Creating Scalable AGI: the Open General Intelligence Framework

Daniel A. Dollinger; Michael Singleton

REVIEW 3 major objections 5 minor 1 cited by

Creating Scalable AGI: the Open General Intelligence Framework

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This paper proposes the Open General Intelligence framework, a modular reference architecture that treats AGI as coordinated specialized modules with a dynamic routing system rather than a single scaled-up model.

desk verdict A readable but shallow position paper that renames existing cognitive architecture concepts and leaves its central weighting mechanism undefined; worth engaging with only as a draft for discussion. read the letter →

arxiv 2411.15832 v2 pith:JNA257T7 submitted 2024-11-24 cs.AI

Daniel A. Dollinger , Michael Singleton This is my paper · ORCID

classification cs.AI

keywords ArtificialGeneralIntelligenceCognitiveArchitectureModularAISystemsMulti-ModalIntegrationDynamicProcessingSystemFabricInterconnectScalableReference

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper proposes a blueprint, called the Open General Intelligence (OGI) framework, for building artificially general intelligence as a set of specialized modules that cooperate like regions of the human brain rather than as one monolithic model. The central claim is that real-world problem solving requires integrating many data types—text, images, sound, tactile, and numeric inputs—and that a dynamic processing system routing work across modules through a high-speed fabric interconnect can deliver the flexibility and scalability that today's single-modality models lack. The authors intend OGI as a macro design reference, not a finished implementation, and they ground its framework areas in known features of human cognition. If the framework is right, it offers a common architecture for AGI that could extend to medical diagnosis, quality assurance, financial decision-making, and other tasks that demand cross-modal reasoning. The paper does not report a working system; it specifies the architecture and proposes how to validate it.

What carries the argument

The load-bearing mechanism is the Dynamic Processing System: a programmable instruction layer that routes tasks and re-weights modules in real time, expressed as $\Phi(C, E_t) = \mathrm{softmax}(g(C, E_t))$, mapping the current context and task to a probability distribution over modules. Around it sit the six Framework Areas and four macro design requirements: multiple data type support, multiple specialized processing modules, an interconnected processing fabric, and cognitive process switching. The second load-bearing mechanism is the Fabric Interconnect, a many-to-many network assumed to transfer information between modules with near-zero latency and hardware-level processing speeds. That interconnect is what makes real-time context updates and cross-module integration possible, and a queuing fallback is described for lower-performance implementations.

What would settle it

Build a small OGI prototype with two specialized modules (for example, vision and language) joined by a fabric interconnect whose added latency can be set to 0, 100 microseconds, 1 millisecond, and 10 milliseconds, and measure task-switching accuracy on a visual question-answering task at each setting. If accuracy collapses at latencies that existing high-performance networks can already achieve, the paper's load-bearing assumption that near-zero-latency hardware-level interconnect is required and achievable is falsified.

Watch

Extended reading notes

Core claim

The authors claim that AGI will not come from scaling single-modality models, and they offer OGI as the macro design reference for a modular cognitive architecture. In OGI, cognition is distributed across six Framework Areas—Executive Control, Autonomous Processing, Input/Output Integration, Short Term Memory, Long Term Memory, and the Fabric Interconnect—coordinated by a Dynamic Processing System that controls routing, primary goals, instructions, and weights. The architecture is explicitly non-linear, with mesh connections among areas. The Dynamic Processing System is given a compact formal shape, a weighting function $\Phi : (C, E_t) \to \Delta^n$ with $w_t = \Phi(C, E_t) = \mathrm{softmax}(g(C, E_t))$, intended to re-balance modules in real time as context and tasks change. The authors' key claim is that this combination produces real-time adaptability, multi-modal integration, and scalable processing that current siloed LLM architectures cannot provide.

Load-bearing premise

The load-bearing premise is that a many-to-many fabric interconnect with near-zero latency and hardware-level processing speeds is both required and achievable at scale, because without such an interconnect the framework's promised real-time adaptability and cross-module integration collapse.

Editorial extensions

If this is right

If OGI becomes the design reference for AGI, future systems will be built as modular multi-modal architectures rather than as scaled monolithic models.
The Dynamic Processing System gives system operators an external programming layer to set goals, routing, and weights, which addresses control and safety directly.
Real-world tasks requiring several data types at once—medical diagnosis, sarcasm and irony detection, quality assurance, equipment troubleshooting—become target applications of one architecture rather than separate research problems.
OGI's standardized input/output integration layer means new sensors, databases, or specialized models can be added without redesigning the executive or autonomous processing areas.
Validation of AGI shifts from single-task benchmark accuracy toward metrics like task-switching speed, cross-module coordination overhead, and resource utilization across hardware platforms.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A natural testable extension is to relax the near-zero-latency fabric assumption and run an OGI-style system over an asynchronous message bus, measuring how task-switching accuracy degrades with added latency; that would show which promised capabilities are architectural and which are hardware-bound.
The weighting function $g(C, E_t)$ is left unspecified, so a concrete parameterization trained by meta-learning would convert the framework from a diagram into a measurable optimization problem.
The executive/autonomous split resembles the standard fast-slow dichotomy in current AI systems, so one prediction is that an OGI implementation will show human-like dual-task interference when executive and autonomous processes compete for short-term memory.
Although the paper targets general intelligence, the same separation of external control from internal autonomy could serve as a systems-engineering checklist for regulated multi-modal AI applications such as medical or financial decision support.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The paper is a clear, well-written position paper proposing a modular cognitive architecture for AGI. It is honest that validation is future work, and the analogies to human cognition and ASIC-like routing are accessible. The explicit list of framework areas and the emphasis on a fabric interconnect give the vision a concrete shape. If you want a quick overview of what a reference architecture for AGI might look like, this is not a bad starting point.

But the substance is thin. The components—executive control, autonomous processing, working and long-term memory, routing, and an interconnected fabric—have been around for decades in SOAR, ACT-R, global workspace theory, and mixture-of-experts systems. The paper cites none of these and does not differentiate its contribution from them. That is a serious omission for a paper claiming novelty.

The bigger problem is that the central control mechanism is vacuous. The Dynamic Processing System is supposed to balance modules via Eq. (2), but that equation is just a softmax of an unspecified function g. The stress-test note is right: softmax is surjective onto the simplex, so this imposes no constraint and describes no actual mechanism. The paper itself says the objective function for g is future work, which is honest, but that means the load-bearing component of the architecture is a placeholder. The fabric interconnect with near-zero latency is also assumed without any engineering evidence, but that is secondary; even a perfect fabric would not help if the control signal it carries is undefined.

The paper does have some strengths. It is readable, explicit about challenges and future work, and the idea of programmable instruction layers with external guardrails is worth discussing. But as a technical contribution it is currently more of a position essay than a design. It would need a real implementation, comparisons to existing architectures, and a defined weighting function before I would consider it a serious contribution.

I would send it to peer review only because it represents a kind of systems-level thinking that the field may need to see more of, even in imperfect form. But a referee should demand major revisions: related work, formalization of the weighting mechanism, and evidence that the interconnect assumptions are plausible. For a reading group, it could spark a useful debate about what is actually new in AGI architecture proposals, but don't expect deep insight. I would not cite it in my own work as it stands.

Referee Report

3 major / 5 minor

Summary. The paper proposes the Open General Intelligence (OGI) framework, a modular systems architecture inspired by human cognition, intended as a macro design reference for AGI. The framework comprises three layers: overall macro design guidance, a dynamic processing system for routing and weighting, and framework areas such as executive control, autonomous processing, IO integration, short/long-term memory, and a fabric interconnect. The authors claim that this design enables real-time adaptability, multi-modal integration, and scalable processing. Section IV formalizes the dynamic weighting mechanism as a mapping from a context-and-task tuple to a probability simplex via an unspecified function g, and Section V outlines future benchmarks and validation metrics; no implementation, data, or experimental results are reported.

Significance. If fully specified and validated, the OGI framework could serve as a useful high-level blueprint for modular AGI systems, and the paper's separation of design guidance, control, and functional areas is a clear organizational contribution. The authors are also honest about open challenges and ground their design choices in a broad cognitive-neuroscience literature. However, as it stands, the central control mechanism is formally vacuous and no component has been implemented or tested; the paper is therefore a conceptual sketch rather than a validated architecture, and its significance depends entirely on future specification and empirical work.

major comments (3)

[Section IV-A, Eq. (2)] The formalization of the dynamic weighting system is vacuous as written: since softmax is surjective onto the probability simplex and the function g(C, Et) is never defined in the manuscript, Eq. (2) imposes no constraint on the weight vector wt and cannot support the claims of real-time adaptability and cognitive process switching. Section IV-E explicitly defers developing objective functions for g to future work, so the paper's central control mechanism is a placeholder rather than a design; the authors should either provide a concrete candidate for g with its inputs and objective, or remove the formalization and clearly mark the framework as purely qualitative.
[Section III-H] The fabric interconnect is assumed to provide many-to-many, near-zero-latency, hardware-speed communication, yet no evidence, scaling argument, or reference to existing interconnect technology is given; because the paper identifies real-time adaptability as a key differentiator of OGI, this assumption is load-bearing, and the paper should at least bound the latency and bandwidth requirements or present a fallback design that preserves the claims without near-zero latency.
[Section III (general)] The architecture largely restates its requirements: the four macro design guidance items (multiple data types, multiple processing modules, interconnected fabric, cognitive switching) map directly onto the framework areas (IO integration, processing areas, fabric interconnect, dynamic processing system), and the paper does not provide an independent argument for why these areas are necessary or sufficient for general intelligence; this circularity limits the framework's testability and should be explicitly acknowledged.

minor comments (5)

[Section I] The phrase 'three distinct tenants' should be 'three distinct tenets'.
[Section V-A] The phrase 'the drop in accuracy compared to standard models will demonstrate GOI's robustness' uses 'GOI' and should be 'OGI'.
[References] Reference [2] appears twice in the reference list with different formatting (one entry uses the *Journal of Experimental Neuroscience* style and one uses the *Annual Review of Physiology* style); the duplicate should be merged into a single consistent entry.
[Section II-C] The phrase 'feedback and forward feedback' is awkward and should likely be 'feedforward and feedback'.
[Abstract / Introduction] The abstract and introduction describe OGI as a 'novel systems architecture' without stating that no component has been implemented or tested; adding an explicit status-of-work sentence early on would better align the claims with the body of the paper.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is an explicitly provisional architecture proposal with no fitted parameters or derived predictions.

full rationale

The paper makes no quantitative prediction and fits no parameters. Its contribution is a modular architecture proposal; the macro guidance items are requirements, and the framework areas are design components, not derived results. All citations are to external human-cognition and AI literature; there are no author self-citations and no uniqueness theorem imported from prior work. The only formal statement is Eq. (2), wt = softmax(g(C, Et)), with g left undefined. The paper itself states in Section IV-A that 'developing a robust and flexible weighting system ... remains a significant technical hurdle' and in Section IV-E calls 'developing objective functions for g(C, Et)' future research. Thus Eq. (2) is an acknowledged placeholder, not a fitted input disguised as a prediction or a definition that secretly contains the claimed output. The near-zero-latency fabric assumption is unsupported but not circular. Accordingly, no circular step can be exhibited under the required standard.

Assumptions & free parameters 0 free parameters · 5 assumptions · 5 invented entities

The framework rests on unproven assumptions about cognition and engineering feasibility. There are no fitted numerical parameters, but there are multiple invented architectural entities with no implementation or external evidence. The central claim depends on these entities behaving as described.

assumptions (5)

domain assumption Cognition must occur across multiple specialized modules that can seamlessly operate as a single system.
The paper's foundational premise, stated in the abstract and Section I, is asserted without derivation or empirical justification.
domain assumption Human brain modularity is a valid template for engineered AGI.
Section II argues from neuroscience that AI systems should be modular, but the transfer from biology to engineered systems is assumed.
ad hoc to paper A many-to-many fabric interconnect with near-zero latency is feasible at scale.
Section III-H requires hardware-level speeds and near-zero latency, but provides no engineering path or evidence.
ad hoc to paper A suitable function g exists so that wt = softmax(g(C,Et)) controls module weights in real time.
Equation (1)-(2) in Section IV-A defines the weighting mechanism, but g is never specified, so the existence of an effective g is assumed.
domain assumption Memory is a spectrum with pliable connections and lifelong learning is required.
Section III-G asserts a spectrum model of memory based on analogy to biology; this design choice is assumed.

invented entities (5)

Dynamic Processing System
purpose: Routes tasks, sets goals, instructions, and weights across modules.
Described in Section III-B as a control component but never implemented; no testable specification.
Executive Control Area
purpose: Central reasoning and internal monologue, monitors and directs other areas.
Section III-C describes a conceptual module with no implementation.
Autonomous Processing Area
purpose: Fast, reactive execution of learned stored procedures.
Section III-D describes a conceptual module with no implementation.
Fabric Interconnect
purpose: Many-to-many, near-zero-latency communication between modules.
Section III-H asserts performance requirements; no hardware or simulation is provided.
Short Term Memory / Long Term Memory areas
purpose: Working context and persistent storage with a spectrum transition.
Sections III-F and III-G present analogical memory components with no implemented interfaces.

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Cite this review

Pith. "Pith review of Creating Scalable AGI: the Open General Intelligence Framework." pith.science (2026). https://pith.science/paper/JNA257T7

@misc{pith2026241115832,
  author       = {Pith},
  title        = {Pith review of: Creating Scalable AGI: the Open General Intelligence Framework},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JNA257T7}},
  note         = {Machine review of arXiv:2411.15832}
}

read the original abstract

Recent advancements in Artificial Intelligence (AI), particularly with Large Language Models (LLMs), have led to significant progress in narrow tasks such as image classification, language translation, coding, and writing. However, these models face limitations in reliability and scalability due to their siloed architectures, which are designed to handle only one data modality (data type) at a time. This single modal approach hinders their ability to integrate the complex set of data points required for real-world challenges and problem-solving tasks like medical diagnosis, quality assurance, equipment troubleshooting, and financial decision-making. Addressing these real-world challenges requires a more capable Artificial General Intelligence (AGI) system. Our primary contribution is the development of the Open General Intelligence (OGI) framework, a novel systems architecture that serves as a macro design reference for AGI. The OGI framework adopts a modular approach to the design of intelligent systems, based on the premise that cognition must occur across multiple specialized modules that can seamlessly operate as a single system. OGI integrates these modules using a dynamic processing system and a fabric interconnect, enabling real-time adaptability, multi-modal integration, and scalable processing. The OGI framework consists of three key components: (1) Overall Macro Design Guidance that directs operational design and processing, (2) a Dynamic Processing System that controls routing, primary goals, instructions, and weighting, and (3) Framework Areas, a set of specialized modules that operate cohesively to form a unified cognitive system. By incorporating known principles from human cognition into AI systems, the OGI framework aims to overcome the challenges observed in today's intelligent systems, paving the way for more holistic and context-aware problem-solving capabilities.

Figures

Figures reproduced from arXiv: 2411.15832 by the authors.

**Figure 2.** The OGI Framework Architecture Each of the area’s functional capabilities have been described, with special consideration for the all requirements outlined in each of the following subsections. This framework is not intended to be linear. The OGI framework is outlined below: A. Intelligent System Macro Design Guidance The intelligent system’s macro capabilities describe overall design guidance when building an inte… view at source ↗

**Figure 3.** The intelligent system can be programmed for dynamic operations [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Autonomous processing operates with minimal executive assistance [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 5.** Figure 5: The transition from short to long term memory may be phased [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

The Internet of Large Language Models: An Orchestration Framework for LLM Training and Knowledge Exchange Toward Artificial General Intelligence
cs.AI 2025-01 reject novelty 2.0 of 10

The paper proposes the Internet of LLM framework for model sharing, unified environments, agent-path optimization, and compute-sharing incentives, but presents no implementation or empirical evidence that it works.

Reference graph

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [1]

Chawla and K

M. Chawla and K. P. Miyapuram, ”Context-Sensitive Computational Mechanisms of Decision Making,” Journal of Experimental Neu- roscience, vol. 12, Nov. 2018, Art. no. 1179069518809057. doi: 10.1177/1179069518809057

work page doi:10.1177/1179069518809057 2018

[2] [3]

Z. J. Chen, et al., ”Revealing Modular Architecture of Human Brain Structural Networks by Using Cortical Thickness from MRI,” Cere- bral Cortex , vol. 18, no. 10, pp. 2374-2381, 2008. doi: 10.1093/cer- cor/bhn003

work page doi:10.1093/cer- 2008

[3] [4]

Sternberg, ”Modular processes in mind and brain,” Cogni- tive Neuropsychology , vol

S. Sternberg, ”Modular processes in mind and brain,” Cogni- tive Neuropsychology , vol. 28, no. 3-4, pp. 156-208, 2011. doi: 10.1080/02643294.2011.557231

arXiv 2011

[4] [5]

C. M. A. Pennartz, et al., ”How ’visual’ is the visual cortex? The interactions between the visual cortex and other sensory, motiva- tional and motor systems as enabling factors for visual perception,” Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences , vol. 378, no. 1886, p. 20220336, 2023. doi: 10.1098/rstb.2022.0336

work page arXiv 2023

[5] [6]

M. B. Kennedy, ”Synaptic Signaling in Learning and Memory,” Cold Spring Harbor Perspectives in Biology , vol. 8, no. 2, p. a016824, Dec

work page

[6] [7]

L. C. Sincich, et al., ”Preserving information in neural transmis- sion,” The Journal of Neuroscience: The Official Journal of the So- ciety for Neuroscience , vol. 29, no. 19, pp. 6207-6216, 2009. doi: 10.1523/JNEUROSCI.3701-08.2009

work page doi:10.1523/jneurosci.3701-08.2009 2009

[7] [8]

Vatansever, et al., ”Default Mode Dynamics for Global Functional Integration,” The Journal of Neuroscience: The Official Journal of the Society for Neuroscience , vol

D. Vatansever, et al., ”Default Mode Dynamics for Global Functional Integration,” The Journal of Neuroscience: The Official Journal of the Society for Neuroscience , vol. 35, no. 46, pp. 15254-15262, 2015. doi: 10.1523/JNEUROSCI.2135-15.2015

work page doi:10.1523/jneurosci.2135-15.2015 2015

[8] [9]

E. J. M ¨uller, et al., ”Parallel processing relies on a distributed, low- dimensional cortico-cerebellar architecture,” Network Neuroscience, vol. 7, no. 2, pp. 844-863, Jun. 2023. doi: 10.1162/netn a 00308

doi:10.1162/netn 2023