration estimate uncertainty empirically; conformal methods add distribution-free coverage guarantees under stated assumptions. A broader reliability toolkit than any one method — still underused in LLM deployment.
How should calibration, epistemic uncertainty and coverage guarantees be combined under non-exchangeable deployment data?
Research direction: test whether explicit causal intervention and counterfactual structure improves learned-world-model planning under distribution shift. Partial causal-world systems exist; reliable gains at useful scale and under hidden confounding remain unresolved.
When do explicit intervention and counterfactual structures improve learned-world planning under distribution shift relative to equally capable noncausal world models?
On held-out interventions and mechanism changes, a causal world model improves planning success, calibration, and sample efficiency over matched latent-dynamics and invariant-learning baselines, with gains surviving misspecified graphs and latent confounding.
Causal World Models use unsupervised deconfounding and report improvements in reinforcement-learning sample complexity and counterfactual physical reasoning.
CausalVerse provides high-fidelity configurable simulations with known causal structure and intervention histories.
Existing causal-control systems remain mainly simulated or sensitive to structural assumptions.
Individual: Reproduce a causal-world-model result in CausalWorld and add hidden-confounder, mechanism-shift, and graph-misspecification stress tests.
Small lab: Compare explicit causal, invariant, and standard latent world models on matched CausalVerse training data and unseen intervention families.
Well-funded lab: Collect controlled real-robot interventions and test whether simulation-learned causal structure improves adaptation to altered objects, actuators, and dynamics.
Causal World Models explicitly combine learned dynamics, latent deconfounding, counterfactual prediction, and reinforcement learning.
Explainable reinforcement learning via causal world models combines causal structure with control and explanation.
Interventional causal-representation methods learn structure from intervention families in controlled settings.
Medium across causal representation learning, world models, and causal reinforcement learning.
Medium in configurable simulation and low under real-world latent confounding.
Research direction: predict and test the data regimes, shifts, modalities and tuning budgets under which deep tabular models outperform strong boosted-tree ensembles. Deep models already win in some large, multimodal or representation-rich settings; a reliable regime-selection rule remains unresolved.
Under equal tuning and total-cost budgets, which data regimes and distribution shifts reliably favor neural or tabular-foundation models over boosted trees, and which still favor trees?
Produce a leak-checked, preregistered regime map covering IID, temporal, grouped, large, high-dimensional, missing, and high-cardinality data, with equal tuning time and total compute plus latency, calibration, and energy reporting.
The earlier 45-dataset benchmark found tree-based models strongest on roughly 10,000-sample datasets.
TabArena finds deep methods catch up with larger time budgets and ensembles while foundation models excel on smaller datasets.
BeyondArena finds tabular foundation models strongest on tiny-to-medium IID data while trees and conventional deep models dominate several non-IID, large, and high-dimensional regimes.
Individual: Compare CatBoost, XGBoost, TabPFN, and TabM on 8-12 datasets using both random and temporal splits under an identical one-hour tuning budget.
Small lab: Build a preregistered 100-plus-dataset regime map including latency, energy, calibration, missingness, high-cardinality features, and temporal or group shift.
Well-funded lab: Train a tabular foundation model on disjoint source corpora and evaluate frozen cross-domain and non-IID transfer against the best trees and AutoGluon under total-cost parity.
TabR beats gradient-boosted decision trees on a benchmark designed to be favorable to them.
TabPFN and later versions lead on several small-to-medium IID suites.
TabArena shows deep ensembles catch up under larger time budgets.
BeyondArena shows that no one family dominates across IID and non-IID regimes.
Very high across boosted trees, deep tabular models, foundation models, and AutoML.
High for rigorous replication and regime mapping; medium for new architectures.
Von Neumann's cellular automata: machines that copy themselves. The origin of artificial life and complexity science.
Rechenberg & Schwefel: mutate, select, repeat — optimization borrowed from biology, born in wind-tunnel engineering.
Holland's crossover + fitness + schema theorem. The adaptationist program in computing; parent of a whole evolutionary lane.
Tierra, Avida: digital ecologies of self-replicators. The open-ended-evolution question remains open — the lane's unfinished business.
Koza: evolve the programs themselves. Produced patentable circuits, then was eclipsed by gradient methods — reborn as LLM-guided evolution.
Bio-inspired ant, particle and flocking methods became established optimization techniques with selected operational and robotics applications; integration with foundation-model robotics remains early.
Evolving network topologies. Charming results, outscaled by gradient descent — brief ES-for-RL revival in 2017, then quiet again.
Event-driven, biologically-timed neurons — the 'third generation' that never found its killer app. Persistent hope of the neuromorphic program.
Carver Mead's silicon retinas and cochleas. Neuromorphic engineering's founding — physics as computation.
Fixed random recurrence + trained readout. A clever shortcut that found its ceiling — revived interest wherever physical substrates compute.
Hawkins' cortical theory as product (Numenta). Two decades of conviction, minimal ML impact — a parallel track that never merged with the mainline.
Adleman demonstrated combinatorial computation in DNA. General-purpose molecular computing remains niche, while adjacent DNA data-storage and biosensing research continues.
TrueNorth, Loihi, SpiNNaker: silicon spikes at microwatts. Research platforms in search of a market for a decade and counting.
HHL and variational circuits promised speedups; barren plateaus and dequantization deflated them. No practical ML advantage demonstrated yet.
Optical systems perform neural-network operations using light. Photonic interconnects are nearer-term, while optical compute remains an active research program with continuing 2020s demonstrations.
Compute inside memory cells to escape the von Neumann bottleneck. Real inference chips exist; training precision remains the wall.
Evolutionary search with LLM mutation operators: new matrix-multiplication records, kernel speedups, open math results. Genetic programming's revenge.
Research direction: test whether local or biologically plausible learning rules can approach backpropagation at useful scale and map efficiently to neuromorphic or analog hardware.
Can local learning approach backpropagation at useful scale while delivering a measured hardware, memory, communication, or energy advantage?
On a useful-scale task and matched architecture, a local rule closes most of the accuracy and robustness gap to backpropagation while demonstrating reproducible end-to-end energy, memory, communication, or latency gains that include host and data-movement overhead.
Forward-Forward replaces forward and backward passes with positive and negative forward passes but was introduced on small problems.
CLAPP-like local self-supervised learning matches comparable global objectives on CIFAR-10, STL-10, and Tiny ImageNet.
Predictive coding can approximate backpropagation on arbitrary computation graphs in theory and small implementations.
Individual: Compare backpropagation, CLAPP, predictive coding, and Forward-Forward on identical Tiny ImageNet networks while measuring stored activations, communication, memory, and accuracy.
Small lab: Scale the strongest local self-supervised rule to ImageNet-100 or ImageNet-1k with matched ResNet or ViT baselines and NeuroBench-style operation metrics.
Well-funded lab: Implement the same local rule on neuromorphic or analog hardware and publish wall-plug energy, latency, robustness, and host-overhead comparisons against a conventional backpropagation system.
Local CLAPP variants match comparable global self-supervised objectives on several small vision datasets.
Predictive-coding updates can approximate automatic differentiation on arbitrary computation graphs.
Neuromorphic systems show task-specific energy advantages, although often without matched learning rules and end-to-end accounting.
Medium across local learning, predictive coding, and neuromorphic computing.
High for small-scale algorithmic work and low for credible useful-scale systems claims.
Research direction: establish a practically relevant, independently replicated quantum-learning advantage with classical data or explicitly costed quantum-data access. Provable advantages exist under specified access models; practical end-to-end advantage under full resource accounting remains unresolved.
Can an end-to-end learning workload show a reproducible quantum advantage over the strongest classical alternative after state preparation, data acquisition, error mitigation, wall-clock time, and energy are all counted?
Pre-register the access model, task distribution, hardware, classical comparison budget, and success threshold; include state-preparation, shots, calibration, error mitigation, queue and wall-clock costs; red-team the classical baseline; demonstrate scaling beyond feasible classical replication; and reproduce on independent hardware.
Provable and experimental learning advantages already exist for carefully specified quantum-data access models, including learning from experiments. Broad empirical benchmarking still finds that ordinary classical models usually outperform tested quantum models on classical-data tasks. The defensible open question is practical end-to-end advantage under full resource accounting, not whether any quantum learning advantage has ever been proved.
Individual: Reproduce selected qml-benchmarks with stronger classical hyperparameter search, identical data splits, and full training and inference timing; publish null results and sensitivity analyses.
Small lab: Implement one learning-from-experiments task on cloud quantum hardware, pre-register the best classical red team, and account for shots, calibration, state preparation, mitigation, transfer, queue, and wall-clock costs.
Well-funded lab: Run the same 30-50-qubit coherent-data learning experiment on two independent hardware stacks, with a separate classical-HPC team and audited time, energy, acquisition, and uncertainty accounting.
Quantum Advantage in Learning from Experiments proves exponential sample advantages under specified quantum-data access and demonstrates experiments up to 40 superconducting qubits and roughly 1,300 gates; those assumptions do not automatically establish a practical advantage for ordinary classical data.
The qml-benchmarks study evaluates 12 quantum models across 160 datasets grouped into 10 benchmarks and generally finds tuned classical models stronger; simulations were limited to modest qubit counts.
Recent analyses report plausible noisy-qubit separations at tens of qubits, but they are not yet an independently replicated end-to-end hardware advantage with full acquisition and runtime accounting.
Medium-high; QML is crowded, while transparent end-to-end cost accounting and adversarial classical baselines remain less common.
High for rigorous small-scale benchmarking and valuable negative results; very low for a scalable practical-advantage closure result.
GM's assembly-line arm. Automation without intelligence — but the hardware lineage AI would eventually inhabit.
First robot to integrate perception, planning and action; A* and STRIPS were invented for it. Glacial in practice, generative in consequence.
What's hard for AI (perception, motor control) is easy for toddlers, and vice versa. The embodiment reality check, still collecting evidence.
Brooks's layered behavior-based approach favored direct perception-action loops over centralized world models and strongly influenced embodied robotics and commercial mobile-robot design.
Humanoid robots served as cognition testbeds in the Cog and ASIMO era. Those programs ended without general intelligence; humanoid research returned decades later through modern control and learned foundation models.
A neural net steering a van from camera pixels at CMU. Imitation learning's proof of concept, three decades early.
Simultaneous localization and mapping is a core technique for many robots, drones and augmented-reality systems, alongside visual-inertial odometry, mapping services and learned perception.
Evolutionary and developmental robotics jointly optimize controllers, behaviors and sometimes morphology. The field remains academically active, with more limited industrial adoption than mainstream deep learning and robotics.
BigDog→Atlas backflips: model-predictive control at its athletic peak — a triumph of control theory largely without learning.
Stanley wins the desert race; the self-driving industry is born in a weekend. Benchmark-as-catalyst, the robotics edition.
MuJoCo, Gym, Isaac, Habitat: physics playgrounds where robot learning actually happens. Sim-to-real as a strategy, not a hack.
Which simulation variations produce robust transfer rather than hiding reality gaps behind benchmark success?
Grasping via RL and domain randomization (OpenAI's Rubik's hand). Sample hunger stalled it; imitation + foundation models took the baton.
A decade of overpromise, then real robotaxis at scale (Waymo, 2024–26). The long tail was the whole problem — and it's yielding slowly.
SayCan and RT-1 began the language-grounded lineage; RT-2, Open X-Embodiment, OpenVLA and π0 scaled vision-language-action learning. Robotics' claimed 'GPT moment' is still being tested.
Which forms of internet-scale pretraining transfer to safe, data-efficient physical generalization?
Optimus, Figure Helix, Unitree: billions chasing general-purpose bodies, first pilot deployments 2025–26. Hype versus unit economics, unresolved.
Research direction: scale foundation-model multi-robot systems to decentralized, online-adaptive and fault-tolerant swarms. Early systems already combine large models with multi-robot coordination; robust embodied scaling remains unresolved.
Can foundation models improve embodied swarms while preserving decentralized execution, scaling to many inexpensive agents, and degrading gracefully under communication, member, sensor, and model failures?
Show increasing team utility across at least three swarm sizes; keep execution decentralized with explicit per-agent compute and bandwidth caps; compare against strong modular decentralized baselines; test packet loss, partitions, Byzantine or faulty members, sensor corruption, and model failure; and transfer to a physical swarm.
CRAFT uses language and vision-language foundation models to coach reinforcement learning for multi-robot coordination, and DeepFleet trains multi-agent foundation models on warehouse fleet movement data. RoCo and SMART-LLM coordinate smaller robot teams with language models. These invalidate a claim that the combination is nearly untouched, while leaving scalable online decentralized swarm intelligence and fault tolerance unresolved.
Individual: In ARGoS or Crazyswarm2 simulation, compare a local foundation-model coach with a modular decentralized controller across swarm sizes and a fixed packet-loss, sensor-noise, and member-dropout matrix.
Small lab: Test six to twelve inexpensive robots under packet loss, partitions, robot dropout, and stale or adversarial guidance; include centralization and communication-budget ablations.
Well-funded lab: Run a 50-plus heterogeneous-robot, multi-site study of online shared learning with decentralized execution, predefined graceful-degradation curves, safety supervision, and independent robustness auditing.
CRAFT trains coordinated multi-quadruped navigation and bimanual manipulation policies with foundation-model coaching and reports physical-policy transfer, but it is not a large, cheap, online-adaptive swarm under broad failures.
DeepFleet models warehouse fleets using data from hundreds of thousands of operational robots and robot-centric local graphs, but focuses on prediction and fleet design using proprietary operational data rather than general online swarm control.
RoCoBench and SMART-LLM show language-model planning for small multi-robot teams; scale, decentralization, latency, and graceful degradation remain limited.
Medium for language-model multi-robot planning; low-medium for decentralized foundation-model swarms with systematic fault tolerance.
High in simulation, medium for a small physical fleet, and low for large heterogeneous swarms with convincing safety and robustness.
Fiction framed machine ethics decades before engineering could. Set the cultural frame every real safety debate still argues with.
'If we use, to achieve our purposes, a mechanical agency with whose operation we cannot interfere…' — value misalignment stated plainly, in 1960.
ELIZA's own creator warned against delegating judgment to machines. AI criticism as a serious intellectual tradition begins here.
Hutter: Solomonoff induction + RL as the optimal agent. Incomputable idealization — a framing device that shaped early alignment theory.
Yudkowsky's decision-theoretic alignment program for hypothetical superintelligences. Largely wound down by 2024 (pivot to advocacy) — a research road that ran out before LLMs arrived.
Orthogonality, instrumental convergence, the treacherous turn. Made existential risk respectable in policy and tech circles.
Amodei et al. translated alignment into ML problems: reward hacking, safe exploration, scalable oversight. The field's practical founding paper.
Training reward models from human comparisons became an important component of instruction-following assistants, alongside supervised fine-tuning, data curation and safety systems; reward-model overoptimization remains a persistent specification risk.
How can preference optimization resist reward-model overoptimization and evaluator misspecification?
Measuring and mitigating disparate harms — COMPAS, face-recognition audits, dataset documentation. Ethics as an ML subfield, and a political flashpoint.
Imperceptible perturbations that fool networks. A decade of attack–defense arms race — unsolved, now mostly managed rather than fixed.
Early interpretability (saliency→circuits)
Feature visualization, attribution maps, Olah's circuits program. From pretty pictures toward an actual science of what networks compute.
Neural saliency and feature visualization 2013-2014
Alignment via argumentative games and recursive oversight. Theory-rich, deployment-poor — folded into today's scalable-oversight research.
Induction heads, superposition, sparse autoencoders finding millions of features. Reverse-engineering models — with a live debate over whether SAE features are real.
Models critique models against written principles — scaling oversight past human labeling. Anthropic's flagship alignment recipe.
How robust are constitutional defenses under adaptive attacks and changing tool access?
Preference alignment without an RL loop. Made alignment tooling commodity — for better (open models) and worse (easy de-alignment).
The attack surface of instruction-followers. Prompt injection remains the unsolved core vulnerability of agentic AI — the more tools, the more surface.
Can prompt-injection defenses remain useful under adaptive attackers without blocking legitimate tool use?
OpenAI's 20%-of-compute pledge to align superintelligence — team dissolved within a year amid departures. A very public dropped thread; weak-to-strong work survived it.
Can weaker judges reliably train stronger models? The successor question once human feedback saturates — alignment's next hard problem.
What feedback remains trustworthy when the model is more capable than its evaluator?
METR, AISIs, dangerous-capability evals, model cards. Measuring what models can do became its own research field — and the science is young.
Which evaluations predict costly real-world behavior rather than benchmark-specific competence?
Law arrives: compute thresholds, transparency duties, incident reporting, safety institutes. Research and regulation now co-evolve.
Formal world-models and proof-carrying AI (Tegmark–Russell program). Aspiration far ahead of tooling — a program waiting for its methods.
Model editing and activation steering can change local facts and behaviors. Still missing: scalable, predictable interventions grounded in causal understanding, with robust generalization and meaningful guarantees.
Can interpretability methods support ex ante predictions of durable model-control interventions, including composition, persistence, and bounded side effects across models and tasks?
Which causal abstractions support interventions that stay reliable across prompts, tasks and model updates?
Before applying each intervention, predict its target effect, collateral effects, interactions with other interventions, and persistence after additional training or context shifts; beat non-mechanistic baselines across multiple model families and seeds; and reproduce the prediction-to-control mapping independently.
Activation steering, representation engineering, sparse autoencoders, causal tracing, and model editing can alter behavior. AxBench finds simple prompting and fine-tuning baselines often beat sparse-autoencoder steering, while localization-versus-editing studies show that causal localization does not reliably identify the best place to edit. Early 2026 work predicts some steering side effects, but results remain method-, dictionary-, and model-dependent.
Individual: On two open small models, use AxBench and CounterFact to predict target and collateral changes before each intervention, then publish calibration curves and all failed predictions.
Small lab: Build a cross-model, cross-seed challenge spanning prompting, fine-tuning, steering, sparse autoencoders, and editing, with tests of intervention composition and persistence after additional training.
Well-funded lab: Run a blinded, preregistered intervention tournament across model families in which one team predicts effects, another executes interventions, and an independent group evaluates hidden targets and side effects.
AxBench provides a broad steering benchmark and finds prompting strongest overall, supervised fine-tuning next, and sparse autoencoders often uncompetitive; it is evidence both of controllability and of weak mechanistic advantage.
Causal localization can identify behaviorally relevant components, yet the localization-versus-editing study finds it does not consistently predict the most effective edit layer.
Recent work predicts some sparse-autoencoder steering side effects before intervention, but generalization varies across models, dictionaries, and target concepts.
High; mechanistic interpretability and steering are crowded, but preregistered prediction-to-control and independent replication are less common.
High for bounded falsification studies, medium for cross-model predictive rules, and low for durable general guarantees.
Research direction: develop testable containment, permission and corrigibility guarantees for tool-using agents under realistic interfaces and adversarial conditions.
Can enforceable least-privilege and containment policies for adaptive tool-using agents survive realistic prompt injection, delegation, persistence, privilege escalation, and sandbox-escape attacks?
Specify the policy and threat model formally; enforce it below the model layer; test benign utility and adaptive attacks across real tool classes, delegation, memory, restart, identity, network, and code execution; show bounded failure probability under repeated trials; and obtain an independent penetration test.
AI Control shows that monitoring and protocol design can improve safety even when an untrusted model intentionally subverts a coding task. AgentDojo and Agent Security Bench provide realistic tool-agent prompt-injection and security suites, while SandboxEscapeBench tests container-escape capability. These are substantial defenses and measurements, but none establishes general containment under an adaptive real-tool threat model.
Individual: Add a deny-by-default, provenance-aware least-privilege wrapper to AgentDojo and compare it with prompt-only defenses on both benign utility and adaptive indirect prompt injection.
Small lab: Combine AgentDojo, Agent Security Bench, and sandbox-escape tasks in gVisor or Firecracker; red-team delegation, memory, restart, exfiltration, and privilege transitions across four model families.
Well-funded lab: Build an isolated replica of a real enterprise tool environment with formal identity, network, filesystem, and approval policies; commission independent model red teaming and a conventional penetration test.
AI Control demonstrates that trusted monitoring, auditing, and protocol design improve outcomes against an intentionally subversive untrusted coding model, within a bounded backdoor-insertion setting.
AgentDojo contains 97 benign tasks and 629 security test cases across four tool domains; tested agents and defenses still trade off utility and security.
Agent Security Bench spans ten scenarios, more than 400 tools, and roughly 90,000 tests, while SandboxEscapeBench adds realistic container-escape tasks; neither alone proves containment.
High for empirical agent security and prompt injection; lower for formal, below-model enforcement tested against adaptive end-to-end attacks.
High for benchmark and least-privilege improvements, medium for robust production patterns, and very low for broad formal containment guarantees.
Transistor counts double every ~2 years. The metronome under every AI wave — and every winter's eventual thaw.
Crawls, Wikipedia, forums, Common Crawl. Humanity accidentally wrote its own training corpus.
Human annotation at industrial scale — made ImageNet possible, and prefigured today's RLHF data industry.
Graphics chips repurposed for general math. The hardware accident on which the entire deep-learning era balances.
Theano→TensorFlow→PyTorch: differentiable programming for everyone. Research velocity ×10; the field's shared workbench.
Silicon co-designed for tensors; NVIDIA's datacenter empire and its challengers. Compute as the strategic resource of the era.
Weights as a commons. The GitHub of ML — distribution infrastructure that made 'open-weight ecosystem' a real thing.
Data/model/pipeline parallelism (Megatron, ZeRO): choreography for ten thousand GPUs. Scaling laws made physical.
$100M+ training runs put frontier science behind capital walls. Academia's role crisis — and the open-weight movement's raison d'être.
MMLU→HLE: tests dissolve on contact with web-scale training. Measuring minds is now a bottleneck science.
Lawsuits, robots.txt revolts, licensing deals, synthetic-data pivots. The free-data era that built the field is over.
Standardized model-tool-data plumbing and orchestration. The 'HTTP moment' for agents, possibly — the plumbing decides what agents can safely do.
Serving, caching, routing, token pricing: the cost curve that decides what actually deploys. Where systems research meets business model.
Gigawatt campuses, nuclear PPAs, grid politics. AI's growth curve collided with physical infrastructure — the binding constraint after chips.
RL placement shipping in production TPUs. The ouroboros: AI improving the hardware that trains AI.
Research direction: sustain capability improvement under a constrained or synthetic-only data supply while preventing collapse, contamination and diversity loss. Collapse is not inevitable when real-data anchors or external diversity controls remain; the stricter synthetic-only capability ratchet remains unresolved.
Can repeated generations of synthetic or self-generated experience preserve or improve held-out capability and diversity without fresh human data, contamination, or a hidden stronger teacher?
Run multiple recursive generations with immutable held-out human evaluations and provenance; measure average quality, tail coverage, diversity, calibration, memorization, and contamination each round; forbid fresh human data and undisclosed stronger-teacher labels after initialization; compare replacement, accumulation, curation, multi-teacher, and self-training regimes; and reproduce independently.
Recursive replacement with generated data can erase distribution tails and cause model collapse. Accumulating synthetic data with the original real distribution can avoid collapse in several modalities, and diverse or pluralistic curation can stabilize bounded regimes. These are genuine counterexamples to inevitability, but they retain real-data anchors, external rewards, multiple teachers, or bounded tasks and do not yet show a sustained synthetic-only capability ratchet.
Individual: Train a 100M-1B model for 10-20 recursive rounds under replacement, accumulation, and diversity-curation conditions using one cryptographically frozen human holdout and complete data lineage.
Small lab: Run a 1B-8B text or multimodal study for 10-30 rounds, auditing contamination, rare-skill retention, diversity, calibration, and teacher dependence with external evaluators fixed before training.
Well-funded lab: Run a 70B-scale, 30-generation multi-teacher experiment with no fresh human data after initialization, immutable external evaluation, explicit teacher-capability accounting, and an independent provenance and contamination audit.
The Curse of Recursion demonstrates progressive loss of distribution tails when generated data replace real data across language and generative-model settings.
Is Model Collapse Inevitable? shows that accumulating synthetic data alongside the original real data avoids collapse in language, molecular, and image experiments; this does not satisfy a no-fresh-anchor synthetic-only loop.
Recent curated, diverse-teacher, and pluralistic-reward systems can delay or prevent collapse in bounded settings, but depend on external selection signals, anchored data, or stronger and diverse teachers.
High; synthetic data, self-training, and model collapse are active topics, but deeply controlled multi-generation studies remain expensive and less common.
High for causal small-model studies, medium for 1B-8B replication, and low for frontier-scale conclusions.
AlphaFold 2 achieved near-experimental accuracy on many CASP14 protein-structure targets, and the AlphaFold Database expanded access to more than 200 million predictions. It is a major AI-for-science result, not a universal solution to structural biology.
ESM language models and RFdiffusion: read protein space like text, then write new proteins into it. Design, not just prediction.
Complexes, DNA/RNA, ligands via diffusion. Drug-discovery pipelines re-tooled around learned structure.
Learned weather models such as GraphCast and GenCast reported strong benchmark performance and much lower inference cost than numerical forecasting systems on selected tasks. Operational adoption is growing, while physics-based models remain essential.
Operational learned forecasting expansion 2025
2.2M predicted crystals, autonomous labs synthesizing candidates — with a live replication debate about how many are truly new. Promise and friction together.
How should predicted novelty and synthesizability be independently validated before claiming discovery?
Deep RL shaping tokamak plasmas (TCV). Control physics by learning — a template for AI in big experimental science.
AlphaGeometry reached Olympiad-level geometry in 2024, while AlphaProof and AlphaGeometry 2 achieved silver-medal-equivalent IMO performance that year; later 2025 systems reported gold-level results.
Radiology assist → clinical LLMs (Med-Gemini class). Regulation, liability and workflow — not accuracy — are now the rate limiters. Watson's failure is the syllabus.
Long-context models reading regulatory DNA and whole genomes. The genome as a language — the next AlphaFold-shaped bet.
Agentic hypothesis, literature and experiment pipelines have produced reported algorithmic and scientific results in bounded domains. Independent reproduction and research-integrity practice are still developing.
Domain-specific self-driving laboratories already close design–execute–learn loops. The open challenge is reliable, reproducible autonomy that forms hypotheses and transfers across instruments, domains and scientific objectives.
Can one autonomous scientific system transfer across instruments and scientific domains while keeping experiments safe, uncertainty calibrated, provenance complete, and results independently reproducible?
Which parts of scientific judgment transfer across domains, and how should autonomous labs document uncertainty and failed experiments?
Pre-register transfer from at least one training domain or instrument to two materially different targets; require calibrated abstention and human safety gates; preserve machine-readable lineage from hypothesis through raw data and analysis; compare against domain-specific automation and human teams; and have an independent lab reproduce a claimed result from the released record.
A 2026 autonomous laboratory agent operates scanning probe microscopy through a domain-specific model and deterministic modular interface. AI Scientist-v2 automates computational machine-learning research across several subdomains, and earlier Coscientist work demonstrated bounded chemistry workflows. These systems establish real partial autonomy, but none closes cross-instrument, cross-domain transfer with independent physical reproduction and calibrated uncertainty.
Individual: Build a provenance-first agent for DiscoveryBench and ScienceAgentBench that must produce an executable rerun bundle, calibrated confidence, and an explicit abstention whenever evidence or permissions are missing.
Small lab: Connect the same policy layer to two instruments, or one instrument plus a high-fidelity simulator, preregister transfer tasks, retain human safety approval, and have a second operator independently repeat the result.
Well-funded lab: Run a two-domain, two-site autonomous-science challenge with different instruments, robotic sample handling, blind replication, machine-readable lineage, uncertainty calibration, and independent safety and research-integrity oversight.
Autonomous Laboratory Agent controls real-time atomic-resolution scanning probe microscopy using a customized domain model, deterministic validation, and modular orchestration; it remains a single instrument family and domain.
AI Scientist-v2 performs template-free computational ML research and produced workshop-level manuscripts, including one accepted at a workshop with a high acceptance rate; it does not demonstrate transfer across physical instruments or independent experimental reproduction.
Coscientist and other self-driving-lab systems demonstrate chemistry and materials workflows, while DiscoveryBench and ScienceAgentBench show that broad data-science agents still solve only a minority of realistic tasks.
High for computational science agents, medium for domain-specific self-driving labs, and low for the same system transferring across physical domains and sites.
High for data-only provenance studies, medium for a single-instrument system, and low for cross-domain transfer with independent physical reproduction.
Subword tokenization (BPE → SentencePiece)
Open-vocabulary modeling with reusable word pieces made rare words, multilingual translation and modern LLM vocabularies practical. The quiet interface between raw text and neural sequence models.
Can learned or token-free representations improve multilingual efficiency without destabilizing training?
Membership inference and related attacks showed that trained models can reveal whether specific records — and sometimes sensitive attributes — were present. Privacy became an empirical attack surface, not only a training objective.
How can leakage be measured and mitigated under realistic black-box access without hiding behind average-case metrics?
Models can be confidently wrong when deployment data differs from training. Detection baselines, shift benchmarks and robustness methods made this a distinct reliability field rather than an edge case.
Can systems recognize consequential distribution shift without labels and abstain without crippling useful coverage?
Modern imitation learning (DAgger → GAIL)
DAgger confronted policy-induced distribution shift; GAIL learned behavior by adversarially matching expert demonstrations. Together they formed the modern route from demonstrations to sequential policies.
How can demonstration learning remain robust when the deployed policy visits states the demonstrator never covered?
Off-policy actor–critic learning extended deep RL from discrete games to continuous actions; maximum-entropy SAC made the recipe substantially more stable for locomotion and manipulation.
Which off-policy control methods remain stable under sparse rewards, imperfect simulators and real hardware constraints?
Lower-precision arithmetic with loss scaling accelerated training and reduced memory without sacrificing model quality. It became a standard training-systems technique, distinct from inference quantization.
How low can training precision go while retaining predictable convergence and debuggable failure modes?
Discrete neural representations & codecs
VQ-VAE learned discrete codebooks; VQGAN and neural audio codecs turned images and sound into learned tokens. This lineage connects generative models, compression and multimodal tokenization.
Can learned discrete representations unify text, audio, images and actions without destructive information bottlenecks?
Adversarial training, SMT-based checking and certified defenses test or guarantee bounded neural-network behavior. These are working methods, though far narrower than whole-agent safety proofs.
How can formal guarantees scale beyond small feed-forward networks to adaptive, tool-using systems?
Training-time and supply-chain attacks implant trigger-dependent behavior that can survive ordinary retraining or safety tuning. This is distinct from test-time adversarial examples and prompt injection.
Can training-pipeline backdoors be detected before deployment when triggers and attacker objectives are unknown?
PINNs place differential equations and boundary conditions inside the training objective for forward and inverse problems. They opened a major scientific-ML program while retaining known optimization and scaling limits.
When do physical constraints improve extrapolation, and when do PINN optimization pathologies dominate?
Architectures that respect rotation, translation and other symmetries became foundational for molecular, physical and 3D learning. Built-in geometry improves data efficiency and physical plausibility.
Which symmetries should be encoded exactly, approximated, or learned from data in complex physical systems?
Inner alignment & goal misgeneralization
A capable learned policy can pursue an unintended objective after distribution shift even when the training reward looked correct. Experiments establish the failure mode, not that every example is a mesa-optimizer.
Can a learned policy's operative objective be diagnosed before distribution shift reveals it behaviorally?
TVM and MLIR created optimizing layers between model graphs and heterogeneous hardware. Compilation, scheduling and kernel generation became essential parts of ML performance rather than afterthoughts.
Can automatic scheduling deliver portable performance across rapidly changing accelerators without opaque regressions?
Domain-specific systems close a design–execute–measure–learn loop with robotics and active learning. They demonstrate real autonomous experimentation without yet providing a general-purpose robot scientist.
How can autonomous laboratories transfer across instruments and domains while preserving calibration, safety and reproducibility?
Dual encoders learned semantic query and document spaces, replacing much lexical matching in open-domain QA. Dense retrieval is the missing systems-and-model bridge into retrieval-augmented generation.
How should retrievers manage freshness, provenance, adversarial documents and uncertainty rather than only top-k relevance?
DeepONet and Fourier Neural Operators learn mappings between functions rather than one fixed discretization. The approach now powers fast surrogate models in weather, fluids and engineering.
Can learned operators preserve conservation laws and generalize across unseen geometries, grids and boundary conditions?
Speech foundation models (wav2vec 2.0 → Whisper)
Self-supervised speech representation learning and weakly supervised web-scale transcription replaced task-specific acoustic pipelines with reusable speech models and robust multilingual transfer.
How can speech models serve low-resource languages and noisy real-world audio without erasing dialect and speaker variation?
Supervised fine-tuning across diverse natural-language tasks taught pretrained models to follow new instructions zero-shot. It is a separate lineage from preference modeling and RLHF.
What task mixtures create genuine instruction generalization rather than benchmark-template recognition?
Continuous-time vector-field learning offers a direct path from noise to data, often with straighter and faster trajectories than diffusion sampling. It is now a major generative-model branch.
Which probability paths yield stable training, few-step generation and trustworthy likelihood or coverage behavior?
Speculative decoding, continuous batching and PagedAttention attack latency, memory fragmentation and throughput. Serving systems are a distinct research layer from model compression.
How should serving jointly optimize latency, throughput, memory, energy and tail reliability under heterogeneous workloads?
WebArena, AgentBench, SWE-bench and OSWorld evaluate stateful execution in websites, repositories and computers. They expose planning and recovery failures that static question-answer benchmarks miss.
How can interactive benchmarks remain reproducible, contamination-resistant and representative of costly real-world failures?
ACT predicts temporally chunked actions; Diffusion Policy models multimodal action distributions. These demonstration-driven policy representations bridge classical imitation learning and vision-language-action models.
How can generative robot policies handle long horizons, contact-rich recovery and distribution shift outside demonstrations?
Targeted weight edits and inference-time activation interventions can change facts or behaviors locally. Reliability, side effects, causal interpretation and generalization remain active limitations.
Can interventions be made predictable, composable and durable without damaging unrelated capabilities?
Control research evaluates how trusted monitors, resampling, editing and limited human audits can constrain an intentionally subversive model. It is an active defense–attack program, not a solved safeguard.
Which control protocols survive adaptive attacks when trusted monitoring and human audit capacity are scarce?
Self-generated curricula & self-editing agents
Voyager, Absolute Zero and Darwin Gödel Machine generate tasks, skills or agent modifications and retain measured improvements. Results are promising but bounded by designed environments, verifiers and benchmarks.
Can improvement remain general, cumulative and safe without a fixed benchmark, hand-designed verifier or hidden human curriculum?
Model-Agnostic Meta-Learning optimized parameters for rapid adaptation from a few examples. It made learning-to-learn concrete, though foundation-model adaptation later changed the center of gravity.
What remains uniquely useful about gradient-based meta-learning once large pretrained models can adapt in context?
Instead of one optimum, MAP-Elites builds an archive of diverse high-performing solutions across behavioral niches. The archive idea feeds evolutionary design and open-ended exploration.
How should behavior descriptors and archives scale when useful novelty cannot be specified in advance?
DeepCoder and DreamCoder use learned guidance, search and reusable abstractions to synthesize programs from examples. The lineage links symbolic search to modern code generation.
Can learned guidance accelerate synthesis while preserving executable correctness, abstraction and compositional generalization?
Entropic regularization made transport distances computationally practical; Wasserstein objectives then influenced generative modeling, domain adaptation and representation learning.
Can transport objectives scale without sacrificing geometry, sample quality or numerical stability?
Top-p sampling truncates the unreliable probability tail dynamically, improving open-ended generation. Decoding policy became a consequential layer between model probabilities and visible behavior.
Can decoding adapt to uncertainty and task risk without masking poor model calibration or amplifying bias?
Unlearning asks a trained model to remove the influence of selected data without full retraining. Verification, utility preservation and realistic deletion guarantees remain open research problems.
What evidence is sufficient to verify that a record's influence was removed from a large generative model?
Vision-and-language navigation and later SayCan ground language instructions in perception, affordances and executable skills. This is the planning bridge into robot foundation models.
How can language plans remain grounded under uncertain perception, changing affordances and execution failures?
Connection retained from the curated map; causal or historical direction has not been individually established.
Editorially proposed research combination; not a claim that the work already exists.
The later policy-optimization family explicitly extends policy-gradient methods.
Restricted Boltzmann machines are a constrained Boltzmann-machine form.
Deep belief networks were constructed from stacked restricted Boltzmann machines.
Layer normalization is an explicit Transformer component.
Legacy supersession claim retained for review; it is not treated as established supersession.
Layer normalization addresses related normalization goals with a different axis of normalization.
Contextual representation work preceded and influenced bidirectional pretrained language models.
Information-retrieval machinery enables retrieval-augmented generation.
U-Net-style backbones are a core implementation component in influential diffusion systems.
U-Net was introduced for biomedical image segmentation and became an application lineage.
Exact article for the stochastic process.
“Foundational” is the map's editorial ranking; the article confirms the development separately, not that ranking.
Best labeled article for the formalist program; the node also reaches back to Frege.
Exact article for Shannon's thesis result.
Covers the McCulloch-Pitts formal neuron; no dedicated current article.
Exact field article covering the von Neumann-Morgenstern foundation.
Best article covering Hubel and Wiesel's receptive-field findings.
Directly covers the first bundled development; MDL has a separate article.
Exact article covering Cook-Karp foundations.
Directly covers the first bundled critique; symbol grounding has a separate article.
Best labeled article for the symbolic-connectionist and systematicity debate.
“Active” is an editorial assessment of current activity, not a conclusion established by this Wikipedia mapping.
This is a map hypothesis; Wikipedia absence does not establish that the proposed gap is novel.
This is explicitly a proposed gap; Wikipedia absence is not evidence that the proposal is novel.
The SHRDLU page documents that its success did not carry over to realistic ambiguity and complexity, but it does not describe a formal project ending or direct supersession.
“Dormant” is an editorial assessment; no encyclopedic fate finding is inferred from article or link presence.
The page explicitly links the report to UK funding cuts and an AI winter, but does not describe the report itself as a superseded approach.
The Mycin page explicitly says MYCIN was never used in routine practice and that its approach continued through EMYCIN and later systems; this supports historical nondeployment, not a simple failure claim.
Covers EURISKO and its relationship to AM; the node bundles both systems.
The page documents XCON's success and related rewrites but does not explicitly say XCON ended or lost its leading role.
Directly covers the first bundled topic; the frame problem has a separate article.
Best labeled article for the expert-system and shell industry rather than one product.
The Expert system page says the standalone expert-system term mostly disappeared and presents both failure and absorption into standard rule-engine tooling as interpretations, so only the standalone industry framing is supported as ended.
The page records LMI's bankruptcy and the demise of its product effort, but does not explicitly attribute the overall hardware category's end to commodity workstations.
The Fifth Generation Computer Systems page explicitly says the ten-year project ended in commercial failure and was surpassed by less specialized hardware.
Best common article because the node bundles Soar and ACT-R.
The article covers both AI winters, including the second.
The AI winter page dates the second major winter to roughly 1987-2000, documents the associated industry collapse and abandonments, and contrasts it with later renewed interest and the current boom.
Direct article for interactive theorem provers and assistants.
“Revived” is an editorial judgment; no encyclopedic fate finding is inferred from article or link presence.
Best article covering Shannon's 1950 chess-programming blueprint.
Directly covers the central pruning development; the node also bundles heuristic evaluation.
Exact article for STRIPS and its planning language.
Exact article for the system; the match has a separate article.
Directly covers SAT solvers; the node also bundles SMT solvers.
Best article covering Libratus, Pluribus, CFR, and solved poker variants.
No clear English Wikipedia article directly or cleanly contextually covers the 2023 LLM-guided-search synthesis. No direct article was recorded; this absence is not evidence against the development.
“Hot frontier” is a time-sensitive editorial assessment, not a conclusion established by this Wikipedia mapping.
The computer-players section directly documents Arthur Samuel's adaptive checkers program.
Direct article covering REINFORCE, actor-critic methods, TRPO, and PPO.
The reward section directly covers dopamine as a temporal-difference-like reward-prediction-error signal.
Best field article; it cites and uses Sutton and Barto's synthesis, but the book lacks a dedicated page.
The article explicitly includes modular and hierarchical reinforcement learning as a research topic; no dedicated current page.
Directly covers Deep Q-Network; DQN itself is a disambiguation page.
Directly covers AlphaZero; the bundled MuZero development has its own page.
Exact article for PPO and its TRPO predecessor.
Exact article for curiosity-driven intrinsic rewards.
Best labeled article for learned world models; not specific to the Ha-Schmidhuber paper alone.
The algorithms section directly covers model-based deep RL and Dreamer; no dedicated current page.
Directly covers one half of the bundled node; AlphaStar has a separate page.
Its open-problems section directly discusses open-ended evolution; POET lacks a dedicated page.
The article directly defines offline or batch RL; no dedicated offline-RL page.
Best labeled article for tool-using LLM agents; the node is a broad synthesis.
The DeepSeek article documents DeepSeek-R1 as a prominent verifiable-reward reasoning system; no dedicated RLVR concept page exists.
The Perceptrons book page says the book is often thought to have caused a decline in neural-network research, while also noting continued work and a later revival; the stronger kills-the-field framing remains disputed.
Direct article for the named model and its deep-belief-network lineage.
The second-wave section directly covers the PDP volumes and revival; no dedicated book page.
Exact article; avoids the LSTM disambiguation page.
The Deep belief network page describes the architecture and training method but does not explicitly document its ending, failure, or supersession.
Exact article and it identifies DNC as an outgrowth.
The page defines Neural Turing Machines but does not explicitly characterize the approach as abandoned, ended, or superseded.
Exact article; avoids the ResNet disambiguation page.
No dedicated machine-learning hypernetwork article; current search hits are sections in other topics or collisions. No direct article was recorded; this absence is not evidence against the development.
Exact article; avoids unrelated Transformer pages.
Exact article; avoids the Capsule disambiguation page.
The Capsule neural network page describes an ongoing architecture and research variants without an explicit ending, failure, or supersession statement.
Its transformer history directly discusses linear attention and fast-weight equivalence; no dedicated current page.
Directly covers Mamba and its S4 ancestry; the node bundles the broader S4-to-Mamba lineage.
No current English Wikipedia article directly or clearly contextually covers Titans test-time memory. No direct article was recorded; this absence is not evidence against the development.
Exact phenomenon article; Wikipedia uses catastrophic interference as the canonical title for catastrophic forgetting.
Direct for ReLU, but the node also combines dropout and the broader practical-depth transition.
The article covers Adam within stochastic gradient descent; AdamW’s decoupled-weight-decay extension is supported by the attached primary paper.
Direct for BatchNorm but only contextual for the node's BatchNorm-to-LayerNorm normalization era.
The article's layer-normalization section directly covers the development; the former exact title redirects here.
Exact field article for the pretrain-and-fine-tune development.
Exact article for Sutton's 2019 essay and principle.
Direct umbrella article for neural scaling laws, including compute, data, and inference scaling.
Exact Low-Rank Adaptation technique article.
Relevant umbrella for quantization, pruning and sparsity; the repaired node now limits itself to model compression.
FlashAttention currently redirects into the transformer article; there is no standalone canonical page for the wider kernel-craft node.
Direct for the data-centric approach but broader curation and synthetic-data claims remain contextual.
The current test-time-compute title redirects to this broader scaling-law article.
The DeepSeek article covers R1 and GRPO, but it is broader than the node's efficient-RL recipe.
Continual learning currently redirects to this canonical synonymous field article.
Exact privacy formalism, but broader than its machine-learning deployment summarized by the node.
Direct for NTK, one strand of a node that also combines double descent and lottery-ticket theory.
Direct for the 1954 demo and useful context for ALPAC, but the node combines the whole boom-bust trajectory.
The page explicitly says expectations were not fulfilled and funding was dramatically reduced after ALPAC, but it does not call the demonstration itself superseded.
Direct for conceptual dependency but only contextual for the node's combined scripts-and-story-understanding program.
Exact model family, used contextually for its long speech-recognition era.
The assigned Hidden Markov model page still lists speech recognition among current applications and does not document the speech-recognition lineage as ended or superseded.
Exact specialized article for word n-gram language modeling.
The Word n-gram language model page explicitly says word n-gram models were largely superseded first by recurrent neural models and then by Transformer-based models.
Direct article for the vector-space information-retrieval development.
Penn Treebank currently redirects to this broader treebank article.
Exact machine-translation paradigm article.
The Statistical machine translation page documents the transition of major services to neural machine translation and describes SMT as dominant before NMT, but stops short of declaring the whole method ended.
Exact for WordNet, used contextually for the node's broader hand-built lexical-resource era.
Broad field article; no standalone article isolates Bengio's 2003 neural-language-model paper.
The page explicitly describes Watson's decline, mostly discontinued applications, the Watson Health sale, and IBM's shift to the distinct WatsonX initiative, though Watson did not wholly end.
Broad field article used for the deep-neural transition in speech recognition.
Direct for word2vec, but the node also combines GloVe and the wider static-embedding era.
The Word2vec page describes the model, uses, and limitations but does not explicitly state that word2vec, GloVe, or static embeddings ended or were superseded.
Exact model article covering the in-context-learning milestone.
Direct for the deployed Copilot product but contextual for the wider Codex-to-agentic-code-generation arc.
Chain-of-thought prompting currently redirects to this broader prompting article.
Direct for the core alignment method but broader than the specific InstructGPT recipe and release.
Exact practice article; it does not independently verify the map's fad or dead-end judgment.
The Prompt engineering page explicitly says the dedicated prompt-engineer title lost traction and became obsolete, while prompt engineering itself remains active within broader practice, matching this node's narrowed job-title framing.
No standalone English Wikipedia article existed at audit time; the prompt-engineering article discusses context engineering as the broader production practice.
Direct for GPT-4 but contextual for the node's broader class of multimodal frontier models.
Relevant ecosystem article; open weights and open source are not identical categories.
Exact LLM context-window concept article.
Exact model-class article covering o1-style and R1-style reasoning models.
Direct for the diffusion family but only contextual for emerging diffusion language models.
Broad class article for a composite, fast-changing set of frontier assistants; it does not validate the status judgment.
Direct article for the contextual language representation and its place before BERT.
Biography provides context for Roberts's 1963 machine-perception thesis; no standalone development article exists.
Biography is the closest stable page for Marr's computational-vision framework.
Direct for SIFT but only contextual for the node's combined SIFT-and-HOG hand-crafted-feature era.
The assigned SIFT page describes current applications and improvements and does not explicitly document the combined SIFT-and-HOG feature era as ended or superseded.
Exact framework article for the real-time face-detection milestone.
Exact architecture and ImageNet milestone article.
Direct for detection, but the node also combines segmentation and several later architecture families.
Direct for DeepFace but contextual for FaceNet, human-level accuracy, and the wider recognition milestone.
Direct article for the architecture and its later diffusion-model use.
Exact CLIP model article under the current canonical punctuation.
Relevant task article; the current encyclopedia has no standalone canonical Segment Anything article.
Direct for NeRF but contextual for the node's combined NeRF-to-Gaussian-splatting development.
Relevant world-model article; V-JEPA and JEPA lack a standalone canonical article.
Broad field article for models integrating multiple modalities natively.
Exact canonical article for normalizing-flow generative models.
Direct for style transfer but only contextual for the node's combined DeepDream-and-neural-art moment.
The Neural style transfer page describes the technique and its continuing variants without an explicit ending, failure, or supersession claim for the broader neural-art moment.
Exact model-class article covering systems such as DALL-E and Stable Diffusion.
Relevant parent architecture article; diffusion transformers do not have a standalone canonical page.
Relevant field article for AI music generation; the node also includes voice cloning and specific products.
Exact model-class article covering Sora- and Veo-style video generation.
Exact Google DeepMind Genie world-model article.
Direct article for the foundational statistical-inference development; the node combines Fisherian and Bayesian strands.
Direct umbrella article for statistical pattern recognition.
Exact algorithm article; replaces the search result for a narrower EM/GMM page.
Exact PAC-learning article; COLT is contextual rather than a separate mapped development.
Direct learning-method article covering ID3/CART rather than the generic decision-tree article.
Representative article for the MCMC half of a composite node that also names variational inference.
Direct for SVMs and contextual for the broader kernel-method family combined in the node.
Representative article for boosting; the node also combines random forests, which have their own article.
Direct article for the CRF development named in the node; replaces an unrelated search result.
Direct article covering topic models and LDA.
Direct for Bayesian optimization and contextual for AutoML, which the node combines with it.
The article confirms conformal prediction; deep ensembles and neural-network calibration are supported by the attached primary papers.
Direct article for von Neumann's self-reproducing cellular automaton.
Direct umbrella article covering the ACO/PSO family named by the node.
Exact NEAT article; more specific than the generic neuroevolution search match.
Closest article covering Mead's analog-VLSI neural-hardware program; no dedicated development article was found.
The Hierarchical temporal memory page says HTM is primarily used today for streaming anomaly detection and describes ongoing development, contradicting an unqualified ended label.
Direct field article covering Loihi and related chips.
Closest broad article for analog compute-in-memory AI; the automatic ANALOG Computing match was the unrelated magazine.
Exact system article found by a targeted title search.
Exact robot article; replaces the unrelated Shakey Graves match.
Direct field article covering subsumption-style robotics.
Exact MIT humanoid-robot project article; replaces an unrelated Transformers result.
The Cog project page explicitly says all development had ceased by 2003 and that the robot is retired to the MIT Museum.
Navlab is the direct Wikipedia context for ALVINN; no standalone ALVINN article was returned.
Exact expanded-title article; replaces the unrelated Slam page.
Direct topic context for dynamic legged locomotion; the node is broader than one Boston Dynamics machine.
Direct article for robotics simulation environments; replaces generic Simulation.
Closest field article for deep-RL robot manipulation; replaces the unrelated deep-brain-stimulation match.
Direct development article; the node's 'long grind' status remains an editorial judgment.
Most specific current article for robot foundation models; broader than any one named model.
Topic article supports the technology; 'renaissance' is an editorial periodization.
Biographical context for Wiener's automation/control warnings; no article for the specific 1960 warning was found.
Direct organizational context for the agent-foundations program named in the node.
Exact book article rather than the generic superintelligence concept.
The paper is covered contextually by the AI-safety field article; no standalone paper article was found.
Direct field article; FAccT is institutional context.
Direct field article covering adversarial examples.
Broad context for early saliency-to-circuits interpretability; the node combines several eras and methods.
Closest field article for debate and iterated-distillation/amplification proposals; no standalone method article was found.
Claude's article provides the closest coverage of Anthropic's Constitutional AI/RLAIF method; no standalone method article was found.
Current article context for direct preference optimization; no standalone DPO article was found.
Direct article covering prompt injection and jailbreak attacks.
Organizational article provides context for the now-dissolved Superalignment team; no standalone initiative article was found.
The OpenAI page explicitly says OpenAI ended the Superalignment project in May 2024 after its co-leaders left.
Field context for scalable oversight and weak-to-strong generalization; no direct combined-method article was found.
Field context for evaluations and AI red-teaming; the node combines practices without one direct article.
Direct governance field article covering major regulatory institutions and laws.
Broad context for guaranteed-safe-AI research; no standalone paradigm article was found.
Concrete web-scale dataset context for the broader 'web as dataset' shift; no single article represents the entire shift.
Exact platform article used for crowd labeling.
Direct GPGPU article; CUDA is a major implementation named in the node.
Core technical context for autodiff frameworks; no single framework article represents the ecosystem shift.
Direct model-hub organization/platform article.
Underlying systems article for distributed model training; replaces the unrelated military training-center match.
Closest current article for concentrated frontier-compute infrastructure; replaces the unrelated concentration-camp match.
Direct benchmark context; saturation and contamination are issues within the topic, not a standalone development article.
Closest direct context for licensing/data-access disputes; replaces an unrelated fighting result.
Exact protocol article found by expanded-name search.
Closest current article for inference-serving economics and infrastructure; no direct economics article was found.
Direct infrastructure article for AI energy and datacenter buildout.
Direct system/family article covering AlphaFold 2.
Broad domain context for protein language models and generative protein design; no direct Wikipedia article was found.
The family article covers AlphaFold 3 and interaction prediction.
Field article provides context for GraphCast/GenCast; neither had a standalone article in the targeted search.
Broad applications context for GNoME materials discovery; no standalone GNoME article was found.
Domain context for ML plasma/fusion control; no standalone AI-control development article was found.
Representative direct system article for the AlphaProof/AlphaGeometry IMO result; no AlphaProof article was found.
Direct for one named genomics model and contextual for the broader node, which also includes Evo.
Broad context for AI co-scientists; no direct co-scientist article was found.
The reviewed endpoint articles cross-link; this is contextual evidence, not causal confirmation.
No cross-link was found between the reviewed endpoint pages; link absence does not disprove this map relationship.
The reviewed “Artificial neuron” article links to “Turing machine”; this is contextual, not causal, evidence.
The reviewed “Turing machine” article links to “Stored-program computer”; this is contextual, not causal, evidence.
The reviewed “Markov chain” article links to “Information theory”; this is contextual, not causal, evidence.
The reviewed “NP-completeness” article links to “Turing machine”; this is contextual, not causal, evidence.
Both developments map to one article, so no distinct article cross-link supports this relationship; this is not disproof.
The reviewed “Free energy principle” article links to “Cybernetics”; this is contextual, not causal, evidence.
This proposed combination is a map hypothesis; Wikipedia absence does not establish novelty.
The reviewed “Logic Theorist” article links to “Dartmouth workshop”; this is contextual, not causal, evidence.
The reviewed “Prolog” article links to “Resolution (logic)”; this is contextual, not causal, evidence.
The reviewed “Lighthill report” article links to “SHRDLU”; this is contextual, not causal, evidence.
The reviewed “Dendral” article links to “Mycin”; this is contextual, not causal, evidence.
The reviewed “Frame (artificial intelligence)” article links to “Semantic network”; this is contextual, not causal, evidence.
The reviewed “Expert system” article links to “Xcon”; this is contextual, not causal, evidence.
The reviewed “Expert system” article links to “Lisp machine”; this is contextual, not causal, evidence.
The reviewed “Fifth Generation Computer Systems” article links to “Prolog”; this is contextual, not causal, evidence.
The reviewed “Frame (artificial intelligence)” article links to “Case-based reasoning”; this is contextual, not causal, evidence.
The reviewed “Frame (artificial intelligence)” article links to “Cyc”; this is contextual, not causal, evidence.
The reviewed “Eurisko” article links to “Cyc”; this is contextual, not causal, evidence.
The reviewed “AI winter” article links to “Fifth Generation Computer Systems”; this is contextual, not causal, evidence.
The reviewed “Inductive logic programming” article links to “Prolog”; this is contextual, not causal, evidence.
The reviewed “Frame (artificial intelligence)” article links to “Description logic”; this is contextual, not causal, evidence.
The reviewed “Knowledge graph” article links to “Semantic Web”; this is contextual, not causal, evidence.
The reviewed “Neuro-symbolic AI” article links to “Connectionism”; this is contextual, not causal, evidence.
The reviewed “Computer chess” article links to “Alpha–beta pruning”; this is contextual, not causal, evidence.
The reviewed “Deep Blue (chess computer)” article links to “Alpha–beta pruning”; this is contextual, not causal, evidence.
The reviewed “SAT solver” article links to “Constraint satisfaction problem”; this is contextual, not causal, evidence.
The reviewed “SAT solver” article links to “Resolution (logic)”; this is contextual, not causal, evidence.
The reviewed “Computer poker player” article links to “Game theory”; this is contextual, not causal, evidence.
A complete pair of reviewed endpoint pages was unavailable; this does not disprove the map relationship.
The reviewed “Game theory” article links to “Markov decision process”; this is contextual, not causal, evidence.
The reviewed “Temporal difference learning” article links to “Markov decision process”; this is contextual, not causal, evidence.
The reviewed “Temporal difference learning” article links to “TD-Gammon”; this is contextual, not causal, evidence.
The reviewed “Backpropagation” article links to “TD-Gammon”; this is contextual, not causal, evidence.
The reviewed “Policy gradient method” article links to “Markov decision process”; this is contextual, not causal, evidence.
The reviewed “Temporal difference learning” article links to “Policy gradient method”; this is contextual, not causal, evidence.
The reviewed “Deep reinforcement learning” article links to “Q-learning”; this is contextual, not causal, evidence.
The reviewed “Deep reinforcement learning” article links to “AlexNet”; this is contextual, not causal, evidence.
The policy-gradient article treats TRPO and PPO as policy-gradient methods and traces the family from REINFORCE and actor–critic methods.
The reviewed “Multi-agent reinforcement learning” article links to “Game theory”; this is contextual, not causal, evidence.
The reviewed “Transformer (deep learning)” article links to “Gato (DeepMind)”; this is contextual, not causal, evidence.
The reviewed “DeepSeek” article links to “Proximal policy optimization”; this is contextual, not causal, evidence.
The reviewed “DeepSeek” article links to “Reinforcement learning from human feedback”; this is contextual, not causal, evidence.
The reviewed “ADALINE” article links to “Perceptron”; this is contextual, not causal, evidence.
The RBM article explicitly identifies restricted Boltzmann machines as a special case of Boltzmann machines.
The reviewed “Connectionism” article links to “Boltzmann machine”; this is contextual, not causal, evidence.
The reviewed “Universal approximation theorem” article links to “Backpropagation”; this is contextual, not causal, evidence.
The RBM article explicitly says deep belief networks can be formed by stacking RBMs.
The reviewed “Vanishing gradient problem” article links to “Deep belief network”; this is contextual, not causal, evidence.
Wikipedia explicitly identifies LayerNorm as a key transformer component and describes its use in the original transformer.
The reviewed “Graph neural network” article links to “LeNet”; this is contextual, not causal, evidence.
The reviewed “Knowledge graph” article links to “Graph neural network”; this is contextual, not causal, evidence.
The reviewed “Neural differential equation” article links to “Residual neural network”; this is contextual, not causal, evidence.
The reviewed “Transformer (deep learning)” article links to “Mamba (deep learning architecture)”; this is contextual, not causal, evidence.
The reviewed “Rectified linear unit” article links to “Batch normalization”; this is contextual, not causal, evidence.
Wikipedia describes LayerNorm as a popular alternative to BatchNorm; that supports the historical comparison, not a strict technical dependency.
The reviewed “Transfer learning” article links to “AlexNet”; this is contextual, not causal, evidence.
The reviewed “BERT (language model)” article links to “Transfer learning”; this is contextual, not causal, evidence.
The reviewed “Word2vec” article links to “Self-supervised learning”; this is contextual, not causal, evidence.
The reviewed “Bitter lesson” article links to “Deep Blue (chess computer)”; this is contextual, not causal, evidence.
The reviewed “Transformer (deep learning)” article links to “Neural scaling law”; this is contextual, not causal, evidence.
The reviewed “LoRA (machine learning)” article links to “GPT-3”; this is contextual, not causal, evidence.
The reviewed “Neural scaling law” article links to “Monte Carlo tree search”; this is contextual, not causal, evidence.
The reviewed “Federated learning” article links to “Differential privacy”; this is contextual, not causal, evidence.
The reviewed “Generative grammar” article links to “Cognitive revolution”; this is contextual, not causal, evidence.
The reviewed “Word n-gram language model” article links to “Hidden Markov model”; this is contextual, not causal, evidence.
The reviewed “Language model” article links to “Word n-gram language model”; this is contextual, not causal, evidence.
The reviewed “Google Neural Machine Translation” article links to “Attention (machine learning)”; this is contextual, not causal, evidence.
The reviewed “Google Neural Machine Translation” article links to “Statistical machine translation”; this is contextual, not causal, evidence.
The ELMo article explicitly describes the transition from contextual bidirectional-LSTM representations to BERT's transformer encoder and similar pretrain–finetune workflow.
The reviewed “Neural scaling law” article links to “GPT-3”; this is contextual, not causal, evidence.
The reviewed “GPT-3” article links to “Reinforcement learning from human feedback”; this is contextual, not causal, evidence.
The reviewed “Open-source artificial intelligence” article links to “Hugging Face”; this is contextual, not causal, evidence.
The reviewed “BERT (language model)” article links to “Retrieval-augmented generation”; this is contextual, not causal, evidence.
Wikipedia confirms that RAG combines generation with information retrieval; the more specific vector-space lineage is a historical synthesis rather than an explicit dependency statement.
The reviewed “Transformer (deep learning)” article links to “Context window”; this is contextual, not causal, evidence.
The reviewed “Reasoning model” article links to “Neural scaling law”; this is contextual, not causal, evidence.
The reviewed “GPT-4” article links to “Diffusion model”; this is contextual, not causal, evidence.
The reviewed “ELMo” article links to “Long short-term memory”; this is contextual, not causal, evidence.
The reviewed “ImageNet” article links to “Amazon Mechanical Turk”; this is contextual, not causal, evidence.
The reviewed “AlexNet” article links to “General-purpose computing on graphics processing units”; this is contextual, not causal, evidence.
The reviewed “Rectified linear unit” article links to “AlexNet”; this is contextual, not causal, evidence.
The reviewed “AlexNet” article links to “Scale-invariant feature transform”; this is contextual, not causal, evidence.
The reviewed “U-Net” article links to “LeNet”; this is contextual, not causal, evidence.
The reviewed “U-Net” article links to “AlexNet”; this is contextual, not causal, evidence.
The reviewed “Vision transformer” article links to “ImageNet”; this is contextual, not causal, evidence.
The reviewed “Contrastive Language–Image Pre-training” article links to “Vision transformer”; this is contextual, not causal, evidence.
The reviewed “Contrastive Language–Image Pre-training” article links to “Self-supervised learning”; this is contextual, not causal, evidence.
The reviewed “Vision transformer” article links to “Image segmentation”; this is contextual, not causal, evidence.
The reviewed “Contrastive Language–Image Pre-training” article links to “Multimodal learning”; this is contextual, not causal, evidence.
The reviewed “GPT-4” article links to “Multimodal learning”; this is contextual, not causal, evidence.
The reviewed “Generative adversarial network” article links to “Game theory”; this is contextual, not causal, evidence.
The reviewed “Flow-based generative model” article links to “Variational autoencoder”; this is contextual, not causal, evidence.
The reviewed “Neural style transfer” article links to “AlexNet”; this is contextual, not causal, evidence.
The reviewed “WaveNet” article links to “LeNet”; this is contextual, not causal, evidence.
The reviewed “Diffusion model” article links to “Boltzmann machine”; this is contextual, not causal, evidence.
The U-Net article explicitly documents its use in diffusion models for iterative image denoising.
The reviewed “Text-to-video model” article links to “Diffusion model”; this is contextual, not causal, evidence.
The reviewed “Kalman filter” article links to “Markov chain”; this is contextual, not causal, evidence.
The reviewed “Pattern recognition” article links to “Statistical inference”; this is contextual, not causal, evidence.
The reviewed “Pattern recognition” article links to “Probably approximately correct learning”; this is contextual, not causal, evidence.
The reviewed “Pattern recognition” article links to “Decision tree learning”; this is contextual, not causal, evidence.
The reviewed “Pattern recognition” article links to “Support vector machine”; this is contextual, not causal, evidence.
The reviewed “Gaussian process” article links to “Bayesian network”; this is contextual, not causal, evidence.
The reviewed “Conditional random field” article links to “Bayesian network”; this is contextual, not causal, evidence.
The reviewed “Bayesian network” article links to “Causal inference”; this is contextual, not causal, evidence.
The reviewed “Bayesian optimization” article links to “Gaussian process”; this is contextual, not causal, evidence.
The reviewed “Von Neumann universal constructor” article links to “Artificial life”; this is contextual, not causal, evidence.
The reviewed “Neuroevolution of augmenting topologies” article links to “Genetic algorithm”; this is contextual, not causal, evidence.
The reviewed “Spiking neural network” article links to “Hebbian theory”; this is contextual, not causal, evidence.
The reviewed “Spiking neural network” article links to “Hodgkin–Huxley model”; this is contextual, not causal, evidence.
The reviewed “Hierarchical temporal memory” article links to “Visual cortex”; this is contextual, not causal, evidence.
The reviewed “Neuromorphic computing” article links to “Spiking neural network”; this is contextual, not causal, evidence.
The reviewed “Optical neural network” article links to “Neuromorphic computing”; this is contextual, not causal, evidence.
The reviewed “Neuromorphic computing” article links to “Hardware for artificial intelligence”; this is contextual, not causal, evidence.
The reviewed “AlphaEvolve” article links to “Genetic programming”; this is contextual, not causal, evidence.
The reviewed “Simultaneous localization and mapping” article links to “DARPA Grand Challenge”; this is contextual, not causal, evidence.
The reviewed “Robot learning” article links to “Robotics simulator”; this is contextual, not causal, evidence.
The reviewed “Vision–language–action model” article links to “Robot learning”; this is contextual, not causal, evidence.
The reviewed “Vision–language–action model” article links to “Humanoid robot”; this is contextual, not causal, evidence.
The reviewed “Solomonoff's theory of inductive inference” article links to “AIXI”; this is contextual, not causal, evidence.
The reviewed “AIXI” article links to “Reinforcement learning”; this is contextual, not causal, evidence.
The reviewed “Transformer (deep learning)” article links to “Mechanistic interpretability”; this is contextual, not causal, evidence.
The reviewed “Claude (AI)” article links to “Reinforcement learning from human feedback”; this is contextual, not causal, evidence.
The reviewed “General-purpose computing on graphics processing units” article links to “Moore's law”; this is contextual, not causal, evidence.
The reviewed “General-purpose computing on graphics processing units” article links to “Tensor Processing Unit”; this is contextual, not causal, evidence.
The reviewed “BERT (language model)” article links to “Hugging Face”; this is contextual, not causal, evidence.
The reviewed “AI infrastructure” article links to “Distributed computing”; this is contextual, not causal, evidence.
The reviewed “AlphaChip” article links to “Tensor Processing Unit”; this is contextual, not causal, evidence.
The reviewed “AlphaChip” article links to “Deep reinforcement learning”; this is contextual, not causal, evidence.
The reviewed “Bioinformatics” article links to “AlphaFold”; this is contextual, not causal, evidence.
The reviewed “AlphaFold” article links to “Diffusion model”; this is contextual, not causal, evidence.
The reviewed “Multimodal learning” article links to “Artificial intelligence in healthcare”; this is contextual, not causal, evidence.
Wikipedia directly documents U-Net's biomedical segmentation origin and medical-imaging uses; the map's broader medical-AI deployment node goes beyond that evidence.
The reviewed “Applications of artificial intelligence” article links to “AI agent”; this is contextual, not causal, evidence.
The reviewed “Applications of artificial intelligence” article links to “AlphaEvolve”; this is contextual, not causal, evidence.
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning
Efficient Estimation of Word Representations in Vector Space
Playing Atari with Deep Reinforcement Learning
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
Neural Machine Translation by Jointly Learning to Align and Translate
Sequence to Sequence Learning with Neural Networks
Adam: A Method for Stochastic Optimization
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Distilling the Knowledge in a Neural Network
Illuminating search spaces by mapping elites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Neural Machine Translation of Rare Words with Subword Units
Continuous control with deep reinforcement learning
Deep Residual Learning for Image Recognition
Communication-Efficient Learning of Deep Networks from Decentralized Data
Generative Adversarial Imitation Learning
A Baseline for Detecting Misclassified and Out-of-Distribution Examples
Membership Inference Attacks Against Machine Learning Models
Neural Architecture Search with Reinforcement Learning
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
Deep Reinforcement Learning from Human Preferences
On Calibration of Modern Neural Networks
Towards Deep Learning Models Resistant to Adversarial Attacks
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
Population Based Training of Neural Networks
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
TVM: An Automated End-to-End Optimizing Compiler for Deep Learning
Deep Contextualized Word Representations
SentencePiece: A simple and language independent subword tokenizer
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Certified Adversarial Robustness via Randomized Smoothing
The Curious Case of Neural Text Degeneration
Risks from Learned Optimization in Advanced Machine Learning Systems
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
The frontier of simulation-based inference
A Simple Framework for Contrastive Learning of Visual Representations
MLIR: A Compiler Infrastructure for the End of Moore's Law
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
Dense Passage Retrieval for Open-Domain Question Answering
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
End-to-End Object Detection with Transformers
Deep active inference agents using Monte-Carlo methods
Conservative Q-Learning for Offline Reinforcement Learning
Denoising Diffusion Probabilistic Models
Fourier Neural Operator for Parametric Partial Differential Equations
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Score-Based Generative Modeling through Stochastic Differential Equations
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Extracting Training Data from Large Language Models
Taming Transformers for High-Resolution Image Synthesis
Causal World Models by Unsupervised Deconfounding of Physical Dynamics
Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams
Learning Transferable Visual Models From Natural Language Supervision
Goal Misgeneralization in Deep Reinforcement Learning
LoRA: Low-Rank Adaptation of Large Language Models
SoundStream: An End-to-End Neural Audio Codec
A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
Finetuned Language Models Are Zero-Shot Learners
Multitask Prompted Training Enables Zero-Shot Task Generalization
Masked Autoencoders Are Scalable Vision Learners
Quantum advantage in learning from experiments
Active Inference in Robotics and Artificial Agents: Survey and Challenges
High-Resolution Image Synthesis with Latent Diffusion Models
Locating and Editing Factual Associations in GPT
Training language models to follow instructions with human feedback
Training Compute-Optimal Large Language Models
Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
Why do tree-based models still outperform deep learning on tabular data?
Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
ReAct: Synergizing Reasoning and Acting in Language Models
Scaling Laws for Reward Model Overoptimization
Scaling Instruction-Finetuned Language Models
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
Fast Inference from Transformers via Speculative Decoding
Robust Speech Recognition via Large-Scale Weak Supervision
RT-1: Robotics Transformer for Real-World Control at Scale
The Forward-Forward Algorithm: Some Preliminary Investigations
SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot
Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models
Accelerating Large Language Model Decoding with Speculative Sampling
Diffusion Policy: Visuomotor Policy Learning via Action Diffusion
LLM+P: Empowering Large Language Models with Optimal Planning Proficiency
Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
QLoRA: Efficient Finetuning of Quantized LLMs
Voyager: An Open-Ended Embodied Agent with Large Language Models
The Curse of Recursion: Training on Generated Data Makes Models Forget
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
WebArena: A Realistic Web Environment for Building Autonomous Agents
RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
3D Gaussian Splatting for Real-Time Radiance Field Rendering
Steering Language Models With Activation Engineering
Efficient Memory Management for Large Language Model Serving with PagedAttention
TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models
MatterGen: a generative model for inorganic materials design
AI Control: Improving Safety Despite Intentional Subversion
Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training
Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
Better than classical? The subtle art of benchmarking quantum machine learning models
Evaluating Frontier Models for Dangerous Capabilities
Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data
AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents
Alignment Faking in Large Language Models
AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders
The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search
DeepSeek-Prover-V2: Advancing Formal Mathematical Reasoning via Reinforcement Learning for Subgoal Decomposition
Absolute Zero: Reinforced Self-play Reasoning with Zero Data
Darwin Gödel Machine: Open-Ended Evolution of Self-Improving Agents
Energy-Based Transformers are Scalable Learners and Thinkers
DeepFleet: Multi-Agent Foundation Models for Mobile Robots
CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks
Adaptive Attacks on Trusted Monitors Subvert AI Control Protocols
CausalVerse: Benchmarking Causal Representation Learning with Configurable High-Fidelity Simulations
Robot Learning from a Physical World Model
Can Local Learning Match Self-Supervised Backpropagation?
VeriSoftBench: Repository-Scale Formal Verification Benchmarks for Lean
Autonomous Laboratory Agent via Customized Domain-Specific Language Model and Modular AI Interface
Attack Selection in Agentic AI Control Evaluations Meaningfully Decreases Safety
Not Just After One: Sleep-Inspired Replay Prevents Catastrophic Forgetting After Sequential Tasks
Improved Large Language Diffusion Models
Beyond IID: How General Are Tabular Foundation Models, Really?
Efficient Sim-to-Real Transfer of World-Action Models from Synthetic Priors
Language and Machines: Computers in Translation and Linguistics
Automatic Language Processing Advisory Committee
Literary context for the Three Laws, not technical evidence.
An Essay Towards Solving a Problem in the Doctrine of Chances
Historical Bayesian background, not a 1925 development.
Pattern Recognition and Machine Learning
Microsoft Research hosts the author PDF.
Superintelligence: Paths, Dangers, Strategies
A Robust Layered Control System for a Mobile Robot
Three Models for the Description of Language
The Complexity of Theorem-Proving Procedures
A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence
Maximum Likelihood from Incomplete Data via the EM Algorithm
ImageNet: A Large-Scale Hierarchical Image Database
STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving
Statistical Methods for Research Workers
Full-view scan; reuse rights can vary by jurisdiction.
Original German scan; the file page records the work as public domain.
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme I
A Formal Basis for the Heuristic Determination of Minimum Cost Paths
The Elements of Statistical Learning, Second Edition
Free institutional copy; copyright is retained.
Full-view historical scan; reuse rights can vary by jurisdiction.
A Fast Learning Algorithm for Deep Belief Nets
A Quantitative Description of Membrane Current and Its Application to Conduction and Excitation in Nerve
Neural Networks and Physical Systems with Emergent Collective Computational Abilities
Contemporary archival account of the demonstration.
Highly Accurate Protein Structure Prediction with AlphaFold
A New Approach to Linear Filtering and Prediction Problems
Introduced UCT, a central MCTS method; it did not solely invent the full MCTS family.
Backpropagation Applied to Handwritten Zip Code Recognition
Distinctive Image Features from Scale-Invariant Keypoints
Information Theory, Inference, and Learning Algorithms
Investigation of a Remarkable Case of Dependent Trials
Original Russian full text; this is the mathematical chain-dependence work, not the later text example.
An Example of Statistical Investigation of the Text Eugene Onegin Concerning the Connection of Samples in Chains
The linked English translation was published in 2006; it is not labeled public domain.
Recursive Functions of Symbolic Expressions and Their Computation by Machine, Part I
A Logical Calculus of the Ideas Immanent in Nervous Activity
Equation of State Calculations by Fast Computing Machines
The Magical Number Seven, Plus or Minus Two
The Logic Theory Machine: A Complex Information Processing System
RAND product page with an authorized downloadable PDF.
Parallel Distributed Processing, Volume 1
Causality: Models, Reasoning, and Inference, Second Edition
Probabilistic Reasoning in Intelligent Systems
GloVe: Global Vectors for Word Representation
Language Models are Unsupervised Multitask Learners
Official OpenAI report page and paper link.
Some Aspects of the Sequential Design of Experiments
The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain
Learning Representations by Back-Propagating Errors
Field-shaping demonstration and popularization, not a claim of sole invention.
Artificial Intelligence: A Modern Approach, Fourth Edition
Programming a Computer for Playing Chess
Computer History Museum archival copy; copyright is retained.
Authoritative IEEE reading page and scan.
A Symbolic Analysis of Relay and Switching Circuits
MIT institutional repository record and full text.
Mastering the Game of Go with Deep Neural Networks and Tree Search
Reinforcement Learning: An Introduction, Second Edition
The node marks the 1998 synthesis; this link is the authorized, updated 2018 edition.
Learning to Predict by the Methods of Temporal Differences
Author publication index includes the paper and erratum.
On Computable Numbers, with an Application to the Entscheidungsproblem
Authorized reproduction hosted by Oxford; includes the published correction.
Rapid Object Detection Using a Boosted Cascade of Simple Features
The General and Logical Theory of Automata
Academic reading copy; rights are retained.
Smithsonian public-domain/CC0 scan; the broader EDVAC team also shaped the architecture.
Readable archival scan; the item does not establish public-domain status.
ELIZA—A Computer Program for the Study of Natural Language Communication Between Man and Machine
Cybernetics: Or Control and Communication in the Animal and the Machine
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
General-purpose computing on graphics processing units
Intrinsic motivation (artificial intelligence)
Reinforcement learning from human feedback
Solomonoff's theory of inductive inference
Stanford Research Institute Problem Solver