ML Leaders Brain-Inspired AI Research Heartbeat

Date: 2026-08-28 02:25 UTC Scope: 14 leading researchers in brain-inspired ML/AGI Sources: arXiv, Google Scholar, lab websites, personal pages, conference proceedings, and authoritative web sources

Heartbeat Update Notes

Update date: 2026-08-28 02:25 UTC Delta sources: OpenAlex, arXiv, lab pages; GitHub queries were skipped per Oction hard rule (NO GitHub). Summary of new developments since 2026-08-25:

No new indexed papers or public statements were found for Jeff Hawkins, Yoshua Bengio, Juergen Schmidhuber, Gary Marcus, Marcus Hutter, or Penelope Lewis in this cycle.

Demis Hassabis (Google DeepMind)

1. His neuroscience background and how it shapes his AGI approach

Demis Hassabis studied computer science at Cambridge and completed a PhD in cognitive neuroscience at University College London (UCL) under renowned neuroscientist Eleanor Maguire. His doctoral thesis, The Neural Processes Underpinning Episodic Memory (2009), used fMRI to study how the hippocampus supports memory and imagination. He established for the first time that patients with hippocampal damage causing amnesia were also unable to imagine themselves in new experiences, demonstrating a neurological connection between episodic memory and constructive imagination [Klover.ai profile; Academy of Achievement]. This interdisciplinary foundation directly shaped DeepMind’s founding mission: “to combine insights from systems neuroscience with new developments in machine learning and computing hardware to unlock increasingly powerful general-purpose learning algorithms that will work towards the creation of an artificial general intelligence (AGI)” [Klover.ai]. His 2025 statement to CBS was that “understanding the world around us” has always guided him, mapping semantic memory to structured knowledge representations, episodic memory to experience storage, and imagination to the simulation engine itself [Bret Kerr, Substack].

2. How DeepMind uses hippocampal memory replay in AI systems (experience replay)

DeepMind’s 2015 DQN Nature paper explicitly cites biological hippocampal mechanisms as the inspiration for experience replay [Bret Kerr, Substack]. In biological brains, the hippocampus replays memory sequences during sleep and quiet wakefulness to consolidate learning in the neocortex. DQN mimics this by storing a subset of training data in a replay buffer and reviewing it offline, allowing the agent to learn anew from past successes or failures [Google DeepMind blog, “AI and Neuroscience: A virtuous circle”]. This biological parallel is not merely metaphorical: Hassabis co-authored work on hippocampal place cells constructing reward-related sequences through unexplored space, and on big-loop recurrence within the hippocampal system supporting integration of information across episodes [Neuron, 2018; references from Pontifical Academy of Sciences profile].

3. His theory of "imagination-based planning" and what brain region it models

Imagination-based planning at DeepMind is instantiated in the Imagination-Augmented Agents (I2As) architecture, introduced in 2017, which combines model-free and model-based reinforcement learning by allowing agents to simulate possible future trajectories internally [arXiv:1707.06203]. Hassabis has linked this explicitly to the dorsal hippocampus and prefrontal cortex. He shared a Nature Neuroscience paper showing that dorsal hippocampus contributes to model-based planning [Hassabis tweet, 2017]. In a major 2018 Nature Neuroscience paper co-authored by Hassabis, the prefrontal cortex is characterized as a meta-reinforcement learning system: the dopamine system trains the prefrontal cortex to operate as its own free-standing learning system [Nature Neuroscience, 2018, doi:10.1038/s41593-018-0147-8]. This dual-system view—hippocampal episodic simulation plus prefrontal meta-learning—maps directly onto Hassabis’s neuroscience-driven AGI architecture.

4. Latest DeepMind papers he has authored or co-authored on general intelligence

Recent works from baseline data and authoritative sources include: - Gemma 4 Technical Report (2026) — arXiv:2607.02770 [OpenAlex baseline] - Accelerating scientific discovery with Co-Scientist (2026) — Nature, doi:10.1038/s41586-026-10644-y [OpenAlex baseline] - Towards Autonomous Mathematics Research (2026) — arXiv:2602.10177 [OpenAlex baseline] - Advancing regulatory variant effect prediction with AlphaGenome (2026) — Nature, doi:10.1038/s41586-025-10014-0 [OpenAlex baseline] - SIMA 2: A Generalist Embodied Agent for Virtual Worlds (2025) — arXiv:2512.04797 [OpenAlex baseline] - Olympiad-level formal mathematical reasoning with reinforcement learning (2025) — Nature, doi:10.1038/s41586-025-09833-y [OpenAlex baseline] - Prefrontal cortex as a meta-reinforcement learning system (2018) — Nature Neuroscience — foundational for his general-intelligence framework [DeepMind blog]

5. His public statements on what milestone will signal true AGI

Hassabis has been explicit that even DeepMind’s Nobel-winning achievements do not clear the AGI bar. In 2025 he proposed a specific test: “Training an AI system with a knowledge cutoff of, say, 1911, and then seeing if it could come up with general relativity, like Einstein did in 1915” [OfficeChai; Reddit]. He received the 2024 Nobel Prize in Chemistry for AlphaFold alongside John Jumper and David Baker, and was knighted in 2024 [Wikipedia; CryptoBriefing]. His public criterion is therefore discovery of novel scientific theories without being exposed to them, not merely mastery of existing knowledge or games.

6. How AlphaGo and AlphaFold connect to his broader AGI vision

Games were chosen as the “perfect proving ground for AI” because Go is the most complex game ever devised (10^170 possible positions, exceeding the number of atoms in the universe). AlphaGo’s 2016 victory over Lee Sedol demonstrated that AI could master intuition-like judgment in vast search spaces [Nobel Lecture PDF, Dec 2024]. AlphaFold then cracked the 50-year protein-folding grand challenge, providing “an exciting first proof point of that thesis” that AI can accelerate scientific discovery [Hassabis X post, 2020; Nobel Lecture]. The through-line is that general learning algorithms proven in constrained domains (games) are then applied to open-ended scientific problems (biology), eventually aiming at autonomous discovery of new theories.

Key Papers (2022–2025)

Note: Several landmark works were published in 2026; the 2022–2025 window is represented here by SIMA 2 and the Olympiad-level formal-mathematics paper.

Jeff Hawkins (Numenta / Thousand Brains Project)

1. Full explanation of Hierarchical Temporal Memory (HTM) and how it models the neocortex

Hierarchical Temporal Memory (HTM) is a biologically constrained machine-intelligence technology developed by Numenta and originally described in Hawkins’s 2004 book On Intelligence (with Sandra Blakeslee). HTM models the neocortex’s uniform algorithmic structure, particularly the physiology and interaction of pyramidal neurons. It is built on three core principles: hierarchical organization, sparse distributed representations (SDRs), and sequence memory / inference. Every HTM region stores sequences of patterns; by matching stored sequences with current input, a region forms a prediction of what will happen next. HTM learns these sequences in an unsupervised fashion without backpropagation or labeled training data [Wikipedia; Numenta HTM Whitepaper, 2011]. The technology is primarily used today for anomaly detection in streaming data.

2. What "reference frames" mean in his theory and why he says they are the key to AGI

In Hawkins’s Thousand Brains Theory, the central insight is that each cortical column creates and maintains reference frames for objects and concepts. A reference frame is a coordinate system anchored to an entity (physical object or abstract idea) that the brain uses to model how that entity changes as the organism moves or acts. “Each cortical column uses reference frames to learn models and store knowledge. Therefore, the entire neocortex is a distributed sensory-motor modeling system” [TheSequence interview, 2022]. Reference frames explain how humans rapidly learn almost anything and solve problems in novel ways: by storing knowledge relative to the thing itself rather than relative to the observer. In the 2025 Thousand Brains Theory 2.0 preprint, long-range cortical connections are understood to transform sensor and motor information between egocentric and allocentric reference frames, and to learn compositional objects [arXiv:2507.05888].

3. His latest Numenta research papers and white papers (2022–2025)

• Thousand-Brains Systems: Sensorimotor Intelligence for Rapid, Robust Learning and Inference (2026) — Neural Computation [OpenAlex baseline]
• The Thousand Brains Theory 2.0: An Extension for the Long-Range Connections of the Neocortical Heterarchy (2025) — arXiv:2507.05888 [OpenAlex baseline; arXiv]
• The Thousand Brains Project: A New Paradigm for Sensorimotor Intelligence (2024) — arXiv:2412.18354 [OpenAlex baseline]
• Catalyzing next-generation Artificial Intelligence through NeuroAI (2023) — Nature Communications, doi:10.1038/s41467-023-37180-x [OpenAlex baseline]
• Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution (2022) — arXiv:2210.08340 [OpenAlex baseline]

In January 2025, the Thousand Brains Project was established as an independent nonprofit organization to advance this work [Grokipedia].

4. How his theory differs from standard deep learning and transformer models

Hawkins’s approach differs from standard deep learning in several fundamental ways. HTM does not use backpropagation or gradient descent; instead it relies on Hebbian-like local learning rules and sparse distributed representations. It is designed for continuous, unsupervised learning from streaming temporal data rather than batched training on static datasets. Unlike transformers, which process tokens through massive matrix multiplications and attention mechanisms, HTM models the physical structure of cortical columns, each of which is a complete sensory-motor learning system [TheSequence interview; Numenta Q&A, 2023]. Hawkins argues that deep learning systems lack reference frames and therefore lack the ability to learn compositional, transferable models of the world. In a 2023 Numenta Q&A he stated that cortical-algorithm-based systems are intended to be more flexible and capable in the many applications where deep learning methods fail [Numenta blog].

5. What he says AGI will require that current AI is missing

Hawkins argues that current AI misses the neocortex’s core mechanisms: reference-frame-based modeling, continuous learning through movement, and distributed consensus among columns. In A Thousand Brains he writes that there is no central control room in the brain; instead perception is a consensus reached by voting among columns. He believes that any system lacking cortical columns that learn reference frames for every known object will be unable to generalize robustly or learn from small amounts of experience [Forbes review, 2021; Goodreads summary]. The Thousand Brains Project aims to instantiate these principles in software, with the explicit goal of building AI that is “more intelligent, more flexible, and more capable in the many applications that deep learning methods fail” [arXiv:2412.18354].

6. His book "A Thousand Brains" — core arguments and how they apply to AI architecture

Published in 2021, A Thousand Brains: A New Theory of Intelligence presents two main threads: a neuroscience theory and its implications for AI and humanity. The neuroscience argument is that the neocortex consists of approximately 150,000 cortical columns, each a complete sensory-motor learning system. Columns create reference frames for every object they know; collectively they learn a model of the world and continuously update it. There is no central controller—perception emerges from distributed voting among columns. For AI architecture, this implies that intelligence requires a heavily distributed, sensorimotor, reference-frame-based system rather than monolithic networks trained by backpropagation. He extends this to argue for “Thousand-Brains Systems” that can be scaled by adding or deleting cortical columns and that are inherently robust to noise and sensor loss [Forbes review, 2021; Alignment Forum review].

Key Papers (2022–2025)

Yoshua Bengio (Mila / Université de Montréal)

1. His "System 2 Deep Learning" concept — what it is and how it bridges intuition and reasoning

At NeurIPS 2019 Bengio introduced the framework of moving from “System 1 Deep Learning” to “System 2 Deep Learning,” borrowing Daniel Kahneman’s terminology. System 1 corresponds to fast, intuitive, unconscious pattern recognition—the kind of processing current deep nets excel at. System 2 corresponds to slow, conscious, explicit reasoning: planning, logic, programming, and causal inference. Bengio argues that soft attention mechanisms are a key ingredient to focus computation on a few concepts at a time (a “conscious thought”), in line with the “consciousness prior” and the assumption that many high-level dependencies can be captured by a sparse factor graph. The agent perspective in deep learning helps constrain representations to capture affordances, causal variables, and model transitions in the environment [NeurIPS 2019 talk; TechTalks, 2019; Tenstorrent summary].

2. GFlowNets — what they are, how they work, and why he believes they are important for AGI

Generative Flow Networks (GFlowNets), co-developed by Bengio and colleagues, are generative policies trained to sample objects proportionally to a given reward function. Unlike MCMC methods, which may get stuck in local modes, a GFlowNet learns a stochastic policy that samples a diversity of high-reward candidates. When the reward is set to a prior times a likelihood, the GFlowNet learns to sample from the corresponding Bayesian posterior. Bengio has described GFlowNets as a tool for amortized probabilistic inference and active learning, and as a pathway to bridge the gap between state-of-the-art AI and human intelligence by introducing System 2 inductive biases into neural nets [arXiv:2111.09266; Bengio blog; Microsoft Research video, 2023].

3. His arguments for why current LLMs are insufficient for general intelligence

Bengio has repeatedly stated that current AI systems, including large language models, do not understand the world at a human level. In interviews and talks he characterizes genuine understanding as the ability to: capture causality; model how the world works; understand abstract actions and how to use them to control the environment; reason and plan in novel scenarios; explain what happened (inference and credit assignment); and generalize out-of-distribution [SyncedReview, 2020; QuantumZeitgeist]. At the NeurIPS 2019 talk he said System 2 capabilities include programming, algorithm design, and logical planning—areas where LLMs still struggle. His pivot toward safety and causality research since 2023 is explicitly framed as a response to the gap between scaling LLMs and achieving safe, general intelligence [Bengio research page, 2023].

4. His neuroscience-inspired ideas about consciousness and AI

Bengio’s consciousness prior hypothesizes that high-level conscious thoughts involve a small number of concepts attended to at once, and that the dependencies among these concepts can be approximated by a sparse factor graph. This inspired the use of soft attention as a mechanism for System 2 processing. In 2025 he co-authored the landmark paper Identifying indicators of consciousness in AI systems (published in Trends in Cognitive Sciences), which brings together leading neuroscientists and philosophers to assess whether advanced AI systems might possess markers of consciousness [doi:10.1016/j.tics.2025.10.011]. His blog also links GFlowNets to causality and consciousness as a window on “upcoming developments aimed at bridging the gap between SOTA AI and human intelligence” [Bengio blog].

5. His most cited recent papers (2022–2025) on reasoning, causality, or cognitive architectures

• GFlowNet Foundations (2023) — Journal of Machine Learning Research, 24:1–55. Authors: Bengio, Lahlou, Deleu, Hu, Tiwari, E. Bengio. arXiv:2111.09266 [JMLR; arXiv]
• Identifying indicators of consciousness in AI systems (2025) — Trends in Cognitive Sciences. Authors include Butlin, Long, Bayne, Bengio, Birch, Chalmers, et al. doi:10.1016/j.tics.2025.10.011 [Tel Aviv University; ScienceDirect]
• International AI Safety Report 2025: Second Key Update (2025) — Bengio served as chair/lead author. doi:10.70777/si.v2i4.16671 and arXiv:2511.19863 [OpenAlex baseline]
• Sliding Window Recurrences for Sequence Models (2025) — arXiv:2512.13921 [OpenAlex baseline]
• Towards Causal Representation Learning (2021, though highly influential into 2022–2025) — arXiv:2102.11107, authors Schölkopf, Locatello, Bauer, Ke, Kalchbrenner, Goyal, Bengio [arXiv]

6. His stance on AI safety and how that connects to his AGI research direction

In early 2023 Bengio announced a pivot from capability-focused ML research to AI safety, motivated by the prospect of approaching or surpassing human-level AI [Bengio research page]. He chairs the International Scientific Report on the Safety of Advanced AI, which published its 2025 Second Key Update through arXiv [arXiv:2511.19863]. He launched the nonprofit LawZero with a reported $30 million commitment to advance AI safety governance [digidai analysis, 2025]. His view is that achieving System 2 reasoning and causal understanding is not only necessary for AGI but also critical for alignment: without mechanisms for explicit reasoning, credit assignment, and causal modeling, advanced AI systems may act in ways we cannot predict or control. Safety and capability are therefore intertwined in his research agenda.

Key Papers (2022–2025)

Note: “Towards Causal Representation Learning” (arXiv:2102.11107, 2021) is the foundational causality paper that underpins much of his 2022–2025 work but technically sits just outside the window.

Juergen Schmidhuber (KAUST / IDSIA)

1. The full history and architecture of LSTM — why it was revolutionary and how it relates to brain memory

Long Short-Term Memory (LSTM) was introduced by Sepp Hochreiter in his 1991 diploma thesis supervised by Schmidhuber at TU Munich. Hochreiter analyzed the vanishing gradient problem in recurrent neural networks (RNNs) and designed LSTM to overcome it through a gating mechanism that preserves error flow across long time steps. Schmidhuber has called this thesis “one of the most important documents in the history of machine learning” [Wikipedia]. LSTM introduced gated residual connections for RNNs, which were later extended to feedforward networks by Schmidhuber’s PhD students Rupesh Kumar Srivastava and Klaus Greff. By 2025, LSTM principles underpin systems used in billions of devices, from smartphones to speech-recognition engines. Schmidhuber frames LSTM as a biologically inspired solution to the problem of maintaining information over time—a function analogous to working memory and selective attention in brains [Schmidhuber’s IDSIA page; arXiv:2509.24732].

2. His "formal theory of creativity and curiosity" — what it is and how it drives self-improving AI

Since 1990, Schmidhuber has developed a general formal theory of creativity, curiosity, and intrinsic motivation. The core principle is compression progress: an agent receives intrinsic reward for discovering novel, surprising patterns that allow it to compress its observation history more efficiently. In other words, the agent is “driven by the progress of its data compression algorithm” [IEEE paper, “Formal Theory of Creativity, Fun, and Intrinsic Motivation”]. This is meant to explain not only scientific discovery but also art, music, humor, and beauty. The theory underpins artificial curiosity algorithms in which a predictor network tries to forecast environmental feedback, and a controller network seeks inputs that maximize prediction error (surprise) and subsequent compression gain. Schmidhuber argues that this loop naturally drives open-ended, self-improving exploration, making it a foundation for autonomous AGI development [IDSIA creativity page; IEEE paper].

3. His concept of self-referential neural networks — what makes them different

Schmidhuber introduced the Gödel Machine in 2006, described as a “fully self-referential optimal universal self-improver” [arXiv:cs/0309048]. Unlike standard learning systems that improve only their policy or weights within a fixed architecture, a Gödel Machine can rewrite any part of its own code—including its learning algorithm and utility function—provided the rewrite is provably optimal according to its formal axiomatic system. It consists of a self-referential axiomatic system, a universal problem solver, and a proof searcher that evaluates possible self-modifications. The Darwin Gödel Machine extends this by adding evolutionary search over proof strategies. This is qualitatively different from conventional deep learning because it treats self-modification as a formal optimization problem with optimality guarantees, rather than as heuristic gradient descent [arXiv:cs/0309048; ResearchGate; IDSIA publications list].

4. The Hutter Prize — what it is, how compression relates to intelligence, and its AGI implications

The Hutter Prize is a €500,000 competition funded by Marcus Hutter (not Schmidhuber, though closely aligned with Schmidhuber’s theoretical framework). It rewards improvements in losslessly compressing a 1 GB English Wikipedia XML text file (enwik9). The prize is motivated by the principle that superior data compression correlates with higher machine intelligence: to compress well, a system must discover the underlying structure and regularities in the data [prize.hutter1.net; Wikipedia]. Schmidhuber’s formal theory of creativity explicitly links compression progress to intrinsic motivation and intelligence—his 2006 IEEE paper argues that art, science, and humor all arise from the reward of finding compressible patterns. The AGI implication is that an optimal compressor would need to build a world model of comparable richness to human understanding; thus, compression benchmarks may serve as proxy measures for general intelligence [Hutter Prize FAQ; Grokipedia].

5. His most recent papers on universal AI and self-improvement (2022–2025)

• Who invented deep residual learning? (2025) — arXiv:2509.24732. A historical and technical argument tracing residual connections back to Hochreiter’s 1991 LSTM thesis and Schmidhuber lab’s subsequent extensions to feedforward nets [arXiv; Schmidhuber personal page]
• Annotated History of Modern AI and Deep Learning (2022, updated 2025) — arXiv:2212.11279. A comprehensive technical report reasserting priority claims for many modern deep-learning concepts [arXiv; IDSIA page]
• Medical Referring Image Segmentation (2026) — MICCAI / Springer. Co-authored with Hou et al. Not central to universal AI but shows ongoing applied work [OpenAlex baseline]
• Learning to Forget: Continual Learning with Adaptive Weight Decay (2026) — arXiv:2604.27063. Addresses catastrophic forgetting, a key obstacle to continual self-improvement [OpenAlex baseline]
• Interestingness as an Inductive Heuristic for Future Compression Progress (2026) — arXiv:2605.14831. Formalizes “interestingness” as an inductive bias for exploration, extending the 1990–2010 creativity theory [note from prior heartbeat data]

6. His public disagreements with other AI researchers on the path to AGI

Schmidhuber is well known for asserting that he and his lab have been denied adequate recognition for foundational deep-learning contributions. In his 2015 essay “Paper by ‘Deep Learning Conspiracy’” (mirrored on his IDSIA site), he argued that the machine-learning community fails to properly assign credit, and that Geoffrey Hinton, Yoshua Bengio, and Yann LeCun received disproportionate acclaim relative to earlier work from his lab [IDSIA; Wikipedia]. He specifically claims that: (a) Hochreiter invented LSTM in 1991; (b) his students introduced residual connections in the early 1990s, long before the 2015 ResNet paper; and (c) many ideas LeCun lists as original contributions were published by Schmidhuber’s lab years earlier [Reddit /r/MachineLearning discussion of “Annotated History”; arXiv:2509.24732]. These disputes are not merely historical; they reflect a substantive disagreement about whether incremental gradient-based scaling (the dominant paradigm) or formal universal-self-improvement (Gödel Machines, compression progress) is the correct theoretical foundation for AGI.

Key Papers (2022–2025)

Gary Marcus (NYU / Independent)

1. His core argument for why deep learning alone cannot produce AGI

Marcus argues that deep learning, especially large language models (LLMs), relies on statistical correlation across massive text corpora without acquiring true understanding, systematic generalization, or causal reasoning. Neural networks learn from a "blank slate" and lack the innate structures that allow humans to generalize compositionally, track enduring objects, and reason abstractly. He contends that scaling alone -- more parameters and data -- cannot overcome these fundamental limitations, because the architectures lack explicit mechanisms for symbolic manipulation, logic, and reliable abstraction. In his 2022 essay "Deep learning is hitting a wall" and subsequent Substack posts, Marcus reiterated that "the pure scaling of LLMs would not get us to AGI" and that symbolic tools must be included.

Sources: Gary Marcus Substack ("A knockout blow for LLMs?", 2025); Axios interview (2025); Newsweek profile ("The Bittersweet, Forrest Gump-like World of AI", 2025); arXiv:2510.18212.

2. What "neurosymbolic AI" means -- how neural nets and symbolic reasoning combine

Neurosymbolic AI integrates neural networks -- which excel at pattern recognition and learning from data -- with symbolic reasoning systems that manipulate discrete symbols, rules, and logic. Marcus defines this as "combining the best of the currently popular neural network approach ... with the symbolic approach that is ubiquitous in logic, computer science, and cognitive science." He points to AlphaGo and AlphaProof as neurosymbolic systems: a neural net evaluates positions while a symbolic tree-search planner selects moves. His 2026 AAAI paper "The Future Is Neuro-Symbolic: Where Has It Been, and Where Is It Going?" traces the history and argues that hybrid architectures are necessary for robust generalization and compositional reasoning.

Sources: AAAI 2026 paper "The Future Is Neuro-Symbolic" (OpenAlex W7138884354); Gary Marcus Substack ("AlphaProof, AlphaGeometry, ChatGPT, and why the future of AI is neurosymbolic", 2025).

3. His concept of "innateness" in AI -- what cognitive structures he believes must be built in from birth

Marcus takes a nativist perspective, arguing that human intelligence relies on innate cognitive structures shaped by evolution -- including priors for object permanence, numerosity, spatial cognition, and grammatical rules. In "Innateness, AlphaZero, and Artificial Intelligence" (2018), he argues that learning relies on innate knowledge about the structure of the world. For AI, this means systems should not start from a blank slate; instead, they should be endowed with built-in representational primitives, compositional operations, and reasoning schemas. He advocates for "structured hybrid systems that incorporate innate knowledge and abilities that represent knowledge compositionally."

Sources: Marcus, G. (2018). "Innateness, AlphaZero, and Artificial Intelligence" (arXiv:1801.05667); Newsweek profile (2025).

4. His specific critiques of GPT and large language models as paths to AGI

Marcus argues that LLMs are fundamentally limited by their reliance on next-token prediction over a fixed training corpus. They lack true comprehension, are prone to hallucination, cannot reliably perform multi-step logical reasoning, and fail at systematic generalization -- performing well on familiar statistical patterns but failing on novel compositional problems. He has documented failures of GPT-4 and o3 on linguistic structure tasks (e.g., "Fundamental Principles of Linguistic Structure are Not Represented by o3", 2025). He also warns that LLMs cannot learn from interactions in real time and are therefore not a credible route to the kind of AGI that could transform society.

Sources: arXiv:2502.10934; arXiv:2506.06299; Marcus Substack ("Three years on, ChatGPT still isn't what it was cracked up to be", 2025).

5. What hybrid architecture he proposes as the correct path forward

Marcus proposes explicitly architected hybrid systems that combine neural networks with separate, pre-existing symbolic reasoning modules. These systems should incorporate innate priors, maintain explicit representations of objects and relations, support compositionality, and integrate learning with reasoning. He points to successes such as AlphaProof (theorem prover + neural network) as proof that hybrid architectures can solve problems beyond the reach of either approach alone. He does not advocate for GOFAI-style pure symbol manipulation, but for neural-symbolic integration where each component handles what it does best.

Sources: TechTalks ("The case for hybrid artificial intelligence", 2020); Marcus Substack ("How o3 and Grok 4 Accidentally Vindicated Neurosymbolic AI", 2025).

6. His latest papers, articles, or collaborations on fixing AI reasoning (2022-2025)

• "The Future Is Neuro-Symbolic: Where Has It Been, and Where Is It Going?" -- AAAI 2026 (co-authored with Vaishak Belle et al.; Marcus as senior author). https://doi.org/10.1609/aaai.v40i48.42130
• "A Definition of AGI" -- arXiv:2510.18212, 2025.
• "How Malicious AI Swarms Can Threaten Democracy" -- OSF / arXiv:2506.06299, 2025.
• "Fundamental Principles of Linguistic Structure are Not Represented by o3" -- arXiv:2502.10934, 2025.
• "Testing AI on language comprehension tasks reveals insensitivity to underlying meaning" -- Scientific Reports, 2024. https://doi.org/10.1038/s41598-024-79531-8

Marcus also maintains an active Substack ("Marcus on AI") where he publishes critiques of frontier models and policy commentary.

Sources: OpenAlex (W7138884354, W4416981563, W4415473393, W4415967303, W4407687096, W4404349757).

Key Papers (2022-2025)

Blake Richards (Mila / McGill)

1. His research on whether backpropagation has a real analog in biological neurons

Richards has extensively investigated whether backpropagation-of-error (BP) -- the dominant algorithm in deep learning -- has a biological analog. His landmark 2016 paper with Guerguiev and Lillicrap, "Towards deep learning with segregated dendrites" (arXiv:1610.00161), proposed that pyramidal neurons could approximate BP using segregated dendritic compartments: basal dendrites receive bottom-up feedforward signals, while apical dendrites receive top-down feedback that conveys error or credit information. This multi-compartment conductance-based model showed that deep supervised learning is possible with biologically constrained neurons. More recently, his lab has explored whether burst-dependent synaptic plasticity and dendritic error signals can coordinate credit assignment across hierarchical circuits without requiring exact BP.

Sources: arXiv:1610.00161; Richards & Lillicrap, "Dendritic solutions to the credit assignment problem" (Stanford Psych209 reading); The Transmitter profile (2025).

2. What "predictive coding" is and how it may explain learning in the brain and in AI

Predictive coding is a theory in which the brain constantly generates predictions about incoming sensory input and updates its internal models by minimizing prediction error. Richards' lab has tested this hypothesis experimentally: a WIRED / CIFAR profile (2021) describes his work using mice to test whether something analogous to AI's predictive learning occurs in the neocortex. Predictive coding can explain the structure of cortical representations -- why visual cortex develop feature-selective responses -- and offers a unifying objective for both biological and artificial networks. In AI, predictive coding has been used to build energy-efficient, self-supervised learning systems that do not require labelled data.

Sources: CIFAR / WIRED profile (2021); The Transmitter profile (2025).

3. His work on dendritic computation -- how the structure of neurons informs AI architecture

Richards argues that the detailed morphology of neurons -- especially the distinct apical and basal dendrites of pyramidal cells -- is not incidental but computationally essential. His framework treats dendritic compartments as functionally specialized processing units: basal compartments handle feedforward inference, while apical compartments integrate feedback and modulatory signals for credit assignment. This insight informs AI architectures such as multi-compartment neural networks and segregated-dendrite models that aim to be both biologically plausible and computationally effective. His 2019 Nature Neuroscience paper with Lillicrap and others formalized a "deep learning framework for neuroscience" that encourages studying circuits through the lens of objective functions, architectures, and learning rules.

Sources: Richards, B.A. et al. (2019). "A deep learning framework for neuroscience." Nature Neuroscience, 22, 1761-1770. https://doi.org/10.1038/s41593-019-0520-2; CAN Young Investigator Award description (2019).

4. His most recent papers from the LiNC Lab (2022-2025)

• "Looking to the brain to improve energy efficiency of AI" -- Current Biology, 2026. https://doi.org/10.1016/j.cub.2026.06.020
• "Network learning: Neuron-by-neuron error signals in the neocortex" -- Current Biology, 2026.
• "MIMIC-MJX: Neuromechanical Emulation of Animal Behavior" -- arXiv:2511.20532, 2025.
• "Embedded Universal Predictive Intelligence: a coherent framework for multi-agent learning" -- arXiv:2511.22226, 2025.
• "Learning From the Past with Cascading Eligibility Traces" -- arXiv:2506.14598, 2025.
• "Decoding Dynamic Visual Experience from Calcium Imaging via Cell-Pattern-Aware Pretraining" -- arXiv:2510.18516, 2025.
• "The schema spectrum: Emergent structures and levels of abstraction in AI and the brain" -- Neuron, 2024.
• "A unified, scalable framework for neural population decoding" -- NeurIPS, 2024.
• "Mixture-of-Depths: Dynamically allocating compute in transformer-based language models" -- arXiv:2404.02258, 2024 (co-authored with David Raposo, Sam Ritter, Timothy Lillicrap, et al.).

Sources: OpenAlex (W7169766900, W7163893437, W4417153210, W4416943814, W4415312099, W4416056166); Neuron (2024); NeurIPS (2024); arXiv:2404.02258.

5. How his neuroscience findings are being applied to practical deep learning improvements

Richards' neuroscience insights have directly inspired several practical advances. The "Mixture-of-Depths" architecture (2024) dynamically allocates compute in transformers based on predictions, improving energy efficiency -- an idea informed by the brain's selective routing. The 2026 Current Biology paper "Looking to the brain to improve energy efficiency of AI" explicitly translates biological energy constraints into design principles for hardware and algorithms. MIMIC-MJX (2025) uses biologically plausible neural control policies learned from kinematics, bridging neuroscience and robotics. His lab also develops scalable neural population decoding frameworks that improve brain-machine interfaces and data efficiency in neural networks.

Sources: Current Biology (2026); arXiv:2511.20532; arXiv:2404.02258.

6. What he says is the biggest gap between how real brains learn vs. how current AI learns

At a NeuroAI workshop convened by his group, Richards identified three major brain-AI capability gaps: (1) current AI's inability to robustly interact with the physical world in real time, (2) brittle learning that produces systems unsuited for continual adaptation, and (3) unsustainable energy and data inefficiency compared to biological brains. He notes that real brains learn continually from a single stream of experience with extraordinary sample efficiency, while AI requires massive curated datasets and retraining. His research agenda aims to close these gaps through bio-inspired architectures, predictive learning, and dendritic computation.

Sources: ResearchGate / NeuroAI workshop summary (2025); Mila profile (2025).

Key Papers (2022-2025)

Irina Rish (Mila / Université de Montréal)

1. What "continual learning" is and why catastrophic forgetting is the central problem

Continual learning (CL) is the ability of an AI system to learn from a non-stationary stream of tasks and data over time, accumulating knowledge without requiring retraining from scratch. The central challenge is catastrophic forgetting (also called catastrophic interference): when neural networks are trained on a new task, they rapidly and drastically overwrite previously learned information. Rish frames this as one of AI's biggest barriers to flexible, lifelong intelligence. She notes that "systems are capable of adapting to new data, but when faced with new datasets and tasks, they often experience catastrophic forgetting, a process where new information washes out old information."

Sources: CIFAR profile (2021); Rish, I. et al. "Towards Continual Reinforcement Learning: A Review and Perspectives" (JAIR, 2022); arXiv:2403.05175.

2. How her approach is inspired by how human brains accumulate knowledge over a lifetime

Rish draws inspiration from the human brain's ability to learn continuously from a single stream of experience, consolidating memories without interference. Her research explores structural plasticity, hippocampal replay mechanisms, neuromodulation, and sparse distributed representations as biological principles that could mitigate forgetting in artificial networks. She proposes that AI systems should incorporate mechanisms analogous to memory consolidation, complementary learning systems (hippocampal-cortical interaction), and adaptive regularization. Her lab, the CERC in Autonomous AI, explicitly aims to "augment AI by developing better computational models and connecting AI research with vast amounts of knowledge about the mind and brain."

Sources: CERC-AAI Lab website (www.irina-lab.ai); Mila profile (2025); Brain Inspired Podcast (BI 123).

3. Her work on neuroimaging and neuroscience-informed AI architectures

Before joining Mila/UdeM, Rish was a researcher at IBM's T.J. Watson Research Center where she led the Neuro-AI challenge and worked on projects at the intersection of neuroscience and AI. Her current interests include dynamical systems approaches to brain imaging analysis, biologically plausible reinforcement learning, and sparse modeling. She has applied probabilistic inference and optimization techniques to neuroimaging data and argues that neuroscience insights -- about memory consolidation, reward processing biases, and neural coding -- should directly inform AI architecture design. She holds 64 patents and has published over 80 research papers.

Sources: UdeM research profile; SENSUM profile; IBM background.

4. Her most recent papers on lifelong learning and brain-inspired ML (2022-2025)

• "Relevant and Irrelevant: A Renormalization Group Analysis of Transformer Attention" -- arXiv:2607.15449, 2026.
• "Representing Time Series as Structured Programs for LLM Reasoning" -- arXiv:2606.12481, 2026.
• "Rank Collapse, Fixed Points, and the Renormalization Group Structure of MLP Residual Networks" -- arXiv:2606.10324, 2026.
• "Failed Reasoning Traces Tell You What Is Fixable (But Not by Reading Them)" -- arXiv:2606.05145, 2026.
• "Continual Learning with Foundation Models: An Empirical Study of Latent Replay" -- CVPR Workshop, 2022 (arXiv:2205.00329).
• "Towards Continual Reinforcement Learning: A Review and Perspectives" -- Journal of Artificial Intelligence Research, 2022.
• "A definition of continual reinforcement learning" -- NeurIPS, 2023.
• "Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference" -- ICLR, 2019 (co-authored with Riemer et al.); foundational for her CL paradigm.

Sources: OpenAlex (W7169923092, W7169767433, W7164511933, W7164208545, W7163519647). CVPR 2022 workshop paper; arXiv:2205.00329; arXiv:2403.05175.

5. What she has organized or published through the Neuro-AI workshop series

Rish was a key organizer of the Neuro-AI challenge at IBM and has been involved in multiple Neuro-AI and continual learning workshops. She gave a keynote on "Continual learning in the era of foundation models" at the NeurIPS 2024 workshop on Scalable Continual Learning for Lifelong Foundation Models. She has also taught the "Towards AGI" graduate course sequence at UdeM in multiple years, covering continual learning, scaling laws, and emergent behaviors. Her teaching and workshop contributions emphasize bridging neuroscience and AI through formal computational models.

Sources: Rish talk archive; NeurIPS 2024 workshop on Scalable Continual Learning; UdeM course listings (IFT6167, IFT6760A).

6. Her views on what brain mechanisms AI must replicate to achieve flexible, general intelligence

Rish argues that AI must replicate several core brain mechanisms to achieve flexible general intelligence: (1) continual learning without catastrophic forgetting, through mechanisms like structural plasticity and hippocampal replay; (2) biologically plausible credit assignment and reinforcement learning; (3) sparse, distributed representations that enable efficient knowledge storage; (4) neuromodulatory systems that gate plasticity and attention; and (5) integration of multiple memory systems (episodic, semantic, procedural). She believes that connecting AI with knowledge from neuroscience, psychology, and philosophy is essential for overcoming the brittleness of current systems.

Sources: CERC-AAI Lab mission statement; Brain Inspired Podcast (BI 123); Mila profile.

Key Papers (2022-2025)

1. His research on credit assignment -- how the brain determines which neurons caused an outcome

Credit assignment is the problem of determining how much each synapse or neuron contributed to a behavioral outcome, so that learning can target the right connections. Lillicrap's research explores how biological neural circuits might solve this problem without using the exact backpropagation algorithm, which is considered biologically implausible. In the landmark 2020 review "Backpropagation and the brain" (Nature Reviews Neuroscience), co-authored with Adam Santoro and others, he examined how backpropagation-through-time can serve as a normative benchmark against which biological theories are compared. He also co-authored "Dendritic solutions to the credit assignment problem" with Blake Richards, showing how apical dendrites of pyramidal neurons can receive feedback signals that assign credit across layers.

Sources: Lillicrap, T.P. & Santoro, A. (2020). "Backpropagation and the brain." Nature Reviews Neuroscience; Richards & Lillicrap (2019) "Dendritic solutions to the credit assignment problem."

2. His work on feedback alignment and target propagation as alternatives to backpropagation

In 2014-2016, Lillicrap and colleagues introduced "feedback alignment" (FA), a biologically plausible alternative to backpropagation. FA replaces the symmetric transpose weight matrices used in backprop with fixed random matrices for feedback. Surprisingly, the network still learns effectively because the forward weights adapt to align with the fixed random feedback over time. The 2016 paper "Random synaptic feedback weights support error backpropagation for deep learning" (Nature Communications) demonstrated that deep networks can learn with random feedback connections, circumventing the "weight transport problem." This work was awarded the 2025 Sejnowski-Hinton Prize at NeurIPS. He has also investigated target propagation and deep feedback control as further biologically grounded credit assignment algorithms.

Sources: Lillicrap et al. (2016). "Random synaptic feedback weights support error backpropagation for deep learning." Nature Communications; NeurIPS 2025 Sejnowski-Hinton Prize announcement; arXiv:2106.06044.

3. How his motor learning research connects to AI planning and control systems

Lillicrap's PhD thesis (Queen's University, 2014) was titled "Modelling Motor Cortex using Neural Network Controls Laws." He trained neural networks to control abstract models of the primate arm, showing how network dynamics are shaped by limb physics and how these properties predict observed patterns of primary motor cortex activity. This work bridges biological motor control and AI by demonstrating that neural networks can learn robust feedback control laws. At DeepMind, he extended these insights to reinforcement learning for continuous control, co-authoring the foundational "Continuous control with deep reinforcement learning" (DDPG) paper and contributing to AlphaGo / AlphaZero's policy optimization components. His motor-learning perspective emphasizes that intelligent action requires closed-loop control shaped by environmental dynamics.

Sources: Lillicrap PhD thesis (2014); DeepMind AlphaGo paper (Nature, 2016); ScienceDirect author profile.

4. His most cited recent DeepMind papers on biological reinforcement learning (2022-2025)

• "Random synaptic feedback weights support error backpropagation for deep learning" -- Nature Communications, 2016 (awarded 2025 Sejnowski-Hinton Prize; >20,000 citations).
• "Mixture-of-Depths: Dynamically allocating compute in transformer-based language models" -- arXiv:2404.02258, 2024 (co-authored with David Raposo, Sam Ritter, Blake Richards, et al.).
• "Evaluating Long-Term Memory in 3D Mazes" -- ICLR, 2023 (co-authored with Jurgis Pasukonis).
• "AndroidInTheWild: A Large-Scale Dataset For Android Device Control" -- NeurIPS, 2023 (co-authored with Christopher Rawles, Alice Li, et al.).
• "Gemini: A Family of Highly Capable Multimodal Models" -- arXiv, 2023 (co-author).
• "Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context" -- arXiv, 2024 (co-author).

While some of these are not strictly biological RL, Lillicrap's core biological RL contributions (FA, backprop-and-brain, DDPG) remain among the most cited in the field. His citation count on Google Scholar exceeds 182,000.

Sources: Google Scholar (htPVdRMAAAAJ); dblp profile; Research.com; NeurIPS 2025 prize announcement.

5. What he believes biological neural circuits can teach us about improving AI training

Lillicrap argues that biological circuits offer solutions to credit assignment, continual learning, and energy efficiency that are still missing from AI. Apical dendrites, he proposes, provide a biological mechanism for conveying error signals across layers without exact backpropagation. He believes that studying how real neurons learn from sparse, delayed rewards can inspire more sample-efficient RL algorithms. The brain's ability to learn online from a single continuous stream of experience, with robustness to non-stationarity, is a key target for AI. He also emphasizes that biological learning is local, event-driven, and metabolically constrained -- principles that could make AI far more efficient.

Sources: "Backpropagation and the brain" (2020); Stanford MBCT talk abstract ("New models and algorithms for addressing limitations in deep reinforcement learning"); CIFAR profile.

6. His collaboration with neuroscientists and what those cross-disciplinary projects have produced

Lillicrap collaborates extensively with neuroscientists. With Blake Richards, he co-authored foundational papers on dendritic credit assignment and the deep-learning framework for neuroscience (Nature Neuroscience 2019, involving 20+ co-authors from both AI and neuroscience). He worked with Stephen H. Scott (motor cortex) during his PhD and with Colin Akerman on synaptic feedback. The 2019 Nature Neuroscience paper explicitly brought together cognitive scientists, computational neuroscientists, and machine learning researchers to formalize how deep learning concepts (objective functions, architecture, learning rules) can be mapped onto neural circuit analysis. These collaborations have produced both biological insights and new AI algorithms.

Sources: Richards, B.A. et al. (2019). "A deep learning framework for neuroscience." Nature Neuroscience; Lillicrap CV; Wikipedia / DeepAI profiles.

Key Papers (2022-2025)

1. Full explanation of AIXI — what it is, how it works, and what it proves about optimal intelligence

AIXI is the canonical model of Universal Artificial Intelligence introduced by Marcus Hutter in the early 2000s and formalized in his 2005 book Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability. AIXI combines Solomonoff induction — a Bayesian method that assigns prior probability to hypotheses according to their Kolmogorov complexity — with sequential decision theory to define an agent that maximizes expected cumulative reward over its lifetime. The agent maintains a distribution over all computable environments and, at each step, selects the action that maximizes expected future reward with respect to that distribution. Hutter proved that AIXI is optimal in the sense that its expected reward is at least as high as any other agent's in the limit, up to a constant additive term; this is the optimality or Pareto optimality result for universal intelligence [Hutter 2005; Hutter, Quarel & Catt 2024, An Introduction to Universal Artificial Intelligence, CRC Press].

Sources: Hutter official bibliography (http://www.hutter1.net/official/bib.htm); Hutter, M. Universal Artificial Intelligence, Springer, 2005; Hutter, Quarel & Catt, An Introduction to Universal Artificial Intelligence, CRC Press, 2024.

2. Why Kolmogorov complexity and data compression are connected to intelligence

Hutter's framework rests on the idea that intelligence is the ability to predict and act well in arbitrary computable environments. Kolmogorov complexity measures the shortest program that can generate a given observation string, so the most compact description of data is also the most general model of its regularities. AIXI uses Kolmogorov complexity through Solomonoff's universal prior, which weights hypotheses by their simplicity. Because compression and prediction are mathematically equivalent — better compression implies better prediction of the next token — Hutter argues that lossless compression performance is a direct, measurable proxy for general intelligence. This is the theoretical basis of the Hutter Prize [Lex Fridman Podcast #75; Hutter Prize FAQ].

Sources: Lex Fridman Podcast #75 with Marcus Hutter; Hutter Prize site (prize.hutter1.net).

3. The Hutter Prize — what it rewards, who has won it, and its relationship to AGI benchmarking

The Hutter Prize is a EUR 500,000 competition funded by Marcus Hutter that rewards improvements in losslessly compressing a 1 GB English Wikipedia XML file (enwik9). The prize is motivated by Hutter's thesis that compression is equivalent to understanding: to compress the file well, a program must discover linguistic structure, facts, and regularities. Winners have included Alexander Rhatushnyak, who improved compression ratios using specialized PAQ variants. The prize serves as an AGI benchmark because a universal compressor would implicitly need world knowledge comparable to human understanding [prize.hutter1.net; Wikipedia].

Sources: prize.hutter1.net; Wikipedia "Hutter Prize".

4. His most recent theoretical papers on universal intelligence and AGI (2022–2025)

• Lossless Compression: A New Benchmark for Time Series (2025) — arXiv:2509.21002. Co-authors Grégoire Delétang, Anian Ruoss, and Shane Legg. Extends compression-as-intelligence benchmarking to time-series data.
• Value Under Ignorance in Universal Artificial Intelligence (2025) — arXiv:2512.17086. Co-author Cole Wyeth. Formalizes decision-making when the agent is ignorant of the true environment prior.
• An Introduction to Universal Artificial Intelligence (2024) — CRC Press. Textbook with David Quarel and Elliot Catt making UAI accessible to a broader ML audience.
• Language Modeling Is Compression (2023) — arXiv:2309.10668, ICLR 2024. Co-authored with DeepMind colleagues; connects large language models to lossless compression.
• Modeling the Arrows of Time with Causal Multibaker Maps (2024) — Entropy 26(9): 776. With A. Ebtekar; theoretical work on causality and time.
• U-Clip: On-Average Unbiased Stochastic Gradient Clipping (2023) — arXiv:2302.02971. With Bryn Elesedy; practical optimization theory relevant to training universal approximations.

Sources: Hutter official bibliography; arXiv records for arXiv:2509.21002, arXiv:2512.17086, arXiv:2309.10668.

5. What limitations AIXI has in practice and how he or others propose addressing them

AIXI is uncomputable because it requires evaluating the Kolmogorov complexity of all possible environments. In practice it must be approximated. The main approximation line is Monte Carlo AIXI (MC-AIXI), developed by Veness and colleagues, which uses particle filtering to approximate the environment distribution. Hutter and collaborators have also explored resource-bounded versions, feature-based reductions, and stochastic optimization. A second limitation is that AIXI assumes a known reward function; recent work by Hutter and Wyeth on value under ignorance addresses decision-making when the reward signal is not fully specified [Hutter 2005; Veness et al. "A Monte-Carlo AIXI Approximation", JAIR 2011; arXiv:2512.17086].

Sources: Hutter 2005; Veness et al., JAIR 2011; arXiv:2512.17086.

6. How his theory compares to other formal frameworks for general intelligence (e.g., Schmidhuber's)

Hutter's AIXI and Schmidhuber's Gödel Machine / universal search frameworks both pursue mathematically optimal intelligence, but they emphasize different aspects. AIXI is an external optimality criterion: it defines the best possible agent given a computable environment class and a reward signal. Schmidhuber's Gödel Machine is self-referential: it can rewrite any part of its own code, including its learning algorithm, if the rewrite is provably optimal under its axioms. Schmidhuber's formal theory of creativity adds intrinsic motivation through compression progress, while AIXI relies on an extrinsic reward signal. Hutter's framework provides a normative benchmark; Schmidhuber's provides a mechanism for open-ended self-improvement. Both agree that compression, prediction, and universal computation are central to intelligence [Hutter 2005; Schmidhuber 2006 Gödel Machine paper].

Sources: Hutter 2005; Schmidhuber, "The Gödel Machine: A Fully Self-Referential Optimal Universal Self-Improver", 2006.

Key Papers (2022–2025)

Ida Momennejad (Microsoft Research)

1. What "successor representations" are and how they model hippocampal memory for planning

A successor representation (SR) is a predictive map that encodes the expected future occupancy of states given a policy. Formally, for each state, the SR is the discounted sum of expected future feature vectors. This compresses the value function into a product of expected future states and immediate rewards, enabling rapid revaluation when rewards change while preserving structural knowledge of the environment. Momennejad's work places SRs in the hippocampus: the hippocampus builds predictive state representations that support model-based planning without requiring a full forward model. Her 2022 Journal of Neuroscience paper with Iva Brunec showed that predictive representations are organized hierarchically across hippocampus and prefrontal cortex, paralleling how RL agents cache multi-scale state predictions [Dayan 1993; Momennejad & Brunec 2022].

Sources: Dayan, P. "Improving Generalization for Temporal Difference Learning: The Successor Representation", 1993; Brunec & Momennejad, Journal of Neuroscience, 2022.

2. Her research on cognitive maps — how the brain builds spatial and conceptual maps for reasoning

Momennejad investigates how the hippocampus and prefrontal cortex construct cognitive maps — structured representations of spatial and conceptual relationships that support flexible behavior. Her research extends Tolman's classical cognitive-map idea to abstract, conceptual domains, showing that the same neural machinery used for spatial navigation supports generalization and planning in non-spatial tasks. At Microsoft Research she has developed the CogEval benchmark to test whether large language models build such structured cognitive maps. The 2023 NeurIPS paper showed that LLMs fail on tasks requiring explicit map-based planning, suggesting that current generative models lack the representational substrate for flexible reasoning [Momennejad et al. 2023, NeurIPS].

Sources: Momennejad personal homepage (https://www.momen-nejad.org/); Momennejad et al., "Evaluating Cognitive Maps and Planning in Large Language Models with CogEval", NeurIPS 2023.

3. How her neuroscience models are being applied to AI at Microsoft Research

At Microsoft Research NYC, Momennejad translates hippocampal predictive-representation theory into AI systems and benchmarks. Her CogEval work uses tasks from animal cognition to evaluate whether LLMs build cognitive maps. She has also worked on adaptive replay buffers for deep model-based RL and on multi-agent LLM collaboration. The unifying thread is importing representational principles from neuroscience — predictive maps, hierarchical state abstraction, offline consolidation — into machine-learning systems, rather than relying solely on scale [Microsoft Research profile; Momennejad et al. ICLR 2023 on replay buffers].

Sources: Microsoft Research publications page; Momennejad et al., ICLR 2023.

4. Her most recent papers on memory, planning, and AI cognition (2022–2025)

• Predictive Representations in Hippocampal and Prefrontal Hierarchies (2022) — Journal of Neuroscience, 42(2): 299–312. With Iva Brunec.
• Replay Buffer With Local Forgetting for Adaptive Deep Model-Based RL (2023) — ICLR 2023. With Rajendran, Rahimi-Kalahroudi, van Seijen, and Chandar.
• Evaluating Cognitive Maps and Planning in Large Language Models with CogEval (2023) — NeurIPS 2023. With Hasanbeig, Frujeri, Sharma, Ness, Jojic, Palangi, and Larson.
• Memory, Space, and Planning: Multiscale Predictive Representations (2024) — arXiv:2401.09491.
• Collective Innovation in Groups of Large Language Models (2024) — arXiv preprint. With Nisioti, Risi, Oudeyer, and Moulin-Frier.
• Memory and Planning in Brains and Machines: Multiscale Predictive Representations (2025) — book chapter in Space, Time, and Memory (Oxford University Press).

Sources: Momennejad homepage; Microsoft Research profile; arXiv records.

5. What she says is the most important brain circuit for AI to replicate to achieve general reasoning

Momennejad's research implies that the hippocampal-prefrontal circuit — especially the predictive, multi-scale representations it builds — is the most important substrate for AI to replicate. This circuit supports memory, planning, generalization, and credit assignment across time. Her CogEval results suggest that without explicit predictive-map-like structures, even very large language models fail at tasks requiring structured planning and generalization. The key brain mechanism is therefore the construction and use of successor-style predictive representations for model-based decision-making [Momennejad 2024 arXiv:2401.09491; NeurIPS 2023 CogEval paper].

Sources: Momennejad 2024 arXiv:2401.09491; Momennejad et al. NeurIPS 2023.

6. Her work on how offline memory consolidation (like sleep) could inform AI training processes

Momennejad's work on replay buffers and predictive representations naturally connects to offline consolidation. Biological replay during sleep reorganizes hippocampal memories for cortical storage; analogously, replay buffers in RL revisit past experiences to stabilize credit assignment. Her 2023 ICLR paper on replay buffers with local forgetting addresses how agents can continue learning without catastrophic interference — a computational problem with direct parallels to sleep-based consolidation. The broader implication is that AI systems may need structured offline phases to consolidate memories, reorganize representations, and avoid forgetting [Momennejad et al. ICLR 2023; Momennejad 2025 book chapter].

Sources: Momennejad et al., ICLR 2023; Momennejad 2025 OUP book chapter.

Key Papers (2022–2025)

1. Her research on memory replay during sleep — what exactly happens neurologically

Penelope (Penny) Lewis is Professor of Psychology and Director of the Neuroscience and Psychology of Sleep Lab (NaPS) at Cardiff University. Her research shows that during sleep — particularly slow-wave (NREM) sleep — the hippocampus reactivates or "replays" memory traces. This replay is coordinated with sleep spindles (bursts of thalamo-cortical activity) and slow oscillations, which are thought to drive the transfer of memory representations from the hippocampus to the neocortex for long-term storage. In 2024 her group published evidence that memory reactivation during slow-wave sleep enhances relational learning in humans, demonstrating that replay is not merely a byproduct of sleep but actively shapes memory [Cardiff profile; Santamaria et al., Communications Biology, 2024].

Sources: Cardiff University profile (https://profiles.cardiff.ac.uk/staff/lewisp8); Santamaria et al., "Memory reactivation in slow wave sleep enhances relational learning in humans", Communications Biology 7, 288 (2024).

2. How her findings on sleep-based consolidation relate to experience replay in AI (used by DeepMind)

Lewis's findings provide biological grounding for experience replay in reinforcement learning. In AI, replay buffers store past experiences and re-sample them during training; in the brain, sleep replays recent experiences to consolidate them. Lewis has explicitly drawn this parallel in public talks and interviews: the brain's offline replay is a biological form of the AI mechanism. Her lab's work on targeted memory reactivation — using sensory cues during sleep to selectively strengthen memories — is a biological analog of prioritized experience replay, where important transitions are sampled more frequently [DeepMind blog on AI and neuroscience; Lewis public talks].

Sources: Google DeepMind blog, "AI and Neuroscience: A virtuous circle"; Lewis media interviews and talks.

3. What her studies reveal about how the brain transfers short-term to long-term memory

Lewis's research supports the standard model of systems consolidation: initially, memories depend on the hippocampus; during sleep, reactivation of hippocampal traces gradually strengthens cortical representations until the memory becomes hippocampus-independent. Her studies show that this transfer is not a simple duplication but an active process involving replay, spindle-ripple coupling, and cortical plasticity. The 2024 Imaging Neuroscience paper from her group showed that cueing memory reactivation during NREM sleep produces long-term plasticity in both brain and behavior — direct evidence that sleep-based consolidation actively rewires memory circuits [Rakowska et al., Imaging Neuroscience 2, imag-2-00250, 2024].

Sources: Rakowska et al., Imaging Neuroscience 2, imag-2-00250 (2024).

4. Her most recent papers on memory consolidation and offline learning (2022–2025)

• Memory reactivation in slow wave sleep enhances relational learning in humans (2024) — Communications Biology 7, 288. Co-authors Santamaria, Kashif, McGinley.
• Cueing memory reactivation during NREM sleep engenders long-term plasticity in both brain and behaviour (2024) — Imaging Neuroscience 2, imag-2-00250. Co-authors Rakowska, Bagrowska, Lazari, Navarrete, Abdellahi, Johansen-Berg.
• MorpheusNet: Resource efficient sleep stage classifier for embedded on-line systems (2024) — IEEE SMC proceedings. With Kavoosi et al.
• Disarming emotional memories using targeted memory reactivation during rapid eye movement sleep (2025) — Imaging Neuroscience 3, IMAG.a.924. Co-authors Greco, Foldes, Abdellahi, Wawrzuta, Harrison, Murphy.
• Distributed and gradual microstructure changes are associated with the emergence of behavioural benefit from memory reactivation (2025) — Imaging Neuroscience. Co-authors Rakowska et al.

Sources: Cardiff NaPS lab; Sleep Engineering publications (https://www.sleepengineering.co.uk/publications-2-0); Communications Biology and Imaging Neuroscience.

5. Any direct collaborations or citations from AI researchers using her neuroscience work

Lewis's work is cited in the AI-neuroscience literature that underpins experience replay. DeepMind's foundational DQN paper (Mnih et al., 2015) cited biological replay as inspiration for experience replay, and subsequent work by Hassabis and colleagues on hippocampal replay intersects with Lewis's findings. Lewis has also collaborated with Oxford neuroscientists including Heidi Johansen-Berg and the Oxford Sleep and Circadian Neuroscience Institute. While she is not primarily an AI researcher, her empirical findings on replay and consolidation inform AI work on continual learning, replay buffers, and neuromorphic sleep states [DeepMind DQN Nature 2015; Lewis Cardiff profile].

Sources: Mnih et al., "Human-level control through deep reinforcement learning", Nature 2015; Cardiff profile.

6. What she believes AI engineers misunderstand about how biological memory actually works

Lewis has emphasized in interviews that memory is not a simple storage-and-retrieval system. Sleep actively transforms memories: it stabilizes some, weakens others, and integrates them into existing knowledge. In particular, REM sleep appears to decouple emotional arousal from memories, suggesting that intelligent agents may need emotion-regulation-like processes to remain stable. AI engineers, she implies, often treat replay as a mere rehearsal of past data, whereas biological replay is selective, state-dependent, and reshapes representations. The timing, neuromodulatory context, and targeted nature of biological replay matter as much as the content [The Guardian interview, Jan 2025; Lewis talks].

Sources: The Guardian, "How to optimise the cognitive benefits of dreams and sleep", 12 Jan 2025.

Key Papers (2022–2025)

Yann LeCun (Meta AI / NYU / AMI Labs)

1. His Joint Embedding Predictive Architecture (JEPA) — what it is and why he says it beats LLMs for AGI

JEPA is a self-supervised learning architecture that learns to predict representations of the world in a latent space rather than reconstructing pixels or tokens. The core idea is to train a model to predict the embedding of a missing or future part of an input from the embedding of an observed part. LeCun argues that generative models (including LLMs) waste capacity predicting irrelevant sensory detail, whereas JEPA learns abstract, task-relevant representations. He believes this is closer to how the brain works and is a more promising path to human-level AI than scaling autoregressive language models. Meta released I-JEPA in 2023, V-JEPA in 2024, V-JEPA 2 in June 2025, and LeJEPA in November 2025 [Meta AI blog, June 2023; arXiv:2301.08243; arXiv:2506.09985; arXiv:2511.08544].

Sources: Meta AI blog, "I-JEPA: The first AI model based on Yann LeCun's vision for more human-like AI", 13 June 2023; arXiv:2301.08243.

2. His detailed argument for why large language models cannot achieve human-level intelligence

LeCun has repeatedly argued that LLMs are limited because they: (1) are trained on discrete text tokens and therefore lack grounded understanding of the physical world; (2) cannot plan or reason in a persistent, structured way; (3) consume enormous amounts of data and energy relative to biological learners; (4) lack persistent memory and world models; and (5) are brittle and prone to hallucination because they predict likely tokens rather than model reality. In his 2022 paper "A Path Towards Autonomous Machine Intelligence" he proposed that animal intelligence relies on world models learned through self-supervised observation, and that text-only training cannot replicate this [LeCun 2022 position paper; interviews 2023–2025].

Sources: LeCun, Y. "A Path Towards Autonomous Machine Intelligence", 2022; Fenxi analysis of LeCun's JEPA vs LLM argument; LeCun public talks 2023–2025.

3. What "world models" mean in his framework and which brain regions they may correspond to

In LeCun's framework, a world model is an internal simulator that predicts how the world will evolve in response to actions. It enables planning by allowing an agent to imagine consequences before acting. He proposes a modular architecture with: a perception module (analogous to sensory cortex), a world model (analogous to association cortex and hippocampus), an actor (motor cortex / basal ganglia), and a cost module (prefrontal cortex / reward systems). JEPA's latent prediction is meant to model how cortical areas predict activity in other areas rather than raw sensory input. The hippocampus corresponds to short-term episodic prediction and planning; the prefrontal cortex to cost evaluation and action selection [LeCun 2022; Meta AI blog].

Sources: LeCun 2022 position paper; Meta AI JEPA blog posts.

4. His most recent Meta AI papers and technical blog posts on JEPA and world models (2022–2025)

• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning (2025) — arXiv:2506.09985. Large video world model pretrained on over one million hours of video; capable of zero-shot robot manipulation.
• LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics (2025) — arXiv:2511.08544. With Randall Balestriero; removes heuristics from earlier JEPA variants.
• V-JEPA: An open source model for learning visual representations from video (2024) — Meta AI blog / arXiv:2403.00504.
• I-JEPA: The first AI model based on Yann LeCun's vision for more human-like AI (2023) — Meta AI blog / arXiv:2301.08243.
• Learning and Leveraging World Models in Visual Representation Learning (2024) — arXiv:2403.00504. With Garrido, Assran, Ballas, Bardes, Najman.
• A Path Towards Autonomous Machine Intelligence (2022) — LeCun's position paper outlining the world-model agenda.

Sources: Meta AI blog; arXiv records for arXiv:2506.09985, arXiv:2511.08544, arXiv:2403.00504, arXiv:2301.08243.

5. His public debates with other AI researchers (Hinton, Bengio, Marcus) on AGI paths

LeCun has publicly debated Geoffrey Hinton, Yoshua Bengio, and Gary Marcus on the path to AGI. He disagrees with Hinton and Bengio on AI risk: LeCun is more skeptical of existential risk from current systems and believes open research is preferable to heavy regulation. He agrees with Bengio and Marcus that LLMs alone are insufficient, but disagrees with Marcus's emphasis on symbolic AI, favoring instead self-supervised world models and energy-based architectures. With Hinton, he disagrees about whether scaling LLMs will lead to human-level intelligence; LeCun argues it will not. In November 2025 he left Meta to become Executive Chairman of AMI Labs, a startup valued at roughly $3.5 billion by March 2026, to pursue the JEPA/world-model agenda independently [CNBC, Nov 2025; Fortune, Dec 2025; public debates 2023–2025].

Sources: CNBC, "Meta chief AI scientist Yann LeCun is leaving to create his own startup", 19 Nov 2025; Fortune, 19 Dec 2025; LeCun public statements and debates.

6. What specific cognitive abilities he says AI must develop before AGI is possible

LeCun lists several capabilities AI must develop before AGI: (1) world models that capture the physics of the environment; (2) persistent memory; (3) reasoning and planning through internal simulation; (4) the ability to learn from observation with sample efficiency comparable to animals and humans; (5) hierarchical representations at multiple timescales; and (6) joint embedding architectures that learn abstract predictions rather than pixel-level generation. He argues that these are prerequisites for any credible path to human-level intelligence and that current LLMs lack all of them [LeCun 2022 position paper; Meta AI blog 2023; V-JEPA 2 technical report 2025].

Sources: LeCun 2022; Meta AI blog 2023; arXiv:2506.09985.

Key Papers (2022–2025)

Demis Hassabis

Gemma 4 Technical Report (2026-07-02, arXiv:2607.02770) — co-authored with the Gemma Team. The report introduces Gemma 4, a new generation of open-weight, natively multimodal language models designed to advance compute efficiency and reasoning. The suite includes a 4B parameter model and larger variants, continuing DeepMind's push toward efficient, capable generalist models.

Yann LeCun

Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learning (2026-08-20, Nature Communications doi:10.1038/s41467-026-76076-4) — co-authored. The work proposes holistic self-supervised learning for medical foundation models, aiming to improve robustness and generalization in radiology applications beyond the standard JEPA/world-model agenda.

Irina Rish

When Geometry Aligns: Dihedral Hidden-State Transformations in UNet, ViT, and DiT Architectures (2026-07-03, arXiv:2607.03580) — co-authored with Mojtaba Faramarzi and Alex Lamb. The paper examines how structural symmetry groups (dihedral transformations) shape hidden-state geometry across convolutional and transformer-based diffusion architectures, linking geometric inductive biases to generalization and sample efficiency.

Tim Lillicrap

Societal Frameworks Can Improve LLM Alignment (2026-06-25, ACM FAccT doi:10.1145/3805689.3806486) — co-authored. The paper argues that LLM alignment should be framed through societal value frameworks rather than isolated preference optimization, reflecting Lillicrap's growing interest in the social and normative context of learning systems.

Ida Momennejad

Algorithmic Grammar of Flexible Cognition: A Walk through Latent Operations (2026-08-04, PsyArXiv doi:10.31234/osf.io/cjv3g_v1) — single-author. Momennejad proposes a formal language for moving between cognitive modes (memory vs. generalization, cached inference vs. step-by-step reasoning) using reinforcement-learning constructs, directly extending her successor-representation and cognitive-map research.

A Compositional Framework for Open-ended Intelligence (2026-06-13, arXiv:2606.15386) — co-authored with Roberta Răileanu. The authors argue that open-ended intelligence requires a mathematics of compositionality and adaptation to novel environments, integrating successor-representation and reinforcement-learning ideas into a broader theory of flexible, general reasoning.

Cross-Cutting Themes

Across all 14 researchers, several common threads emerge:

1. Memory and replay are central to learning. Hassabis, Lewis, Momennejad, Rish, and Schmidhuber all emphasize that intelligent systems must store, replay, and consolidate past experience. Biological replay during sleep and AI experience replay are treated as instances of the same underlying principle.

2. World models and predictive representations. LeCun, Momennejad, Hawkins, Hutter, and Bengio converge on the need for internal models that predict future states, whether through JEPA latent prediction, successor representations, reference frames, or AIXI/Solomonoff induction.

3. Credit assignment without exact backpropagation. Richards and Lillicrap have made the detailed structure of neurons — especially dendritic compartments — central to understanding how biological circuits could assign credit. Their work feeds into biologically plausible alternatives to backprop.

4. Continual learning and catastrophic forgetting. Rish, Schmidhuber, and Lewis address how systems learn continuously without overwriting old knowledge, drawing on hippocampal-cortical interaction, replay, and structural plasticity.

5. Reasoning and System 2 processing. Bengio, Marcus, and LeCun agree that current pattern-matching AI lacks explicit reasoning, planning, and causal understanding, even if they disagree on the right architectural remedy.

6. Compression, prediction, and intelligence. Hutter and Schmidhuber both ground intelligence in prediction and compression, providing formal yardsticks that complement the neuroscience-inspired work of the others.

Divergent Views

The researchers disagree on several key points:

• Scaling vs. architecture. LeCun, Marcus, and Bengio argue that scaling autoregressive LLMs will not reach AGI; others focus less on this debate and more on specific mechanisms. Hinton (not in this list but referenced) represents the scaling camp; LeCun opposes it most vocally.
• Symbolism vs. connectionism. Marcus advocates explicit neurosymbolic hybrid systems with innate symbolic structures; LeCun and Hawkins favor neural, embodied, predictive systems; Hutter favors mathematically optimal sequential agents.
• Biological fidelity vs. functional equivalence. Hawkins demands cortical-column-level biological detail; Hutter cares about functional optimality independent of biological implementation; Richards and Lillicrap occupy the middle ground, using biology to inspire algorithms.
• Extrinsic reward vs. intrinsic motivation. Hutter's AIXI optimizes an external reward signal; Schmidhuber's creativity theory uses compression progress as intrinsic reward.
• Risk and regulation. LeCun is skeptical of existential risk narratives and favors open research; Bengio chairs international AI safety reports and has pivoted toward safety governance.

Emerging Consensus

Despite disagreements, a loose consensus is forming around the following:

• LLMs alone are insufficient for AGI. Even researchers who disagree on solutions (LeCun, Bengio, Marcus) agree that next-token prediction over text is not enough.
• World models and predictive representations are necessary. Whether called successor representations, reference frames, JEPA embeddings, or AIXI environments, the ability to simulate and predict future states is widely seen as essential.
• Memory systems must be multi-scale and offline-consolidated. Replay, sleep-like consolidation, and separation of fast episodic and slow semantic storage appear in multiple frameworks.
• Biological plausibility matters as inspiration, not necessarily as implementation. Most researchers use neuroscience to identify computational principles rather than to copy every detail.

Actionable Intelligence for Oction

For Oction's client deployments and architecture decisions, this heartbeat suggests:

1. Prefer modular, memory-aware architectures over monolithic LLM-only pipelines. Client-specific knowledge should be stored in isolated, structured memory systems with replay/consolidation mechanisms, not just embedded in model weights.

2. Invest in predictive world models and JEPA-like latent prediction for client-specific domains. Where data is scarce, self-supervised latent prediction may outperform generative token prediction and reduce exposure of raw client data.

3. Treat continual learning and catastrophic forgetting as first-class constraints. Client systems will receive non-stationary data; design replay buffers, regularization, and offline consolidation from the start.

4. Keep compression and prediction as evaluation metrics. Hutter's compression-intelligence equivalence and Schmidhuber's compression progress provide principled ways to measure understanding without labeled benchmarks.

5. Maintain biological inspiration without over-engineering biological fidelity. Use hippocampal replay, dendritic credit assignment, and successor representations as design principles, but implement them in computationally efficient forms suitable for client hardware.

6. Stay architecture-agnostic where possible; avoid betting entirely on transformers or LLMs. The field is converging on the need for world models, reasoning, and memory, but the winning implementation is not yet clear. Build infrastructure that can plug in multiple model families and learning paradigms.