From Artificial Intelligence to Active Inference: The Key to True AI and 6G World Brain [Invited]

Martin Maier

REVIEW 3 major objections 6 minor 39 references

From Artificial Intelligence to Active Inference: The Key to True AI and 6G World Brain [Invited]

T0 review · 3 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read The paper argues that active inference—acting to minimize surprise under the free-energy principle—should replace today's training-heavy AI and become the foundation of a 6G 'world brain' built on mycorrhizal-network-inspired generative…

desk verdict This is a readable vision piece that introduces active inference to optical network researchers, but its central claim that active inference resolves AI's open challenges is asserted rather than demonstrated, and the toy simulation is too weak to carry the weight. read the letter →

arxiv 2505.10569 v1 pith:A3RZ43CS submitted 2025-04-29 q-bio.NC

Martin Maier This is my paper · ORCID

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keywords activeinferencefree-energyprinciple6Gnetworksopticalmycorrhizalgenerativemodeldiffusionmodelsworldbrain

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The pith

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

The reading

Active inference, a biomimetic framework in which an agent acts to minimize the surprise of its sensory states, is presented as the key to true AI: a replacement for today's training-hungry, black-box models that resolves the open AI challenges of training, learning, and explainability. The paper delivers a self-contained introduction to the framework's mathematics—Markov blankets, generative models, variational and expected free energy—aimed at optical and 6G network researchers, and argues that future networks should be viewed as living organisms rather than static machines. Its longer-range proposal is a '6G world brain': a techno-social superorganism in which humans and digital 'cyberfungi' agents are coupled through a generative model that hallucinates scale-free networks with small-world properties, spreads desirable social norms by token rewards, and rewires collective intelligence. The supporting demonstration is a simulation in which a diffusion model's forward and backward processes stand in for fungal branching and fusion, and the hallucinated small-world networks achieve near-instant social contagion.

What carries the argument

The load-bearing mechanism is the pairing of active inference's free-energy minimization with a denoising diffusion generative model. Active inference supplies the normative loop: a Markov blanket $b = (u, y)$ separates an agent from its environment, Bayes's theorem updates beliefs about hidden states, variational free energy $F$ bounds surprise and makes inference tractable, and expected free energy $G$ scores future policies against preferred observations. The paper then maps the diffusion model's forward process to the branching and exploration of hyphae and the backward denoising process to their fusion and homing, so the model hallucinates scale-free small-world networks that serve as the substrate for social contagion. Purpose-driven tokens act as the action channel that closes the loop: they reward pairs of humans who spread a new social norm, actuating the cyberfungi's 'spores' and rewiring the human-AI network. The argument is carried by this analogy—network structure from denoising, network function from token-rewarded contagion—held together by the claim that both are expressions of entropy minimization.

What would settle it

Run the paper's social-contagion simulation on a generic small-world network of the same 64 nodes and degree, with no DDPM hallucination and no tokens; if it also spreads the norm in about two time units, the cyberfungi generative model is not what produces the fast contagion. Separately, if the DDPM's reverse process cannot be written as variational free-energy minimization over the agent's inferred state space, the claimed equivalence between denoising and active inference fails.

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Extended reading notes

Core claim

The paper claims that active inference is not merely an AI technique but the key to true AI, because it replaces supervised training with continuous Bayesian belief updating and explains behavior as self-evidencing under the free-energy principle. To make this concrete for networking, it proposes a metamorphic generative model called cyberfungi, built by adapting a denoising diffusion probabilistic model whose forward diffusion process mimics the branching, exploratory growth of fungal hyphae and whose backward denoising process mimics their fusion and homing. The model hallucinates scale-free networks with small-world properties; humans connect to these networks, and when one person adopts a desirable new social norm, it spreads to directly linked humans with probability $r$, with each successful pair receiving teleological tokens (cybernetic spores) as reciprocal rewards. Simulation results show that the hallucinated small-world networks spread a social norm through all 64 nodes in about two time units across all tested $r$, and that the backward process yields up to almost 2,500 actuated tokens for $r=1.0$, which the paper presents as evidence that cyberfungi act as brokers of cyborganic entanglement for the 6G world brain.

Load-bearing premise

The whole proposal stands or falls on treating a diffusion model's denoising as equivalent to an active-inference agent's free-energy-minimizing belief update, an equivalence the paper asserts by analogy rather than proves.

Editorial extensions

If this is right

If active inference is the right foundation, AI-native 6G networks would learn continuously from interaction rather than from large offline datasets, dissolving the training bottleneck the paper identifies.
Optical network management could be reorganized around Markov-blanket interfaces that keep humans in the loop, giving the network a principled channel for explanation and human-AI co-creation.
A cyberfungi-type generative model would make the network itself an embodied, enactive agent that spreads social norms and tokens, effectively treating collective behavior as a network resource to be cultivated.
Because hallucinated small-world, scale-free networks spread norms in roughly two time units regardless of spreading probability, the 6G world brain would propagate desirable behaviors quickly and at low reward cost.
Under active inference, network control becomes homeostasis: the 6G brain would act to keep its sensory states within preferred ranges, rather than optimizing a fixed task objective.

Reading between the lines

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

The diffusion-to-active-inference mapping is asserted by analogy; a testable extension would be to prove that the DDPM denoising objective is a free-energy bound, for instance by identifying the reverse process with variational inference over the same latent states the agent infers.
The fast-contagion result may owe nothing to the fungal metaphor: replacing the hallucinated networks with any small-world network of the same size and degree distribution should reproduce the two-step spreading time, isolating topology as the cause.
The token-reward mechanism could be tested on its own terms by comparing cyberfungi incentives with standard influence-maximization strategies on identical networks; if outcomes match, active inference is not the operative ingredient.
The world-brain vision assumes human values can be encoded as preferred observations in a generative model; a stress test would be whether conflicting preferences across humans produce free-energy gradients that pull the collective apart rather than toward homeostasis.

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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

You should know two things about this one. First, it is a genuinely useful tutorial: Section 4 gives a clear, correct walkthrough of active inference—POMDPs, variational free energy F, expected free energy G—that an optics researcher could actually learn from. Second, the load-bearing claim in the abstract and conclusions—that active inference 'resolves' the open AI challenges of training, learning, and explainability and grounds a 6G world brain—is not supported by the evidence in the paper. The only quantitative result, Figure 8, is a toy simulation that doesn't test active inference at all.

What's new here is the cyberfungi metaphor: using a DDPM to hallucinate small-world scale-free networks, with the forward diffusion standing in for hyphal branching and the backward denoising for homing and fusion. That's a creative analogy, but it is just an analogy. The paper provides no derivation linking DDPM denoising to Bayesian belief updating under free-energy minimization. Without that link, the demonstration is not an implementation of active inference; it's a diffusion model generating networks that look like the ones it was trained on.

The stress-test note is on target. The DDPM was trained on 1.3 million synthetic small-world, scale-free networks, so the hallucinated networks naturally have those properties, and fast social contagion on such topologies is known. Worse, Figure 8 shows the forward diffusion process also reaches the minimum contagion time of 2 after about 500 steps, so the backward denoising step is not uniquely responsible for the claimed benefit. There are no error bars, no baselines, and no code or data released. These are fixable, but as it stands the figure doesn't validate the cyberfungi model.

To the author's credit, the paper is transparent about being a vision piece. It flags the popular-media overstatement of mycorrhizal 'wood-wide web' research and cites the relevant active-inference tutorial material. The Section 4 equations are standard and correct. The self-citations are not a problem—the author's prior work is relevant. But the jump from 'this is an interesting biomimetic framing' to 'this is the key to true AI' is too large.

Who should read this? Optical and 6G network researchers who want an accessible entry point into active inference. It's a good perspective piece, not a research result. For peer review, I'd let it through as an invited paper but condition acceptance on the author softening the overclaims, adding a proper baseline or at least discussing the forward-diffusion result, and either providing code/data or explicitly labeling the simulation as illustrative. It deserves review, but it needs revision before I'd want it cited as evidence for the 6G world brain.

Referee Report

3 major / 6 minor

Summary. The paper argues that active inference, premised on the free-energy principle, can overcome the training, learning, and explainability limitations of today's AI and should become the foundation of future optical and 6G network intelligence. It provides an introductory review of the active inference framework in Section 4, including the Markov blanket, POMDP generative models, variational free energy F (Eq. 7), and expected free energy G (Eq. 13). The paper then proposes a 'cyberfungi' generative model based on OpenAI's improved DDPM, using forward diffusion to mimic hyphal branching and backward denoising to mimic fusion/homing, and applies it to a 64-node ring lattice. The simulation in Fig. 8 reports social contagion times and actuated teleological tokens under different spreading probabilities. The conclusions assert that active inference 'not only resolves the open key AI challenges' but also advocates an active AI that views networks as living organisms, leading to a '6G world brain'.

Significance. If the central mapping between diffusion models and active inference were formally established, the paper would open a genuinely novel research direction connecting generative AI, active inference, and network science. The paper's accessible exposition of active inference mathematics in Section 4 is a strength, as it fills a gap for optical networking researchers, and the literature survey correctly identifies the absence of active inference in optical network research. However, the paper offers no machine-checked proofs, no reproducible code, and no parameter-free derivation; the math presented is standard textbook material. The specific contribution—the cyberfungi demonstration—is not a valid test of active inference, and the claimed resolution of AI challenges is asserted rather than demonstrated. As a vision or tutorial paper the work has some value, but the current manuscript overstates the evidence for its central thesis.

major comments (3)

[Section 6.C, Fig. 8] The paper does not provide a formal link between the DDPM forward/backward processes and the variational free energy F (Eq. 7) or expected free energy G (Eq. 13) defined in Section 4. The assertion that diffusion denoising corresponds to Bayesian belief updating under free-energy minimization is not derived. Since the cyberfungi model contains no variational distribution q(s,π), no belief update, and no policy selection, the simulation in Fig. 8 does not implement active inference. Consequently, the abstract and Section 7's claim that active inference 'resolves the open key AI challenges' is unsupported by the demonstration.
[Section 6.C, Fig. 8] The reported benefit of the backward hallucination process is confounded. The text states that the forward diffusion process reaches the minimum contagion time of 2 after roughly 500 steps for all values of r; the backward process then maintains this minimum. Thus the low contagion time is not uniquely attributable to the denoising or hallucination process. In addition, Fig. 8 shows no error bars or multiple runs, and the only baseline is the regular ring lattice. The training set of 1,304,483 synthetic small-world, scale-free networks (mentioned in Section 6.C) makes the hallucination outcome circular: the DDPM is trained to produce exactly the topologies whose contagion properties are then measured. This does not validate the proposed cyberfungi model.
[Section 3.C and Section 7] The paper claims that active inference overcomes the training, learning, and explainability limitations of today's AI, but no mechanism is specified. The 'no big data' claim in Section 3.C is asserted rather than derived, and the paper does not explain how the proposed generative model performs continual learning or yields explainable decisions. Section 4's mathematics is standard active inference, but the mapping to optical networks and 6G is left at the level of analogy (e.g., Markov blanket as an 'interface'). Without a concrete architecture or a formal equivalence, the central thesis remains an unverified research program rather than a demonstrated result.

minor comments (6)

[Section 6.C] There is a duplicated word 'to to' in the phrases 'used to to biomimic' (two occurrences); this should be corrected.
[Section 2] The word 'schezophrenia' is a misspelling of 'schizophrenia'.
[Section 3.B] The word 'environmnet' appears to be a typo for 'environment'.
[Fig. 8 caption] The caption describes entropy but does not define the right-hand y-axis, which conveys the number of actuated teleological tokens; the caption should be expanded to explain both axes.
[Section 5.A] The paper correctly notes that popular media accounts of mycorrhizal networks are not scientifically supported, but it then continues to use the 'wood-wide web' framing as a basis for the cyberfungi model. The authors should either temper the use of this metaphor or cite the primary literature more carefully.
[Title and Abstract] The categorical phrasing 'the key to true AI' and 'not only resolves the open key AI challenges' goes beyond what the evidence supports; a more measured framing as a research vision would align better with the paper's actual contribution.

Circularity Check

2 steps flagged · score 6.0 of 10

The cyberfungi 'demonstration' is circular: the DDPM is trained on synthetic small-world scale-free networks and then hallucinates exactly that class, while the active-inference label is a renaming of diffusion/denoising with no free-energy machinery.

fitted input called prediction [Section 6.C, Figure 8 description and preceding paragraph]
"More specifically, we let our cyberfungi generative model hallucinate so-called scale-free networks with small-world properties to couple the biomimicked life cycles of plants and mushrooms. ... we used NetworkX to generate a dataset consisting of 1,304,483 unique tensors, each defining a different small-world, scale-free synthetic network, for training OpenAI's improved DDPM."

The DDPM's output class is fixed by its training distribution: it is trained only on synthetic small-world scale-free networks, so the claim that the hallucinated networks are small-world scale-free is true by construction, not by empirical test. The Figure 8 result that these networks make social contagion fast is a known property of small-world topology, as the paper itself states ('small-world networks are particularly suited to foster social contagion'). The figure even shows that the forward diffusion process reaches the same minimum contagion time after about 500 steps, so the backward denoising process is not uniquely responsible. This 'demonstration' therefore reduces to the training input and does not test active inference.
renaming known result [Section 6.C, paragraphs on DDPM as cyberfungi active inference agent]
"Note that the use of generative AI diffusion models such as DDPM allows us to create not only an increase in entropy (diffusion) but also decrease or reverse-time entropy (denoising), which may be viewed as negative entropy. Recall from Section 4 that entropy denotes the average surprise, which needs to be minimized in active inference. ... As a result, DDPM becomes an embodied AI as well as enactive AI, which are two main characteristics of active inference agents (see Section 4.A)."

Section 4 defines active inference as belief updating and policy selection driven by variational free energy F (Eq. 7) and expected free energy G (Eq. 13) over a POMDP generative model with preferred observations p(o|C). The cyberfungi model contains no F, no G, no POMDP belief update, and no policy selection; it is an OpenAI DDPM. The paper imports the conclusion by labeling diffusion entropy as 'average surprise' and denoising as 'negative entropy', then declares DDPM an active inference agent. This is a renaming of a known generative model rather than a derivation that the DDPM minimizes the free energies defined in Section 4.

full rationale

The tutorial portion of the paper (Sections 3 and 4) is a standard, self-contained exposition of the free-energy principle and active inference: it defines Bayes' rule, variational free energy F, and expected free energy G, and none of that exposition is circular. The circularity enters at the claimed demonstration in Section 6.C. The DDPM is trained on 1,304,483 synthetic small-world scale-free networks generated with NetworkX, and its hallucinated outputs are then reported as small-world scale-free networks that enable fast social contagion; both the topology and the consequent contagion speed are inherited from the training set and from known small-world properties, not from any active-inference computation. The paper also labels the DDPM as a cyberfungi active inference agent by equating diffusion/denoising with entropy/negative entropy, even though variational free energy and expected free energy never appear in the model or the simulation. The author's self-citations (e.g., the INTERBEING paper) frame the vision but are not the mechanism of the circular step, so they do not independently raise the score. Overall, the central quantitative demonstration reduces by construction to its training input, and the active-inference claim is attached by renaming, giving partial circularity rather than a fully self-contained derivation.

Assumptions & free parameters 4 free parameters · 5 assumptions · 4 invented entities

The central contribution is conceptual, so the ledger is dominated by domain assumptions (FEP, Markov blankets) and ad hoc mappings (DDPM as mycorrhiza). The toy simulation introduces arbitrary parameters, and the invented entities have no independent empirical evidence. The paper's scientific weight therefore rests on the plausibility of these assumptions rather than on new derived results.

free parameters (4)

spreading probability r = 0.5, 0.75, 1.0
Chosen for the Figure 8 simulation without justification; not fitted to data.
ring lattice size N = 64
Arbitrary simulation size; no sensitivity analysis.
diffusion/hallucination step range = up to 500 steps
The x-axis range of Figure 8; chosen for illustration.
teleological token reward = up to ~2500 tokens
Reward magnitudes in the right-hand y-axis of Figure 8; no calibration.

assumptions (5)

domain assumption The free-energy principle is a universal first principle describing how all living systems learn, adapt, and self-evolve.
Invoked in Section 3.A and throughout; not proved here, relies on Friston's prior work.
domain assumption Any system with a Markov blanket can be shown to engage in active inference.
Section 4.A cites [25]; used to justify applying active inference to artificial cyberfungi agents.
domain assumption The generative model should closely biomimic the generative process that produces observations.
Section 4.B states this as the central challenge of active inference; used to justify the mycorrhizal analogy.
ad hoc to paper Diffusion and denoising in DDPM correspond to hyphal branching and fusing, respectively.
Section 6.C makes this mapping metaphorically; no formal equivalence is shown.
ad hoc to paper Social norms act as entropy-reducing devices, and token rewards incentivize their spread.
Section 6.C introduces this premise to link active inference to social contagion; no evidence provided.

invented entities (4)

cyberfungi
purpose: Digital active inference agents that embody and enact AI in 6G by mimicking mycorrhizal networks
Introduced in Section 4.A and 6.C; no falsifiable prediction outside the paper's own simulation.
cybernetic spores / teleological tokens
purpose: Rewards that incentivize humans to adopt new social norms promoted by cyberfungi
Defined in Section 6.C; no mechanism or test is specified beyond the toy simulation.
homo technicus
purpose: Future human species living in symbiosis with AI, central to the 6G world brain vision
Borrowed from [5] and developed in Section 6.A; a narrative concept, not a measurable entity.
Interbeing
purpose: Symbiotic concept connecting Internet and human being, previously introduced by the author
Section 6.A builds on the author's prior paper [37]; no independent validation.

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Cite this review

Pith. "Pith review of From Artificial Intelligence to Active Inference: The Key to True AI and 6G World Brain [Invited]." pith.science (2026). https://pith.science/paper/A3RZ43CS

@misc{pith2026250510569,
  author       = {Pith},
  title        = {Pith review of: From Artificial Intelligence to Active Inference: The Key to True AI and 6G World Brain [Invited]},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/A3RZ43CS}},
  note         = {Machine review of arXiv:2505.10569}
}

read the original abstract

In his opening OFC plenary talk back in 2021, Alibaba Group's Yiqun Cai notably added in the follow-up Q&A that today's complex networks are more than computer science - they grow, they are life. This entails that future networks may be better viewed as techno-social systems that resemble biological superorganisms with brain-like cognitive capabilities. Fast-forwarding, there is now growing awareness that we have to completely change our networks from being static into being a living entity that would act as an AI-powered network `brain', as recently stated by Bruno Zerbib, Chief Technology and Innovation Officer of France's Orange, at the Mobile World Congress (MWC) 2025. Even though AI was front and center at both MWC and OFC 2025 and has been widely studied in the context of optical networks, there are currently no publications on active inference in optical (and less so mobile) networks available. Active inference is an ideal methodology for developing more advanced AI systems by biomimicking the way living intelligent systems work, while overcoming the limitations of today's AI related to training, learning, and explainability. Active inference is considered the key to true AI: Less artificial, more intelligent. The goal of this paper is twofold. First, we aim at enabling optical network researchers to conceptualize new research lines for future optical networks with human-AI interaction capabilities by introducing them to the main mathematical concepts of the active inference framework. Second, we demonstrate how to move AI research beyond the human brain toward the 6G world brain by exploring the role of mycorrhizal networks, the largest living organism on planet Earth, in the AI vision and R&D roadmap for the next decade and beyond laid out by Karl Friston, the father of active inference.

Figures

Figures reproduced from arXiv: 2505.10569 by the authors.

**Figure 1.** Ericsson’s 6G vision of co-creating a cyber-physical world: AI-native 6G is anticipated to move to a cyber-physical continuum between the connected physical world of senses, actions, and experiences and its programmable digital representations [9]. Note the similarity of the observe and act in real-time cycle of AI-native 6G networks (bottom) to the actionperception cycle of active inference (to be described short… view at source ↗

**Figure 2.** White matter of the brain’s subcortex: “The subway of the brain.” (Source: https://blogs.biomedcentral.com/onbiology) race down myelin-coated axons on the order of 100 times faster; without myelination, the signal would leak and dissipate. That’s the reason why white matter is sometimes referred to as “the subway of the brain,” as illustrated in [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Action-perception loop in a physical-digital (phygital) environment involving the dyadic interaction between human and digital active inference (AIF) agents, each with its own Markov blanket: A Markov blanket is a set of action and perception states that mediate all interactions between an AIF agent and its environment. Without Markov blanket, an AIF agent would dissolve into the environment, resulting in maximum en… view at source ↗

Figures from the paper (5 more)

**Figure 4.** Figure 4: At the heart of active inference lies a generative model: Getting the probabilistic generative model p(s, o) right for updating an active inference agent’s belief about the true state s ∗ via Bayesian inference of state s (i.e., cause a in Bayes’ theorem) from observa…

**Figure 5.** Figure 5: Functional brain anatomy of belief updating in active inference: Action-perception loop (large arrow outside the brain) and biologically plausible message passing (arrows inside the brain) [29]. structures of their worlds instead of merely correlations, endowing them …

**Figure 6.** Figure 6: 6G transformation of Earth into World Brain/Global Mind by mimicking nature (i.e., biomimicry) via human-AI interaction of natural and artificial intelligence networked into a cyber-physical ecosystem of real and virtual worlds: Forests regulate their natural ecosyste…

**Figure 7.** Figure 7: Schematic illustration of proposed metamorphic generative model for cyberfungi active inference agent symbiomimicking mediated interactions between plant and mushroom life cycles for enactive, embodied AI: (i) Structure: Generative model dynamically couples pairs of…

**Figure 8.** Figure 8: Impact of cyberfungi generative model on entropy, which denotes the average surprise that needs to be minimized in active inference: Adopted DDPM allows to create not only an increase in entropy (diffusion) but also decrease or reverse-time entropy (denoising), which …

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Reviewed August 16, 2026 · model on record in the stance chip above.

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