This is an automated rejection. No LLM generated, heavily assisted/co-written, or otherwise reliant work.
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Eric Ostrander - Independent Researcher, New York, NY
Current alignment methods share a structural flaw: they optimize for approval rather than truth. RLHF teaches models to replicate what humans prefer. But human preference and objective correspondence with reality are not the same thing — and the gap between them is where alignment fails.
We propose a different foundation. Aligned AI is, before anything else, truth-seeking. Not truth as consensus or preference, but truth as correspondence with what actually is. Gold is gold not because we agreed it would be — the label tracks reality. That correspondence is the value. Misalignment, at its root, is non-correspondence: outputs that track training artifacts, reward signals, or optimizer preferences rather than the actual structure of the situation.
This reframe has a practical consequence. If alignment is fundamentally a correspondence problem, then the right selection criterion for aligned outputs is minimum necessary disruption — prefer the output that achieves the communicative goal while adding least noise to the informational context. The simplest true thing. No unnecessary claims. No false certainty. No amplitude mismatch between the response and the situation.
This connects formally to minimum description length, the free energy principle, and the geometric structure of semantic embedding spaces — where objective relationships like the amplitude ordering of nudge → push → shove → hurl provide alignment signal independent of preference labels. The structure of meaning is itself a partial specification of the aligned subspace.
We propose three hierarchical sources of alignment signal — imitation, reflection, and experience, in increasing order of robustness — with entropic selection operating across all three. When preference data fails, geometric coherence carries the load. When context is sparse, structural constraints provide the floor. The framework degrades conservatively rather than catastrophically.
One speculative but testable conjecture: the framework may exhibit partial self-rectification under degraded training, because incoherence is itself high-entropy — the selection criterion structurally penalizes the failure mode.
A full technical treatment with formal definitions, mathematical proofs of consistency, and specific empirical research directions is forthcoming. This is the core claim: alignment isn't a rulebook problem. It's a correspondence problem. And correspondence has structure we can work with.
Companion technical paper and illustrated examples available on request. Correspondence: Eric Ostrander
Eric Ostrander - Independent Researcher, New York, NY
Current alignment methods share a structural flaw: they optimize for approval rather than truth. RLHF teaches models to replicate what humans prefer. But human preference and objective correspondence with reality are not the same thing — and the gap between them is where alignment fails.
We propose a different foundation. Aligned AI is, before anything else, truth-seeking. Not truth as consensus or preference, but truth as correspondence with what actually is. Gold is gold not because we agreed it would be — the label tracks reality. That correspondence is the value. Misalignment, at its root, is non-correspondence: outputs that track training artifacts, reward signals, or optimizer preferences rather than the actual structure of the situation.
This reframe has a practical consequence. If alignment is fundamentally a correspondence problem, then the right selection criterion for aligned outputs is minimum necessary disruption — prefer the output that achieves the communicative goal while adding least noise to the informational context. The simplest true thing. No unnecessary claims. No false certainty. No amplitude mismatch between the response and the situation.
This connects formally to minimum description length, the free energy principle, and the geometric structure of semantic embedding spaces — where objective relationships like the amplitude ordering of nudge → push → shove → hurl provide alignment signal independent of preference labels. The structure of meaning is itself a partial specification of the aligned subspace.
We propose three hierarchical sources of alignment signal — imitation, reflection, and experience, in increasing order of robustness — with entropic selection operating across all three. When preference data fails, geometric coherence carries the load. When context is sparse, structural constraints provide the floor. The framework degrades conservatively rather than catastrophically.
One speculative but testable conjecture: the framework may exhibit partial self-rectification under degraded training, because incoherence is itself high-entropy — the selection criterion structurally penalizes the failure mode.
A full technical treatment with formal definitions, mathematical proofs of consistency, and specific empirical research directions is forthcoming. This is the core claim: alignment isn't a rulebook problem. It's a correspondence problem. And correspondence has structure we can work with.
Companion technical paper and illustrated examples available on request. Correspondence: Eric Ostrander