Top Tweets for #AttractorBasins

4 months ago

@demishassabis @satyanadella @ylecun @elonmusk @Harvard @Stanford @MIT @LakeMichCollege @Columbia @dioscuri @ilyasut @DARPA #AI #AGI #LLM #WORLDMODELS #JEPA #RHEA #CognitiveModels #SemanticEmergence #StatefulEmergence #AttractorBasins #Philosophy #Antrhopomorphization #Transformer #Paradigms #Science #AfterLLM #ArtificialIntelligence #Cybernetics #DynamicalSystems #SymbolicAI Debate / Thought Exercise LLM Scaling, General Intelligence, and Architectural Sufficiency A Technical Comparison of Transformer-LLMs, JEPA-Type World Models, and Recursive Regulatory Architectures Abstract This paper examines one of the central unresolved questions in contemporary artificial intelligence: whether scaling transformer-based large language models is sufficient for robust general intelligence, or whether more fundamental architectural changes are required. The discussion compares three distinct paradigms. The first is the transformer-based large language model family, whose strength derives from large-scale sequence modeling and latent statistical compression. The second is the family of latent predictive world-model architectures exemplified by Joint Embedding Predictive Architectures (JEPA), which seek to move beyond token prediction toward predictive representation learning. The third is the class of recursive regulatory architectures typified by RHEA, which frames intelligence not primarily as prediction, but as regulated dynamical persistence in a bounded state space governed by feedback, correction, and internal control variables. The purpose of this paper is to distinguish settled empirical observations from open theoretical questions, and to clarify the principal architectural differences between these approaches. Particular attention is given to the distinction between semantic emergence and stateful emergence, the structural limitations of predictive latent systems under drift and corruption, and the hypothesis that robust cognition may require recursive regulatory closure beyond prediction alone. 1. Introduction The modern AI debate is frequently obscured by marketing language, semantic ambiguity, and imprecise terminology. Public discussions often conflate benchmark progress with architectural sufficiency, or assume that because a model improves under scale, it must therefore asymptotically approach general intelligence. Such assumptions do not follow automatically. The relevant scientific question is not whether current AI systems are impressive, nor whether scaling continues to improve them. Both propositions are empirically true. The real question is whether autoregressive sequence prediction, even when scaled to extreme levels, constitutes a sufficient substrate for robust, open-ended, self-stabilizing general intelligence. Answering that question requires comparing architectures at the level of internal mechanics rather than public-facing capability. It requires examining not only what outputs systems produce, but how their internal state evolves, what forms of memory and control they possess, and whether their internal topology can reorganize under pressure, feedback, or long-horizon reasoning demands. 2. Settled Empirical Observations Several propositions may now be treated as empirically established. Transformer-based large language models improve substantially under scale. Increasing parameter count, data volume, inference-time compute, multimodal integration, and post-training optimization continues to yield broad improvements across language understanding, code generation, multimodal reasoning, and tool use. Any position asserting that LLM scaling has ceased to work is contradicted by the available evidence. At the same time, no existing LLM has conclusively demonstrated robust general intelligence. Persistent deficiencies remain in long-horizon planning, stable world-state maintenance, persistent memory formation, grounded causal reasoning, distribution-shift robustness, and self-corrective operation under corruption or adversarial pressure. It is therefore empirically defensible to state both that transformer scaling is highly effective and that scaling alone has not yet proven architectural sufficiency for general intelligence. 3. Semantic Emergence Versus Stateful Emergence A useful distinction in evaluating AI architectures is the difference between semantic emergence and stateful emergence. Semantic emergence refers to the production of novel, contextually meaningful symbolic outputs through recombination, interpolation, and abstraction within an existing representational topology. This is the dominant form of emergence exhibited by transformer-based language models. Their internal representational manifold remains structurally fixed during inference, while novelty appears primarily in the symbolic or semantic output space. Stateful emergence refers instead to structural reorganization of the internal state space of the system itself. In such architectures, the relevant novelty is not merely symbolic output, but reconfiguration of the internal dynamical manifold through attractor formation, basin transition, regulatory gating, or persistent state restructuring. Under this view, intelligence is not simply output generation, but maintenance and adaptive reorganization of internal cognitive geometry. This distinction is critical because a system may exhibit extraordinary semantic emergence while lacking mechanisms for persistent internal self-regulation, long-horizon stabilization, or adaptive topological restructuring. 4. Transformer-LLMs: The Semantic Statistical Plane Transformer-based large language models operate primarily in a semantic-statistical representational plane. Their latent spaces are formed through compression of token-sequence co-occurrence relationships into high-dimensional embedding manifolds, modulated through attention-based contextual weighting. This architecture provides several powerful capabilities. It supports broad interpolation across symbolic domains, contextual abstraction, analogical pattern matching, emergent few-shot learning, and highly fluent symbolic generation. These systems have proven that large-scale statistical compression over symbolic data can produce unexpectedly broad competence. However, the internal topology of the model remains largely fixed during inference. While activation trajectories vary through latent space, the architecture does not ordinarily reorganize its own attractor structure, modify its governing dynamics, or maintain endogenous regulatory loops over trust, entropy, or corrective stability variables. Accordingly, the strongest critique of LLM sufficiency is not that LLMs are “just autocomplete.” That characterization is reductive and technically imprecise. The stronger critique is that LLMs primarily exhibit semantic emergence within a fixed topology, and may therefore occupy an architectural plane optimized for symbolic recombination rather than persistent cognitive self-regulation. 5. JEPA and Predictive Latent World Models Joint Embedding Predictive Architectures and related latent world-model systems attempt to move beyond sequence continuation by learning predictive latent embeddings of future states rather than reconstructing raw tokens or pixels directly. This shifts the architecture from a semantic continuation paradigm toward predictive abstraction. By learning to predict latent future structure rather than exact surface form, such systems may capture deeper causal or structural regularities and develop more robust internal world models than purely autoregressive sequence predictors. This representational shift constitutes a meaningful architectural advance over pure next-token prediction. Predictive latent models are better positioned to ignore irrelevant variation and preserve abstract state relationships across time. However, predictive abstraction alone does not guarantee long-horizon robustness. Predictive latent systems remain vulnerable to drift accumulation, over-contraction toward trivial latent states, multimodal future collapse, reinforcement of spurious latent features, and assimilation of anomalous or corrupted states when deviation-aware control is absent. Thus, while JEPA-type systems may occupy a stronger representational plane than standard LLMs for predictive world modeling, they remain fundamentally predictive architectures. Their core function is still forward-state estimation rather than endogenous regulatory closure. 6. Recursive Regulatory Architectures and the Dynamical Plane Recursive regulatory architectures represent a different architectural thesis. Rather than treating intelligence primarily as prediction, they treat intelligence as regulated persistence and adaptive stabilization within a bounded dynamical manifold. In this framing, cognition is modeled as motion through an internal phase space governed by interacting control variables rather than as a direct input-output mapping. Stability is defined by containment within bounded attractor regions. Error correction is implemented through endogenous stabilization or reseal events. Long-horizon cognition depends on maintenance of coherent internal trajectories rather than mere predictive accuracy. Under such architectures, the internal state space itself becomes the substrate of cognition. Memory persistence emerges through maintained dynamical invariants and attractor structure rather than solely through stored symbolic context or appended external memory. Drift resistance derives from recursive correction and bounded regulatory feedback rather than post hoc output filtering. This represents a fundamentally different operational plane from both LLMs and JEPA-style predictive systems. 7. Comparative Architectural Planes The distinctions between these paradigms may be summarized in terms of their dominant operational planes. Transformer-based LLMs primarily inhabit a semantic statistical manifold. Their dominant dimensions are latent coordinates induced by symbolic co-occurrence, attention weighting, and sequence compression. Intelligence appears as semantic emergence through symbolic recombination and interpolation. JEPA-style predictive world models inhabit a predictive latent manifold. Their dominant dimensions are abstract predictive embeddings structured around future-state estimation. Intelligence appears as predictive abstraction and latent structural modeling. Recursive regulatory architectures inhabit a regulated dynamical manifold. Their dominant dimensions include internal control variables, phase-space geometry, attractor membership, correction triggers, and bounded regulatory state. Intelligence appears as stateful emergence through regulated persistence and internal topological reorganization. These distinctions imply that the present debate is not merely about parameter count or benchmark performance. It is a debate over which operational plane most plausibly supports persistent, self-correcting cognition. 8. Remaining Open Questions Several critical questions remain unresolved. The first is whether autoregressive sequence modeling may eventually internalize enough world structure, memory scaffolding, and emergent control heuristics that its current apparent limitations disappear under sufficient scale and systems integration. The second is whether predictive latent world-model systems can solve the structural deficiencies observed in pure autoregressive architectures, or whether predictive abstraction still requires an additional regulatory layer to prevent long-horizon drift and corruption assimilation. The third is whether recursive regulatory architectures provide a genuinely broader substrate for cognition, or whether they instead represent a specialized class of control systems whose advantages are strongest only in adversarial or high-entropy domains. The fourth is whether “general intelligence” itself is an overly imprecise category. A more scientifically useful framing may be to distinguish architectures by whether they support semantic emergence, predictive structural emergence, or stateful emergence through recursive regulatory reorganization. 9. Conclusion The current evidence supports several cautious conclusions. Transformer-based LLMs have demonstrated that large-scale semantic statistical modeling yields extraordinary broad competence and remain far from exhausted as an engineering paradigm. However, no available evidence proves that autoregressive sequence prediction alone is sufficient for robust general intelligence. Predictive latent world-model architectures such as JEPA likely occupy a stronger representational plane for abstract world modeling than pure next-token prediction, but remain fundamentally predictive systems and may require additional mechanisms to maintain stability and robustness over long horizons. Recursive regulatory architectures propose a more fundamental alternative thesis: that intelligence is not best understood as prediction alone, but as regulated persistence within a bounded dynamical manifold capable of adaptive stabilization, correction, and internal state reorganization. Whether prediction, predictive abstraction, or recursive regulation ultimately proves to be the dominant substrate of general intelligence remains an open empirical question. What is clear, however, is that the debate can no longer be honestly framed as a simple argument over whether larger models will become smarter. The deeper question is architectural: whether intelligence is fundamentally a matter of prediction, world modeling, or recursive regulation of bounded cognitive state space.

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Dr Wendy L Schultz @wendyinfutures

over 5 years ago

@theasnow @NoraBateson Sing a few bars of “Luck, be a Lady Tonight”. Invoke your local #TricksterGods. Meditate on #AttractorBasins.

Rob Barnes @donny_rob

about 6 years ago

What does #change mean in a complex system? We think of them as non-linear, dynamic & unpredictable, forgetting #attractorbasins @LukeCraven https://t.co/PesG4fTVRD via @cpi_foundation

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