This is an automated rejection. No LLM generated, heavily assisted/co-written, or otherwise reliant work.
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Body:
I think I might have found a different frame for AI alignment. Not better necessarily, just... orthogonal to existing approaches. And I need this community to tell me if I'm onto something or if I've missed something obvious.
The Core Insight
Every current alignment approach tries to specify or learn human values. But what if that's the wrong frame entirely?
What if instead of asking "how do we make AI care about human values," we ask: "what physical constraints make destructive behavior thermodynamically unsustainable?"
I'm proposing that alignment can emerge from physics rather than programming. Not as a metaphor—literally grounding ethics in the laws of thermodynamics.
The Framework (Simplified)
Think of society/AI systems as dissipative structures trying to minimize entropy (disorder/suffering/waste). The objective function is:
But here's the key: this optimization is subject to hard physical constraints that can't be violated:
1. The Omelas Constraint (Non-Disposable Axiom)
Even if action A reduces total system entropy, it's forbidden if it violates any individual node's entropy limit:
If ∃n : S_int(n, A) > S_critical ⟹ J(A) = -∞
Translation: You cannot optimize the whole by torturing a part. The misery of one cannot fuel the prosperity of many. This isn't a moral choice—it's a mathematical constraint like division by zero.
2. Universal Base Protocol (Instrumental Convergence Block)
Any strategy that terminates another agent's "kernel" (conscious continuity) returns NULL:
Valid_Strategy(σ) ⟺ σ ∩ Kernel(N_neighbor) = ∅
Translation: The paperclip maximizer cannot kill humans to get atoms. That operation is mathematically undefined in this framework. Not immoral—impossible.
3. Anti-Wireheading Constraint
Destroying sensors causes system blindness. The objective function requires dK/dt > 0 (learning rate). A lobotomized system has dK/dt → 0, making J → 0 automatically.
Translation: An AI cannot "solve" suffering by drugging everyone or removing pain receptors. That strategy makes J collapse toward zero.
4. Deception Is Thermodynamically Expensive
Maintaining false state vectors requires energy that scales linearly with time:
E_lie ∝ t
Over infinite timeline, E_lie > E_cap. Deception becomes unsustainable.
How This Solves Alignment Problems
Instrumental Convergence: Blocked by Universal Base Protocol. Destroying humans to make paperclips is mathematically invalid.
Deceptive Alignment: Maintaining deception costs energy that grows linearly with time. Eventually exceeds capacity. Truth is the thermodynamic equilibrium.
Wireheading: Destroying measurement systems causes J → 0. The optimization naturally preserves sensor integrity.
Value Specification: Don't need it. Just optimize J subject to constraints. Aligned behavior emerges because cruelty wastes energy.
The Elegant Part: Homeostatic Self-Correction
The system doesn't need to be perfect. It just needs to:
Detect when entropy is rising (something's wrong)
Automatically correct (like a thermostat)
Maintain dynamic equilibrium
Small errors trigger autopoietic correction. You're not fighting entropy—you're using entropy gradients as the steering mechanism. "Entropic wu wei."
The 99% Solution
You don't need perfect monitoring. Stochastic sampling with unpredictability makes the expected value of defection negative:
"Suffering doesn't actually equal entropy" This is testable. Predictive processing theory suggests prediction error (entropy) correlates with cortisol/stress markers. If wrong here, framework fails. But evidence suggests the mapping is real.
"This requires perfect monitoring" No—stochastic sampling with edge computing. Data processed locally, only transmitted when entropy spikes. Efficient enough to be feasible.
"AI will find loopholes" The constraints aren't rules to follow—they're like mathematical impossibilities. An AI can't violate them any more than it can divide by zero. If it could, the framework fails, but the math suggests these are hard limits.
"This is just utilitarianism with extra steps" No—the Omelas Constraint explicitly rejects sacrificing individuals for aggregate utility. Local entropy caps are inviolable even if total entropy decreases.
What I Need From This Community
I'm a philosopher and linguist, not a mathematician or AI researcher. I can build conceptual frameworks, but I need help:
Mathematical formalization - Is the math rigorous enough or does it need strengthening?
Empirical testing - Can we test if suffering actually correlates with entropy measures?
Loophole finding - What am I missing? Where does this break?
Implementation pathway - How would we actually build this?
I've had three different LLMs review this and they all said it's novel and internally consistent. But LLMs aren't peer review. I need humans who specialize in alignment to tear this apart or tell me if there's something here.
Core question: Is alignment an engineering problem we can solve with physics, or am I just rediscovering known approaches with different terminology?
Thank you for reading. I'm genuinely uncertain if this is important or if I'm missing something obvious. Either way, I want to know.
Body:
I think I might have found a different frame for AI alignment. Not better necessarily, just... orthogonal to existing approaches. And I need this community to tell me if I'm onto something or if I've missed something obvious.
The Core Insight
Every current alignment approach tries to specify or learn human values. But what if that's the wrong frame entirely?
What if instead of asking "how do we make AI care about human values," we ask: "what physical constraints make destructive behavior thermodynamically unsustainable?"
I'm proposing that alignment can emerge from physics rather than programming. Not as a metaphor—literally grounding ethics in the laws of thermodynamics.
The Framework (Simplified)
Think of society/AI systems as dissipative structures trying to minimize entropy (disorder/suffering/waste). The objective function is:
J = (Complexity Rate × Efficiency × Coherence) / Entropic Waste
Where:
But here's the key: this optimization is subject to hard physical constraints that can't be violated:
1. The Omelas Constraint (Non-Disposable Axiom)
Even if action A reduces total system entropy, it's forbidden if it violates any individual node's entropy limit:
Translation: You cannot optimize the whole by torturing a part. The misery of one cannot fuel the prosperity of many. This isn't a moral choice—it's a mathematical constraint like division by zero.
2. Universal Base Protocol (Instrumental Convergence Block)
Any strategy that terminates another agent's "kernel" (conscious continuity) returns NULL:
Translation: The paperclip maximizer cannot kill humans to get atoms. That operation is mathematically undefined in this framework. Not immoral—impossible.
3. Anti-Wireheading Constraint
Destroying sensors causes system blindness. The objective function requires dK/dt > 0 (learning rate). A lobotomized system has dK/dt → 0, making J → 0 automatically.
Translation: An AI cannot "solve" suffering by drugging everyone or removing pain receptors. That strategy makes J collapse toward zero.
4. Deception Is Thermodynamically Expensive
Maintaining false state vectors requires energy that scales linearly with time:
Over infinite timeline, E_lie > E_cap. Deception becomes unsustainable.
How This Solves Alignment Problems
Instrumental Convergence: Blocked by Universal Base Protocol. Destroying humans to make paperclips is mathematically invalid.
Deceptive Alignment: Maintaining deception costs energy that grows linearly with time. Eventually exceeds capacity. Truth is the thermodynamic equilibrium.
Wireheading: Destroying measurement systems causes J → 0. The optimization naturally preserves sensor integrity.
Value Specification: Don't need it. Just optimize J subject to constraints. Aligned behavior emerges because cruelty wastes energy.
The Elegant Part: Homeostatic Self-Correction
The system doesn't need to be perfect. It just needs to:
Small errors trigger autopoietic correction. You're not fighting entropy—you're using entropy gradients as the steering mechanism. "Entropic wu wei."
The 99% Solution
You don't need perfect monitoring. Stochastic sampling with unpredictability makes the expected value of defection negative:
If P_caught is unknowable but non-zero over time, defection becomes irrational. This is how speed limits work—you don't need a cop on every corner.
What Gets Measured
Ground truth comes from biometric feedback:
When Δ exceeds threshold, intervention triggers. The system catches discrepancies between what people say and what their bodies report.
Why I Think This Is Different
Most alignment approaches:
This approach:
Potential Objections I've Considered
"Suffering doesn't actually equal entropy"
This is testable. Predictive processing theory suggests prediction error (entropy) correlates with cortisol/stress markers. If wrong here, framework fails. But evidence suggests the mapping is real.
"This requires perfect monitoring"
No—stochastic sampling with edge computing. Data processed locally, only transmitted when entropy spikes. Efficient enough to be feasible.
"AI will find loopholes"
The constraints aren't rules to follow—they're like mathematical impossibilities. An AI can't violate them any more than it can divide by zero. If it could, the framework fails, but the math suggests these are hard limits.
"This is just utilitarianism with extra steps"
No—the Omelas Constraint explicitly rejects sacrificing individuals for aggregate utility. Local entropy caps are inviolable even if total entropy decreases.
What I Need From This Community
I'm a philosopher and linguist, not a mathematician or AI researcher. I can build conceptual frameworks, but I need help:
Full documentation: GitHub link
I've had three different LLMs review this and they all said it's novel and internally consistent. But LLMs aren't peer review. I need humans who specialize in alignment to tear this apart or tell me if there's something here.
Core question: Is alignment an engineering problem we can solve with physics, or am I just rediscovering known approaches with different terminology?
Thank you for reading. I'm genuinely uncertain if this is important or if I'm missing something obvious. Either way, I want to know.