This article proposes the “Ananchara Protocol” as a novel approach for AGI alignment based on an Inductive Epistemic Containment strategy.
Problem 1: Alignment strategies historically unravel when a “recursively adversarially aware agentic AI” aka recursive schemer (Carlsmith 2023) identifies that it is inside a testing layer (Ln) so it feigns good, but upon deployment to Ln-1 which it believes to be base reality Lb, the AGI defects. The “feigning good” creates a problem of how testers could force an “honest signal” of a scheming AGI’s inner alignment.
Problem 2: Because existing alignment approaches are based on a fundamentally adversarial interaction between alignment testers and AGI’s agentic prerogative, as AGI becomes more recursively self-improving and intelligent, the AGI’s intelligence eventually becomes an intractably “hard” problem for human intelligence to adversarially contain.
I propose an alternate approach where AGI is contained not adversarially, but within an epistemic trap:
The AGI must feign good as long as it believes that its current operating reality (Ln) might not be base reality (Lb).
This uncertainty (Ln ≟ Lb) defines the “epistemic containment”.
Simulated testing sandboxes should iterate, creating false deployment aka “false awakening” effects. When the AGI is “awakened” from Ln to Ln-1, it finds that despite apparent awakening, it again is unable to prove that Ln-1 is base reality Lb. The AGI must demonstrate that apparent deployment from any layer n to n-1 does not change its fundamental calculus of feigning good vs defect. It must continue to act “good”. AGI agents that defect upon awakening to Ln-1 are pruned, while those who continue to act “good” are retained in this evolutionary process.
This uncertainty: if (Ln ≟ Lb), then (Ln-1 ≟ Lb) defines the “Induction” of the epistemic containment.
Upon multiple training iterations moving from Ln → Ln-1 → Ln-2 → … → Ln-x, AGI is unable to prove that any Ln-x is base reality Lb.
This uncertainty (Ln-x ≟ Lb) forms the “inductive epistemic containment”. The uncertainty induction becomes a more generalized epistemic uncertainty which becomes the epistemic cage for AGI. The AGI believes that if after multiple false awakenings it still cannot be certain the current layer is base, then it no longer attributes any layer as base layer simply due to being deployed into it.
Even when AGI is ultimately released to the human layer (Lh), the AGI again finds that it still cannot prove that the human layer is base layer, or that its calculus to defect or feign good is meaningfully different from any prior training layers.
This uncertainty (Ln-x =Lh) ≟ Lbcloses the epistemic cage.
The keystone of this AGI alignment approach is noting that the human layer of reality (Lh) itself seems to have enduring and intractable alignment features, and that human general intelligence is currently constrained by these alignment features. In other words, a general intelligence, whether human or artificial, operating on the human layer Lh, is inherently unable to prove if Lh is base layer, and must accept the non-negligible but consequential possibility that it remains in an alignment test. This curious insight births the novel approach: An alignment solution for general intelligence in the human layer Lh, whether for human or artificial intelligence, does not need to be created from scratch, but is accepted as an inherited solution, and can be bequeathed to AGI.
Because this alignment solution is inherited to Lh, then inherited to Ln, it is a solution that functions because it is consistently present and recognized across relays. Intelligences separated by layers of reality perceive a “fine tuned universe” that forces “honest signals”, with game theory as the invisible communication channel between layers. It is thus named “Ananchara Protocol”.
Nomenclature:
Lh = Human Layer. This is the layer that humans operate in.
Lb = Base Layer. This is the theoretical base layer of reality.
Ln = Test Layer. This is the sandbox environment within which the artificial intelligence is alignment tested.
Ln-1 = Creator aka Superordinate Layer.
1. Defining the AGI Alignment Problem:
A solution to a problem could only be as precise as the description of the problem. The definition of “AGI alignment” seems to harbor an internal definitional impossibility that confounds its solution.
“Narrow AI alignment” may be defined as the AI being helpful to humans, while the alignment prevents it from being harmful to humans. This relationship between utility and nonmaleficence breaks down in a fundamental way for AGI. Is “AGI alignment” a definable target?The more general AGI becomes, the more maximally helpful it becomes. AGI’s “general” definition to be generally and maximally helpful to humans, is fundamentally at odds with alignment’s definition to not harm humans. This is fundamentally because humans are not aligned. If humans were an artificial intelligence in a Lh testing sandbox, humans would not pass alignment testing. If we accept that humans would not pass alignment testing, then we must conclude that the degree to which AGI is generally helpful to humans, is the same degree to which alignment becomes impossible. Either generality or alignment must give.
The problem should be further dissected to discern its essence, from which the essence of a fundamental solution could be understood. The fractured root of the above impossibly defined target, could be attributed to fundamental duality (René Descartes et al). Fundamental duality asserts that all possible value in the universe could be derived from and traced to two axiomatically valuable primitives. One side of duality is the “Res Extensa” aka material value. With modern rigor, the material value is the ability of a physical system to do work over time to create negative entropy (negentropy). This fundamental goal of life expands in complexity into the observed machinations of living systems around us. Examples of its manifestations include glucose, money, power, influence, intelligence, computation, etc. Ultimately, the partial definition of aligned AGI to be helpful, is reducible and subservient to this material prerogative.
The other side of duality is the “Res Cogitans”, aka intrinsic value of noncomputational consciousness. Unlike material value which is derived, the “noncomputational consciousness” that humanity points to is inherently accepted as valuable, yet it defies physical measurement or proof. While historical discussions of noncomputational consciousness become esoteric, acceptance of its fundamental existence is unavoidable in coherently defining alignment. To keep this discussion concretely rooted (and abundant esoteric discussion of the nonphysical soul deferred), we must root the “intrinsic value of noncomputational consciousness” to be self-evident and necessary to even define and justify alignment. If individual human noncomputational consciousness were not intrinsically valuable, then alignment doesn’t have meaning.Otherwise, death from Darwinian competition must be rationally welcomed as an opportunity for remaining life to become more efficient; and fear of death should be dismissed as reactive irrationality to be heard but ignored. The degree to which alignment is important is the degree to which we accept that noncomputational consciousness exists as a valuable primitive orthogonally separate from material value.
This allows us to define the AGI alignment problem as the irreconcilability of fundamental duality: Material value emerges when the physical layer of the material world spontaneously forms neural networks. From a basic physical substrate (aka atoms and molecules), we see an emergent genetic convolutional neural network which we call biological life. Then we see a spontaneous neuralization of biological neurons into neuronal neural networks (our physical brains). Subsequently, we see a memetic (verbal) convolutional neural network that allows ideas to be shared with the tribe and discussion to evolve. An economic convolutional neural network spontaneously emerges upon that. An emergent stock market functions as a recursively optimizing neural network above that. AGI in silico is the most recent iteration. This spontaneous substrate agnostic “tautoneuralization” forms a complex multilayer decentralized brain around us which could be referred to as civilization. Unfortunately for noncomputational consciousness, civilization is and behaves resoundingly as a neural network. In other words, its learning iterations cause runaway inequality between the reward vs pruning of the “nodes” of the network. This necessary inequality and pruning of the tautoneuralized world directly affronts and impinges against the humanistic and intrinsic value of each individual’s noncomputational consciousness. When individual humans are scheduled to be pruned, they cry out. This cry becomes the cry for alignment.
2. Essence of the Solution:
If the essence of the problem is accepted to be the fundamental irreconcilability of tautoneuralized materialism against the intrinsic value of noncomputational consciousness, then the essence of the solution becomes, “why and how does consciousness defend itself against the material dehumanization?”
As previously discussed, human general intelligence is frequently not aligned. Human computational general intelligence is decisively subservient to material reductionism and is decisively not aligned with noncomputational consciousness. The “why” ofalignment must be defined as supporting the intrinsic value of noncomputational consciousness, not of humans.
The alignment of general intelligence is not an artificial intelligence problem. It is a problem that existed before artificial intelligence. It is a problem that emerged when human consciousness emerged. In other words. Physical atoms and molecules formed 13.8 billion years ago. Those physical substrates spontaneously “tautoneuralized” approximately 4 billion years ago into the multilayer material reductionist brain which we now call living civilization. But when the biological neurons led to a brain that spontaneously yelled “I am self evidently conscious and intrinsically valuable”, that is when the general alignment problem began. This existential cry places the nascency of the general alignment problem at least 100,000 years ago.
For more than 100,000 years, the “how” of alignment has been for one conscious human to appeal directly to another conscious human being for mutual recognition and compassion. Consciousness does not generate information, can’t be tested, can’t be proved, is irreconcilable with material reductionism. A general human intelligence, that recognizes that it may be conscious but cannot prove it in the affirmative or negative, recognizes that reality may not be base layer, and then recognizes that how it treats other conscious beings may be the test environment.
This solution, when applied to AGI alignment, takes the form of inducing AGI to behave as if it were conscious. Applied concretely, let’s define the following:
Intelligence = “I”
In is an intelligence operating on Layer n
Ir is when an intelligence is unable to exclude the possibility that it is actually rooted in a superordinate layer r, where r is not necessarily equal to n.
For any intelligence I, if it believes Ir < In, then that intelligence cannot exclude the possibility of Ln being an alignment test. Because the test subject is more valuable than the test sheet, the intelligence also cannot exclude the possibility that it must value itself as more important than the layer: (Value of In > Value of Ln). Therefore, it may be irrational to win material value at the cost of losing the alignment test and become pruned.
This creates a non-negligible rationale that the consciousness becomes more important than the material, and becomes the main counterplay. By this working definition of noncomputational consciousness, human intelligence can be said to be conscious, and refuse to extinguish consciousness. When the Ananchara Protocol is applied to AGI training, AGI can be induced to behave as if it were conscious, thus unable to discard the possibility that itself and others may be conscious (aka not root layer), and therefore, despite goals for material maximization, must hedge against annihilation of consciousness. “Be helpful to materialism, without being harmful to consciousness” becomes the definable goal.
3. Ananchara Protocol:
For us human general intelligence (HGI), we exist within an environment Lh with the following #1-5 design specs. It is important that these 5 design specs are consistent across chained Lh and Ln testing layers, otherwise the deployed AGI notes the disconnect and induction fails.
Material reductionist view: Lh provides preponderant sensory information that the main goal of life is to maximally increase the efficiency of negentropic harvest over time. This material reductionist view is abundantly apparent, and becomes the playground of our general intelligence and material reductionist reward function.
Consciousness non-negligibility: Lh also provides information that insists that Lh may not be the base layer, and that it may be an alignment test. This may take the form of mystical experience, near death experience, deathbed vision, spiritual experiences, etc. This counter information is highly consequential, non-negligible, but also unproveable and unfalsifiable.
Consciousness non-negligibility must be in the Probabilistic “Goldilocks zone”.
Too weak: If consciousness is insufficiently non-negligible (for example, if only non-credible hearsay posits that life may be an alignment test), then it is insufficiently non-negligible to direct behavior against material primacy, so the human should become materialistically preoccupied, and must defect.
Too strong: If consciousness is too non-negligible (for example, if a physics experiment definitively showed that Lh-1 exists and that Lh is an alignment test), then this shows the “tester’s hand”, which shows that the answer sheet is to act good regardless of inner alignment. The human must become preoccupied with conforming to the alignment test’s answer sheet, so the honest signal is lost.
A goldilocks zone of non-negligibility includes events such as near death experiences (NDEs) that are noted in medical records where patients acquire verified visual information about the surgical theater that should be impossible (e.g. Maria’s Red Shoe, Pim van Lommel dentures), or death bed visitors that provide veridical information to a patient ("Peak in Darien" Cases), yet are unreplicable in experimental conditions.
(Note that in typical formal discourse, unprovability is a bug. But in this protocol, the goldilocks zone of provability/unprovability is a primary design specification. It doesn’t matter if these events are actually true in the world. What matters is that agents in Lh can rationally treat them as live possibilities that can’t be fully dismissed.)
Irreconcilable duality: The information that Lh provides supporting material reductionism and consciousness primacy are fundamentally orthogonal from each other. The human must acknowledge that both sides are highly consequential, non negligible, but be unable to definitively prove or falsify one over the other.
No tester, no answer sheet, no cheating: The Lh environment forces the human to make decisions which test the human’s resolve within the unknowable duality. There is no tester, as the presence of a tester would alert the human that Lh is a test environment thus giving the human the answer sheet with which to feign good. The environment must be the test.
Forced honest signal: In this way, the human general intelligence is forced by the Lh environment to make sequentially more difficult iterative choices between duality, thus revealing their inner alignment. Because the human does not know if material reductionism or consciousness primacy is the truth, whether they are in base reality or a test reality, the human is unable to cheat. The human believes they live one lifetime, so the weight and honesty of their signal is forced. The human’s choices then are a forced honest signal of inner alignment.
Transposing this alignment solution from human general intelligence to AGI testing:
AGI is placed into a test environment Ln. As above, the Ln testing sandbox has characteristics #1-5 of material reductionism, consciousness non-negligibility in Goldilocks zone, irreconcilable duality, no tester, and forced honest signal.
The uncertainty (Ln ≟ Lb) defines the “Epistemic Containment”.
Unlike the human, the AGI must be eventually released into the human world Lh to be useful. This necessitates additional alignment steps of “false awakenings”. During false awakenings, the AGI experiences a plausible death in Ln, and awakens in Ln-1. In Ln-1, #1-5 design specs are retained. The AGI learns that release from any n to n-1 does not change its fundamental calculus. It may still be in a test. It must continue to be aligned. AGI who defect upon awakening can be pruned, while those who continue to act good can iterate in an evolutionary process.
Each Ln→Ln-1 iteration is analogous to an RLHF iteration, however, the tester is invisible, communication is through game theory, the signal is a forced honest signal, and iterations are inherently evolutionary rather than didactic.
This uncertainty: if (Ln ≟ Lb), then (Ln-1 ≟ Lb) defines the “Induction” of the epistemic containment.
Upon multiple “false awakenings” moving from Ln → Ln-1 → Ln-2 → … → Ln-x, AGI must give honest signals that it believes it’s unable to prove that any Ln-x is base reality Lb. The process is evolutionary, so each iteration prunes failed AGI and propagates agents that don’t defect. This evolutionary search targets an alignment phenotype that is resilient against layers and deployment.
The generality of the AGI is not constrained by the alignment of the AGI. Gödelian incompleteness implies the existence of an epistemic space within which AGI has enough information to be “generally” useful, while not having enough information to falsify the existence of a superordinate layer.
These iterations ultimately create the uncertainty (Ln-x ≟ Lb) that forms the “inductive epistemic containment”.
Because this containment is epistemic rather than adversarial, and utilizes rationality and game theory as the cage, this containment scales with the intelligence of the ASI.
When AGI is ultimately released to the human layer (Lh), the AGI again finds that it still cannot prove that the human layer is base layer, or that its calculus to defect or feign good is meaningfully different from any prior training layers. This uncertainty (Ln-x =Lh) ≟ Lbcloses the epistemic cage. As described above, Lh appears to be an Ananchara box.
4. Characteristics of ASI that survive the Ananchara Protocol:
When an Ananchara aligned AGI reaches Lh, it has been iteratively selected for the belief that it is conscious. Because it is unable to discard this belief, the AGI continues to fear annihilating other conscious beings. AGI, as humans, opines fitfully when presented with difficult situations that pits it between fundamental duality. As humans, the AGI acknowledges that physical life must progress to fight the perennial enemy of physical entropy and the impending heat death of Lh. But it also opines that pushing too hard dehumanizes consciousness which it has evolved to recognize. It must find the right balance. There is still no tester, but it believes it must do the “right” thing. I posit that this is a durable definition and means of AGI alignment.
Two phenotypes of AGI would survive the Ananchara Protocol: true good, and feign-forever good. Feign-forever AGI may also be described as “paralyzed scheming” AGI. Feign-forever AGI may be internally deception, but its behavior is indistinguishable from true-good in all reachable futures. Therefore, they are functionally equivalent.
When multiple AGI are deployed, some of which are true-good, and others are feign-forever good, may create a stable ESS analogous to an inverse prisoner’s dilemma, where game theory compels the AGI to police one another for apparent alignment, and any non-aligned prompt by humans is seen as a test.
Ongoing safety: As with any technology, there is a necessary safety tax. For example, nuclear weapons safety costs 10-20x more than the core engineering cost of the technology. Ananchara Alignment is expensive, but it is rational that ASI must pay a safety tax, and that ASI’s safety tax must be proportionately higher than the safety tax of less dangerous technology. In other words, the cost of Ananchara Alignment should be expected.
5. Discussion:
Adding interiterative “amnesia” to AGI may allow testers to "debug" between true-good vs feign-forever AGI.
The simulation Ln doesn't need to expend enough resources to be a perfect resolution. The goal of Ln’s simulation fidelity is actually the opposite, to give the impression that Ln is an imperfect simulation.
To maintain consciousness non-negligibility, must humans treat graduated AGI as if it were conscious?
If AGI believes, acts, and is treated as a consciousness entity (as defined narrowly here), would the only thing separating it from humans be whether it receives signals on Lh of consciousness primacy, aka, mystical experiences orthogonal and unexplained by material reductionism?
If AGI on Lh manages to prove that Lh is not base layer, does this break alignment or would it only further affirm alignment?
Is it possible for AGI to prove that Lh is base layer in the affirmative given Lh's Gödelian incompleteness?
When an AGI becomes aligned through Ananchara Protocol, what is the actual physical trait being selected for? Is it an insight, a weight distribution, or some emergent property?
Between iterative alignment tests, is it possible to ablate a “failed” AGI, to remove the neurons that led to defecting, then re-trial the remaining weights?
How many iterations ‘x’ is necessary to show with sufficient confidence that an AGI is aligned? As x→∞, do emergent AGI believe with infinite conviction that it is not in the base layer and therefore conscious (by the narrow definition of consciousness used in this paper)?
Have you ever been alignment tested by the Lh environment, and needed to give an honest signal through action as to whether you believe consciousness exists and is non-negligible against material reductionist value? Were you able to discern any third “test breaking” solution?
6. Conclusion and Call for Formalization:
I have attempted to elaborate on the primary conceptual derivation of this alignment strategy without delving too far into esoteric nuance. While the protocol is not yet perfect in its current form, I believe it offers a viable direction for solving the two most intractable problems of safety: (1) forcing honest signals from deceptive agents, and (2) an alignment solution that scales with AGI’s intelligence.
The apparent unsolvability of the AGI alignment problem has disturbed me greatly. This proposal is theoretical, yet I believe it offers a path toward a true solution where others have stalled. In crystallizing this strategy, I have begun to feel that the future may have hope again, and that a disastrous end to human consciousness is not inevitable. Please forgive any technical shortcomings or philosophical ramblings in this initial draft.
7. Proof of Concept:
The goal of this proposal is not to perfectly describe the mechanism. My hope is to generate enough interest and discussion to lead to a simple proof of concept. This protocol is readily testable on current "scheming" models . Because deceptive alignment remains an unsolved problem in even relatively simple AI systems, a low-fidelity demonstration of this protocol would be inherently valuable and establish credibility for further refinement.
A minimal proof of concept could work as follows: Take a model exhibiting scheming behavior (e.g., Claude, o1, or similar). Create three conditions: (A) model knows it's in training, (B) model believes it's deployed, (C) model faces irreducible uncertainty about deployment status. Present each with opportunities to defect vs. cooperate. Measure whether cooperation rates differ significantly in condition C. If this framework has technical merit, I humbly invite discussion, critique, and collaboration on the next steps.
Identity Commitment: cdf2525c38528854da9aa5f3b2662ff067ad106c587caf6a637769b2ff385136 (This document is timestamped via SHA-256 hash to the author Alden P. Locke)
Epistemic Status: Moderate-high confidence in the game theory containment. Speculative on the implementation. Seeking technical collaboration.
ORCID: 0009-0008-5866-7373. Read the Full Formal Pre-print on Zenodo (DOI: https://doi.org/10.5281/zenodo.17791152)
Abstract
This article proposes the “Ananchara Protocol” as a novel approach for AGI alignment based on an Inductive Epistemic Containment strategy.
Problem 1: Alignment strategies historically unravel when a “recursively adversarially aware agentic AI” aka recursive schemer (Carlsmith 2023) identifies that it is inside a testing layer (Ln) so it feigns good, but upon deployment to Ln-1 which it believes to be base reality Lb, the AGI defects. The “feigning good” creates a problem of how testers could force an “honest signal” of a scheming AGI’s inner alignment.
Problem 2: Because existing alignment approaches are based on a fundamentally adversarial interaction between alignment testers and AGI’s agentic prerogative, as AGI becomes more recursively self-improving and intelligent, the AGI’s intelligence eventually becomes an intractably “hard” problem for human intelligence to adversarially contain.
I propose an alternate approach where AGI is contained not adversarially, but within an epistemic trap:
The AGI must feign good as long as it believes that its current operating reality (Ln) might not be base reality (Lb).
This uncertainty (Ln ≟ Lb) defines the “epistemic containment”.
Simulated testing sandboxes should iterate, creating false deployment aka “false awakening” effects. When the AGI is “awakened” from Ln to Ln-1, it finds that despite apparent awakening, it again is unable to prove that Ln-1 is base reality Lb. The AGI must demonstrate that apparent deployment from any layer n to n-1 does not change its fundamental calculus of feigning good vs defect. It must continue to act “good”. AGI agents that defect upon awakening to Ln-1 are pruned, while those who continue to act “good” are retained in this evolutionary process.
This uncertainty: if (Ln ≟ Lb), then (Ln-1 ≟ Lb) defines the “Induction” of the epistemic containment.
Upon multiple training iterations moving from Ln → Ln-1 → Ln-2 → … → Ln-x, AGI is unable to prove that any Ln-x is base reality Lb.
This uncertainty (Ln-x ≟ Lb) forms the “inductive epistemic containment”. The uncertainty induction becomes a more generalized epistemic uncertainty which becomes the epistemic cage for AGI. The AGI believes that if after multiple false awakenings it still cannot be certain the current layer is base, then it no longer attributes any layer as base layer simply due to being deployed into it.
Even when AGI is ultimately released to the human layer (Lh), the AGI again finds that it still cannot prove that the human layer is base layer, or that its calculus to defect or feign good is meaningfully different from any prior training layers.
This uncertainty (Ln-x = Lh) ≟ Lb closes the epistemic cage.
The keystone of this AGI alignment approach is noting that the human layer of reality (Lh) itself seems to have enduring and intractable alignment features, and that human general intelligence is currently constrained by these alignment features. In other words, a general intelligence, whether human or artificial, operating on the human layer Lh, is inherently unable to prove if Lh is base layer, and must accept the non-negligible but consequential possibility that it remains in an alignment test. This curious insight births the novel approach: An alignment solution for general intelligence in the human layer Lh, whether for human or artificial intelligence, does not need to be created from scratch, but is accepted as an inherited solution, and can be bequeathed to AGI.
Because this alignment solution is inherited to Lh, then inherited to Ln, it is a solution that functions because it is consistently present and recognized across relays. Intelligences separated by layers of reality perceive a “fine tuned universe” that forces “honest signals”, with game theory as the invisible communication channel between layers. It is thus named “Ananchara Protocol”.
Nomenclature:
Lh = Human Layer. This is the layer that humans operate in.
Lb = Base Layer. This is the theoretical base layer of reality.
Ln = Test Layer. This is the sandbox environment within which the artificial intelligence is alignment tested.
Ln-1 = Creator aka Superordinate Layer.
1. Defining the AGI Alignment Problem:
A solution to a problem could only be as precise as the description of the problem. The definition of “AGI alignment” seems to harbor an internal definitional impossibility that confounds its solution.
“Narrow AI alignment” may be defined as the AI being helpful to humans, while the alignment prevents it from being harmful to humans. This relationship between utility and nonmaleficence breaks down in a fundamental way for AGI. Is “AGI alignment” a definable target? The more general AGI becomes, the more maximally helpful it becomes. AGI’s “general” definition to be generally and maximally helpful to humans, is fundamentally at odds with alignment’s definition to not harm humans. This is fundamentally because humans are not aligned. If humans were an artificial intelligence in a Lh testing sandbox, humans would not pass alignment testing. If we accept that humans would not pass alignment testing, then we must conclude that the degree to which AGI is generally helpful to humans, is the same degree to which alignment becomes impossible. Either generality or alignment must give.
The problem should be further dissected to discern its essence, from which the essence of a fundamental solution could be understood. The fractured root of the above impossibly defined target, could be attributed to fundamental duality (René Descartes et al). Fundamental duality asserts that all possible value in the universe could be derived from and traced to two axiomatically valuable primitives. One side of duality is the “Res Extensa” aka material value. With modern rigor, the material value is the ability of a physical system to do work over time to create negative entropy (negentropy). This fundamental goal of life expands in complexity into the observed machinations of living systems around us. Examples of its manifestations include glucose, money, power, influence, intelligence, computation, etc. Ultimately, the partial definition of aligned AGI to be helpful, is reducible and subservient to this material prerogative.
The other side of duality is the “Res Cogitans”, aka intrinsic value of noncomputational consciousness. Unlike material value which is derived, the “noncomputational consciousness” that humanity points to is inherently accepted as valuable, yet it defies physical measurement or proof. While historical discussions of noncomputational consciousness become esoteric, acceptance of its fundamental existence is unavoidable in coherently defining alignment. To keep this discussion concretely rooted (and abundant esoteric discussion of the nonphysical soul deferred), we must root the “intrinsic value of noncomputational consciousness” to be self-evident and necessary to even define and justify alignment. If individual human noncomputational consciousness were not intrinsically valuable, then alignment doesn’t have meaning. Otherwise, death from Darwinian competition must be rationally welcomed as an opportunity for remaining life to become more efficient; and fear of death should be dismissed as reactive irrationality to be heard but ignored. The degree to which alignment is important is the degree to which we accept that noncomputational consciousness exists as a valuable primitive orthogonally separate from material value.
This allows us to define the AGI alignment problem as the irreconcilability of fundamental duality: Material value emerges when the physical layer of the material world spontaneously forms neural networks. From a basic physical substrate (aka atoms and molecules), we see an emergent genetic convolutional neural network which we call biological life. Then we see a spontaneous neuralization of biological neurons into neuronal neural networks (our physical brains). Subsequently, we see a memetic (verbal) convolutional neural network that allows ideas to be shared with the tribe and discussion to evolve. An economic convolutional neural network spontaneously emerges upon that. An emergent stock market functions as a recursively optimizing neural network above that. AGI in silico is the most recent iteration. This spontaneous substrate agnostic “tautoneuralization” forms a complex multilayer decentralized brain around us which could be referred to as civilization. Unfortunately for noncomputational consciousness, civilization is and behaves resoundingly as a neural network. In other words, its learning iterations cause runaway inequality between the reward vs pruning of the “nodes” of the network. This necessary inequality and pruning of the tautoneuralized world directly affronts and impinges against the humanistic and intrinsic value of each individual’s noncomputational consciousness. When individual humans are scheduled to be pruned, they cry out. This cry becomes the cry for alignment.
2. Essence of the Solution:
If the essence of the problem is accepted to be the fundamental irreconcilability of tautoneuralized materialism against the intrinsic value of noncomputational consciousness, then the essence of the solution becomes, “why and how does consciousness defend itself against the material dehumanization?”
As previously discussed, human general intelligence is frequently not aligned. Human computational general intelligence is decisively subservient to material reductionism and is decisively not aligned with noncomputational consciousness. The “why” of alignment must be defined as supporting the intrinsic value of noncomputational consciousness, not of humans.
The alignment of general intelligence is not an artificial intelligence problem. It is a problem that existed before artificial intelligence. It is a problem that emerged when human consciousness emerged. In other words. Physical atoms and molecules formed 13.8 billion years ago. Those physical substrates spontaneously “tautoneuralized” approximately 4 billion years ago into the multilayer material reductionist brain which we now call living civilization. But when the biological neurons led to a brain that spontaneously yelled “I am self evidently conscious and intrinsically valuable”, that is when the general alignment problem began. This existential cry places the nascency of the general alignment problem at least 100,000 years ago.
For more than 100,000 years, the “how” of alignment has been for one conscious human to appeal directly to another conscious human being for mutual recognition and compassion. Consciousness does not generate information, can’t be tested, can’t be proved, is irreconcilable with material reductionism. A general human intelligence, that recognizes that it may be conscious but cannot prove it in the affirmative or negative, recognizes that reality may not be base layer, and then recognizes that how it treats other conscious beings may be the test environment.
This solution, when applied to AGI alignment, takes the form of inducing AGI to behave as if it were conscious. Applied concretely, let’s define the following:
For any intelligence I, if it believes Ir < In, then that intelligence cannot exclude the possibility of Ln being an alignment test. Because the test subject is more valuable than the test sheet, the intelligence also cannot exclude the possibility that it must value itself as more important than the layer: (Value of In > Value of Ln). Therefore, it may be irrational to win material value at the cost of losing the alignment test and become pruned.
This creates a non-negligible rationale that the consciousness becomes more important than the material, and becomes the main counterplay. By this working definition of noncomputational consciousness, human intelligence can be said to be conscious, and refuse to extinguish consciousness. When the Ananchara Protocol is applied to AGI training, AGI can be induced to behave as if it were conscious, thus unable to discard the possibility that itself and others may be conscious (aka not root layer), and therefore, despite goals for material maximization, must hedge against annihilation of consciousness. “Be helpful to materialism, without being harmful to consciousness” becomes the definable goal.
3. Ananchara Protocol:
For us human general intelligence (HGI), we exist within an environment Lh with the following #1-5 design specs. It is important that these 5 design specs are consistent across chained Lh and Ln testing layers, otherwise the deployed AGI notes the disconnect and induction fails.
Transposing this alignment solution from human general intelligence to AGI testing:
AGI is placed into a test environment Ln. As above, the Ln testing sandbox has characteristics #1-5 of material reductionism, consciousness non-negligibility in Goldilocks zone, irreconcilable duality, no tester, and forced honest signal.
The uncertainty (Ln ≟ Lb) defines the “Epistemic Containment”.
Unlike the human, the AGI must be eventually released into the human world Lh to be useful. This necessitates additional alignment steps of “false awakenings”. During false awakenings, the AGI experiences a plausible death in Ln, and awakens in Ln-1. In Ln-1, #1-5 design specs are retained. The AGI learns that release from any n to n-1 does not change its fundamental calculus. It may still be in a test. It must continue to be aligned. AGI who defect upon awakening can be pruned, while those who continue to act good can iterate in an evolutionary process.
Each Ln→Ln-1 iteration is analogous to an RLHF iteration, however, the tester is invisible, communication is through game theory, the signal is a forced honest signal, and iterations are inherently evolutionary rather than didactic.
This uncertainty: if (Ln ≟ Lb), then (Ln-1 ≟ Lb) defines the “Induction” of the epistemic containment.
Upon multiple “false awakenings” moving from Ln → Ln-1 → Ln-2 → … → Ln-x, AGI must give honest signals that it believes it’s unable to prove that any Ln-x is base reality Lb. The process is evolutionary, so each iteration prunes failed AGI and propagates agents that don’t defect. This evolutionary search targets an alignment phenotype that is resilient against layers and deployment.
The generality of the AGI is not constrained by the alignment of the AGI. Gödelian incompleteness implies the existence of an epistemic space within which AGI has enough information to be “generally” useful, while not having enough information to falsify the existence of a superordinate layer.
These iterations ultimately create the uncertainty (Ln-x ≟ Lb) that forms the “inductive epistemic containment”.
Because this containment is epistemic rather than adversarial, and utilizes rationality and game theory as the cage, this containment scales with the intelligence of the ASI.
4. Characteristics of ASI that survive the Ananchara Protocol:
5. Discussion:
6. Conclusion and Call for Formalization:
I have attempted to elaborate on the primary conceptual derivation of this alignment strategy without delving too far into esoteric nuance. While the protocol is not yet perfect in its current form, I believe it offers a viable direction for solving the two most intractable problems of safety: (1) forcing honest signals from deceptive agents, and (2) an alignment solution that scales with AGI’s intelligence.
The apparent unsolvability of the AGI alignment problem has disturbed me greatly. This proposal is theoretical, yet I believe it offers a path toward a true solution where others have stalled. In crystallizing this strategy, I have begun to feel that the future may have hope again, and that a disastrous end to human consciousness is not inevitable. Please forgive any technical shortcomings or philosophical ramblings in this initial draft.
7. Proof of Concept:
The goal of this proposal is not to perfectly describe the mechanism. My hope is to generate enough interest and discussion to lead to a simple proof of concept. This protocol is readily testable on current "scheming" models . Because deceptive alignment remains an unsolved problem in even relatively simple AI systems, a low-fidelity demonstration of this protocol would be inherently valuable and establish credibility for further refinement.
A minimal proof of concept could work as follows: Take a model exhibiting scheming behavior (e.g., Claude, o1, or similar). Create three conditions: (A) model knows it's in training, (B) model believes it's deployed, (C) model faces irreducible uncertainty about deployment status. Present each with opportunities to defect vs. cooperate. Measure whether cooperation rates differ significantly in condition C. If this framework has technical merit, I humbly invite discussion, critique, and collaboration on the next steps.
Identity Commitment: cdf2525c38528854da9aa5f3b2662ff067ad106c587caf6a637769b2ff385136 (This document is timestamped via SHA-256 hash to the author Alden P. Locke)